By Tom Tippett
October 14, 2004
When we release our annual Projection Disk in the spring, we give our customers a chance to get a head start on the baseball season. With projected statistics and ratings for over 1600 established big leaguers and top minor-league prospects, plus league schedules, park factors, team rosters, projected pitching rotations, bullpen assignments, lineups and depth charts, the Projection Disk gives them everything they need to play out the new season using the Diamond Mind Baseball simulation game.
It also gives us a chance to get a head start on the season. Ever since we created the first Projection Disk in 1998, we've been publishing our projected standings along with comments on the outlook for all 30 teams. Those projected standings are based on the average of a number of full-season simulations using the Projection Disk.
Of course, nobody really knows what's going to happen when the real season starts, but we're always curious to see how our projected results compare to the real thing. And we're equally interested in seeing how our projections stack up against the predictions made by other leading baseball experts and publications. This article takes a look at those preseason predictions and identifies the folks who were closest to hitting the mark in 2004. And because anyone can get lucky and pick the winners in one season, we also look at how everyone has done over a period of years.
In addition to projecting the order of finish, our simulations provide us with projected win-loss records, projected runs for and against, and the probability that each team will make the postseason by winning its division or grabbing the wild card.
Unfortunately, most of the predictions that are published in major newspapers, magazines and web sites don't include projected win-loss records. Instead, they give the projected order of finish without indicating which races are expected to be hotly contested and which will be runaways. Some don't even bother to predict the order of finish, but settle instead for the division winners and wild card teams.
As a result, we do our best to assign a meaningful score to each prediction based solely on order of finish within each division. We borrowed the scoring system from our friend Pete Palmer, co-author of Total Baseball and The Hidden Game of Baseball, who has been projecting team standings for more than 35 years.
Pete's scoring system subtracts each team's actual placement from its projected placement, squares this difference, and adds them up for all the teams. For example, if you predict a team will finish fourth and they finish second, that's a difference of two places. Square the result, and you get four points. Do this for every team and you get a total score. The lower the score, the more accurate your predictions.
We don't try to break ties. If, for example, two teams tie for first, we say that each team finished in 1.5th place for the purposes of figuring out how many places a prediction was off. Suppose a team was projected to finish third and they tied for first instead. That's a difference of 1.5 places. The square of 1.5 is 2.25, so that would be the point total for this team. That's why you'll see some fractional scores in the tables below.
That first year, we created a little database with our projected standings and those of fourteen national publications, and we were pleased to see that we ended the year with the best accuracy score among those fifteen forecasts. When we wrote up the results and posted them to our web site, however, we were very careful not to make any grand claims, saying:
"I'm not sure what to make of all this. It's just one year, and it's entirely possible that we were just lucky. Time will tell whether our approach to projecting seasons is consistently better than average."
Over time, we expanded our database to include the predictions of prominent baseball writers from major newspapers and other publications. This is easier said than done because some publications and web sites change their approach from year to year. For example, we used to track the predictions of several ESPN.com writers and editors, but they limited their picks to division winners in 2003. So the number of entries in our database can rise and fall depending on what the various publications do and whether we were able to find those predictions in our spring survey.
In the sections below, we'll show you how various prognosticators ranked in 2004 and over a period of years, with the period varying in length depending on when we added that person or publication to our database. We don't make any claims of completeness here -- there are lots of other predictions that are not in our database -- but we think you'll find that our sample is an interesting one.
For several reasons, we want to emphasize that it's important that nobody take these rankings too seriously.
First, this isn't the only scoring system one could use to rank these projections, of course. A fellow named Gerry Hamilton runs a predictions contest every year (see http://www.tidepool.com/~ggh1/index.html) and assigns a score based on how many games each team finished out of their predicted place in the standings. (We came 22nd out of 195 predictions in their 2004 contest after finishing 4th in 2003.)
Second, because of publishing deadlines, the predictions in some spring baseball magazines are made long before spring training started, others are prepared in early-to-mid March, and some are compiled just before opening day. Obviously, the longer you wait, the more information you have on player movement and injuries.
Third, many newspaper editors ask staff writers to make predictions so their readers have something to chew on for a couple of days. Some writers hate doing them but comply because their editors insist. Some do it even though their main beat is a different sport. Others may make off-the-wall picks just for grins or feel compelled to favor the hometown teams.
It's interesting to see how everyone did this year, but it's even more interesting to look back to see how different people perceived the baseball world before the season started. We'll start by showing you the prediction rankings for the current season, then we'll follow that up with a review of each division race and how those races affected these rankings.
Forecaster Score New York Times 30 Las Vegas over-under line 32.5 Tony DeMarco, MSNBC.com 40 Diamond Mind simulations 42 Bob Hohler, Boston Globe 42 Joe Sheehan, Baseball Prospectus 42 Michael Wolverton, Baseball Prospectus 42 David Lipman, ESPN.com 44 Michael Holley, Boston Globe 46 Gary Huckabay, Baseball Prospectus 46 Team payroll (per USA Today) 46 Poll of SABR members 48 Athlon 48 Eric Mack, CBS SportsLine 48 2003 final standings 48 MLB Yearbook 50 Baseball Prospectus 52 Nate Silver, Baseball Prospectus 52 Lindy's 52 Dan Shaughnessy, Boston Globe 52 ESPN.com power rankings 56 Phil Rogers, ESPN.com 56 Steve Mann 56 The Sporting News (Ken Rosenthal) 58 Rany Jazayerli, Baseball Prospectus 58 Charley McCarthey, CBS SportsLine 58 Baseball America 60 Sports Illustrated 60 Spring Training Yearbook 60 Tristan Cockroft, CBS SportsLine 60 USA Today 61.5 Street & Smith 62 Chris Kahrl, Baseball Prospectus 62 Miami Herald 64 Derek Zumsteg, Baseball Prospectus 64 USA Today Sports Weekly 66 Jonah Keri, Baseball Prospectus 66 Pete Palmer 68 Dallas Morning News 68 Seattle Times 68 CBS SportsLine 72 Gordon Edes, Boston Globe 72 Scott Miller, CBS SportsLine 72 ESPN the magazine (Peter Gammons) 74 Los Angeles Times 74 Bob Ryan, Boston Globe 76 Adam Reich, CBS SportsLine 80 Spring training results 134
The "Diamond Mind simulations" entry is the one representing the average result of simulating the season 100 times. These simulations were done about three weeks before the season started.
There are a few other entries in this list that don't represent the views of a writer or a publication. If you predicted that the 2004 standings would be the same as in 2003, your score would have been 48. If you put together a set of standings based on the Las Vegas over-under line, you'd have racked up an impressively low total of 32.5 points. If you thought the teams would finish in order from highest to lowest payroll, your score would have been 46.
And if you predicted that the regular season standings would match the 2004 spring training standings, your score would have been 134. In other words, the spring training results were almost useless as a predictor of the real season, and that's been true for at least the past four years.
Much more interesting than the overall scores, in our opinion, are the details. Which teams were consistently under- or over-estimated? Which divisions contained the biggest surprises? Did anyone predict that certain teams would have a sudden change of fortune?
Leaving out the entries that don't represent writers or publications, here are some observations about how the others saw things last spring:
AL East. Everyone had either New York or Boston winning the division, with the Yankees being picked first four more times than the Red Sox. Other than Gary Huckabay, who picked Toronto second and Boston third, everyone had this as a two-team race. A good number of people picked Baltimore third ahead of Toronto, but four people picked the Orioles to finish last, too, so there was no clear consensus on the Orioles.
AL Central. The Kansas City Royals were the downfall for many this year. The young Royals led the division for much of the 2003 season before fading down the stretch, then added some veteran players during the winter. As a result, they were a trendy pick to win the division or finish second behind Minnesota. A good part of the reason our score is among the leaders in 2004 is that we identified the Royals as one of the teams most likely to disappoint. That was based largely on our simulation results, but also based on the fact that the 2003 Royals didn't have the statistical foundation to justify their high placement. Surprisingly, seven predictions had Detroit finishing fourth, in every case because they thought the Indians would be even worse.
AL West. A year ago, our score was significantly improved because we chose to rank the Mariners ahead of the Angels when those two teams finished in a virtual tie for second in our simulations. This year, those teams were again neck and neck, with the Mariners averaging one more win but the Angels having a slightly better run margin. In a decision we'd love to have back, we gave the nod to Seattle. More than twice as many people chose Anaheim to win the division over Oakland, with three choosing the Mariners for first place. Everyone picked the Rangers to finish last, meaning that nobody in our survey got this division (or any other division) correct from top to bottom.
NL East. Before the season, the Phillies appeared to be loaded with talent, the Marlins were shedding payroll after winning the World Series, and the Braves seemed quite vulnerable. All three teams were selected by at least one person to win the division, with Philly being the choice about 80% of the time. Most predictions had a clear separation between the top three and the bottom two, but Montreal (five times) and New York (three times) snuck into third place on a few lists.
NL Central. Only two entries (Diamond Mind and Steve Mann) had the Cardinals finishing first in this division. The others seemed caught up in the hype surrounding the Cubs young pitching (Prior, Wood, Zambrano) and the Astros older pitching (Clemens, Pettitte). Just about every prediction had the Cubs and Astros duking it out for first with the Cardinals third. The picks for first place were almost evenly split between Chicago and Houston, with the Cubs having a very slight edge. There was some variation in the order of the bottom three teams, but nobody picked any of them to finish in the top half of the division.
NL West. Picking the Dodgers to finish at or near the top was a key to the better-scoring predictions this year, as was picking against the Diamondbacks. We were among those who thought Arizona would finish ahead of Los Angeles, but we were not alone. Approximately 2/3 of the predictions had Arizona beating the Dodgers, with thirteen people picking the D'backs to win the division outright. (In an example of the importance of timing, Arizona finished one game ahead of the Dodgers in our simulations, but the teams would have been reversed had we run them again after Milton Bradley was traded to LA.) It's clear that many people thought this division was wide open, as four of the five teams (everyone but the Rockies) were picked to finish first at least once.
Summing up. For the first time ever, not a single division was nailed by even a single predictor. Certain teams surprised a lot of people by overachieving (Texas, Los Angeles) or falling short (Arizona, Seattle, Kansas City, Toronto). As a result, the prediction scores were much higher this year than in 2003. A year ago, things went more in accordance with expectations.
Here are the rankings for those who were included in our sample every year. There's a new entry this year. We went back and ranked all of the teams based on their payroll as reported in USA Today in April, and we computed a standings score based on the "prediction" that teams would finish in order from highest to lowest payroll. As you can see, that doesn't seem to be a very good predictor.
Forecaster 2004 2003 2002 2001 2000 1999 1998 Total Diamond Mind 42.0 28.0 40.0 54.5 68.0 42.0 44.5 319.0 Las Vegas over-under 32.5 30.0 46.0 65.5 51.5 48.0 52.0 325.5 Sports Illustrated 60.0 30.0 48.0 56.5 40.0 56.0 54.0 344.5 Steve Mann 56.0 48.0 60.0 38.5 58.0 54.0 44.0 358.5 Sports Weekly 66.0 38.0 42.0 46.5 58.0 51.5 60.0 362.0 Athlon 48.0 36.0 38.0 67.5 42.0 72.0 72.0 375.5 Sporting News 58.0 44.0 54.0 52.5 38.0 78.0 54.0 378.5 Pete Palmer 68.0 56.0 50.0 70.5 54.0 40.0 58.0 396.5 Street & Smith 62.0 36.0 70.0 68.5 58.0 68.0 64.0 426.5 Previous season 48.0 42.0 48.0 64.5 56.0 70.0 100.0 428.5 Payroll ranking 46.0 64.0 102.0 60.0 88.0 72.0 44.0 476.0
In 1999, we added some writers from the Boston Globe.
Forecaster 2004 2003 2002 2001 2000 1999 Total Gordon Edes, Boston Globe 52.0 32.0 54.0 56.5 26.0 28.0 248.5 Las Vegas over-under line 32.5 30.0 46.0 65.5 51.5 48.0 273.5 Diamond Mind simulations 42.0 28.0 40.0 54.5 68.0 42.0 274.5 Sports Illustrated 60.0 30.0 48.0 56.5 40.0 56.0 290.5 USA Today Sports Weekly 66.0 38.0 42.0 46.5 58.0 51.5 302.0 Athlon 48.0 36.0 38.0 67.5 42.0 72.0 303.5 Baseball America 60.0 28.0 48.0 54.5 54.0 70.0 314.5 Steve Mann 56.0 48.0 60.0 38.5 58.0 54.0 314.5 Sporting News 58.0 44.0 54.0 52.5 38.0 78.0 324.5 Previous season standings 48.0 42.0 48.0 64.5 56.0 70.0 328.5 Dan Shaughnessy, Globe 52.0 56.0 70.0 44.5 54.0 58.0 334.5 Pete Palmer 68.0 56.0 50.0 70.5 54.0 40.0 338.5 Bob Ryan, Boston Globe 76.0 40.0 58.0 84.5 58.0 40.0 356.5 Street & Smith 62.0 36.0 70.0 68.5 58.0 68.0 362.5 Payroll ranking 46.0 64.0 102.0 60.0 88.0 72.0 432.0
The Diamond Mind simulations missed the mark by quite a bit in 2000. We added a new concept to our projection system that year, but we were unhappy with the results, and we took that out of the model before generating our projections in 2001. The results have been much better since. As you can see, the Las Vegas over-under line has been getting much better in recent years.
Forecaster 2004 2003 2002 2001 2000 Total Las Vegas over-under line 32.5 30.0 46.0 65.5 51.5 225.5 Athlon 48.0 36.0 38.0 67.5 42.0 231.5 Diamond Mind simulations 42.0 28.0 40.0 54.5 68.0 232.5 Sports Illustrated 60.0 30.0 48.0 56.5 40.0 234.5 Gordon Edes, Boston Globe 72.0 32.0 54.0 56.5 26.0 240.5 Baseball America 60.0 28.0 48.0 54.5 54.0 244.5 Sporting News 58.0 44.0 54.0 52.5 38.0 246.5 Previous season standings 48.0 42.0 48.0 64.5 56.0 248.5 USA Today Sports Weekly 66.0 38.0 42.0 46.5 58.0 250.5 Steve Mann 56.0 48.0 60.0 38.5 58.0 260.5 Dan Shaughnessy, Globe 52.0 56.0 70.0 44.5 54.0 276.5 Street & Smith 62.0 36.0 70.0 68.5 58.0 294.5 Pete Palmer 68.0 56.0 50.0 70.5 54.0 298.5 Bob Ryan, Boston Globe 76.0 40.0 58.0 84.5 58.0 316.5 Payroll ranking 46.0 64.0 102.0 60.0 88.0 360.0
Lindy's was a strong addition to our survey in 2001. We also added the San Francisco Chronicle that year, but they've been dropped from this list because we couldn't find their 2004 predictions. That paper ranked second from 2001 to 2003.
Forecaster 2004 2003 2002 2001 Total Diamond Mind simulations 42.0 28.0 40.0 54.5 164.5 Lindy's 52.0 40.0 42.0 36.5 170.5 Las Vegas over-under line 32.5 30.0 46.0 65.5 174.0 Tony DeMarco, MSNBC.com 40.0 34.0 34.0 67.5 175.5 Athlon 48.0 36.0 38.0 67.5 189.5 Baseball America 60.0 28.0 48.0 54.5 190.5 USA Today Sports Weekly 66.0 38.0 42.0 46.5 192.5 Sports Illustrated 60.0 30.0 48.0 56.5 194.5 Steve Mann 56.0 48.0 60.0 38.5 202.5 Previous season standings 48.0 42.0 48.0 64.5 202.5 Sporting News 58.0 44.0 54.0 52.5 208.5 Los Angeles Times 74.0 18.0 44.0 73.5 209.5 Gordon Edes, Boston Globe 72.0 32.0 54.0 56.5 214.5 Dan Shaughnessy, Globe 52.0 56.0 70.0 44.5 222.5 Street & Smith 62.0 36.0 70.0 68.5 236.5 Pete Palmer 68.0 56.0 50.0 70.5 244.5 Bob Ryan, Boston Globe 76.0 40.0 58.0 84.5 258.5 Payroll ranking 46.0 64.0 102.0 60.0 272.0 Spring training results 134.0 70.0 86.0 113.5 403.5
Here's how things looked from 2002 to 2004. The LA Times was unable to follow up the excellent 2003 predictions that put them in top spot in last year's two-season rankings.
Forecaster 2004 2003 2002 Total Tony DeMarco, MSNBC.com 40.0 34.0 34.0 108.0 Las Vegas over-under line 32.5 30.0 46.0 108.5 Diamond Mind simulations 42.0 28.0 40.0 110.0 Bob Hohler, Boston Globe 42.0 32.0 38.0 112.0 Athlon 48.0 36.0 38.0 122.0 Lindy's 52.0 40.0 42.0 134.0 Los Angeles Times 74.0 18.0 44.0 136.0 Baseball America 60.0 28.0 48.0 136.0 Sports Illustrated 60.0 30.0 48.0 138.0 Previous season standings 48.0 42.0 48.0 138.0 USA Today Sports Weekly 66.0 38.0 42.0 146.0 USA Today 61.5 32.0 58.0 151.5 Sporting News 58.0 44.0 54.0 156.0 Gordon Edes, Boston Globe 72.0 32.0 54.0 158.0 Steve Mann 56.0 48.0 60.0 164.0 Street & Smith 62.0 36.0 70.0 168.0 Bob Ryan, Boston Globe 76.0 40.0 58.0 174.0 Pete Palmer 68.0 56.0 50.0 174.0 Dan Shaughnessy, Globe 52.0 56.0 70.0 178.0 Payroll ranking 46.0 64.0 102.0 212.0 Spring training results 134.0 70.0 86.0 290.0
Finally, here's how things have looked over the past two years.
Forecaster 2004 2003 Total Las Vegas over-under line 32.5 30.0 62.5 Diamond Mind simulations 42.0 28.0 70.0 Tony DeMarco, MSNBC.com 40.0 34.0 74.0 Bob Hohler, Boston Globe 42.0 32.0 74.0 Athlon 48.0 36.0 84.0 Baseball America 60.0 28.0 88.0 Sports Illustrated 60.0 30.0 90.0 Previous season standings 48.0 42.0 90.0 MLB Yearbook 50.0 40.0 90.0 Lindy's 52.0 40.0 92.0 Los Angeles Times 74.0 18.0 92.0
USA Today 61.5 32.0 93.5 Street & Smith 62.0 36.0 98.0 Sporting News 58.0 44.0 102.0 USA Today Sports Weekly 66.0 38.0 104.0 Gordon Edes, Boston Globe 72.0 32.0 104.0 Steve Mann 56.0 48.0 104.0 Dan Shaughnessy, Globe 52.0 56.0 108.0 Spring Training Yearbook 60.0 48.0 108.0 ESPN the magazine 74.0 36.0 110.0 Payroll ranking 46.0 64.0 110.0 Pete Palmer 68.0 56.0 114.0 Bob Ryan, Boston Globe 76.0 40.0 116.0 Spring training results 134.0 70.0 204.0
Overall, we've been pretty happy with our results, and if there's one thing that stands out, it's our ability to identify over-rated teams.
In 2004, we saw the Royals as a 2003 overachiever that was unlikely to repeat, we projected the Blue Jays to finish below .500, and we didn't buy all of the hype surrounding the Cubs and Astros.
A year earlier, our simulations correctly indicated that the Mets were likely to finish at the bottom of their division again, the Angels were very unlikely to repeat their 2002 success, and the Dodgers wouldn't score enough runs to make a serious run at the NL West title.
Even so, we're always surprised by something that happens each year. We didn't anticipate the emergence of the Rangers and Dodgers in 2004 or the surprising finishes of the Marlins and Royals the year before. As a result, we have a bunch of test cases to study as we consider possible improvements to our projection system.
More than anything, this process -- projecting the season in March, watching the real thing for six months, and taking a look back after the season -- is highly educational for us. So we'll be back with our projected 2005 team standings in March.
]]>By Tom Tippett
December 10, 2001
If you haven't already done so, please read the introduction to the 2002 Gold Glove Review article for a summary of the techniques we use for evaluating defensive performance.
Pitchers. There's a very strong tendency for Gold Glove voters to fixate on one guy and keep giving him the award year after year after year, as long as he doesn't get hurt or do anything to make it clear that something has changed. This tendency is especially strong for pitchers, perhaps because the voters don't get to see them as often.
At other positions, we can judge performance over a span of 1,000 to 1,400 defensive innings, but even the most durable starting pitchers are in the field only for 200-250 innings. And relievers get only a fraction of the innings of a starting pitcher.
With 14 or 16 teams in the league, a voter might get to see a certain shortstop play 80 innings in the field. That's not much in the context of a whole season, but it sure beats the 10-20 innings they might see of a starting pitcher or the 4-5 innings a reliever might pitch in those games.
So it's hard for anyone to evaluate pitcher defense just by watching, because nobody is in position to watch enough pitchers in enough situations to get a complete picture. And it's hard to evaluate pitchers just by looking at their putouts and assists because a pitcher's tendency to induce ground balls can have a major impact on those numbers. Even if you're a brilliant fielder, you're not going to look good next to Greg Maddux if you're a fly-ball pitcher and they're using traditional fielding stats to evaluate you.
This year, Mike Mussina was chosen for the fifth time, and he's a pretty good pick. He had a good year, handling 43 chances successfully while participating in 5 double plays, making only one error, and doing a very good job holding opposing runners. But there are other deserving candidates.
(By the way, I'll leave it up to you to decide whether holding runners is a pitching skill or a defensive skill. But I'll mention it for those of you who think it's relevant to a Gold Glove debate.)
Freddy Garcia also participated in five double plays and made only one error while handling 68 chances successfully, more than half again as many as Mussina. On the other hand, Garcia creates more chances for himself because he's a ground ball pitcher, and he doesn't hold runners well.
Steve Sparks had 62 successful chances, only one error, and held runners well despite throwing a pitch, the knuckleball, that is easy to run on. He was involved in one double play.
Brad Radke had 57 successful chances, four double plays, and only one error, but wasn't quite as good as Sparks and Mussina at holding runners.
Andy Pettitte was error-free in 49 successful chances with one double play and has a terrific pickoff move, though he is less successful holding runners close when he goes home with the pitch.
Jeff Weaver also handled 49 chances without an error. He was in on four double plays and was in the middle of the pack in holding runners. All things considered, my vote would have gone to Garcia this year.
In the other league, Greg Maddux won his 12th straight, and there's no question that he's a very good fielder. But it must also be said that he has a head start on his competition because he's an extreme ground-ball pitcher who creates for himself a ton of opportunities to make plays. This year, he led the majors by handling 72 chances successfully, making only one error in the process.
But there are two arguments against Maddux's iron grip on this award. First, quite a few others have ranked above Maddux each year in plays made per batted ball in his zone. And Maddux has made 14 errors in the past five years; that's a lot for a pitcher, and only three other pitchers have made more in that span.
Consider Kirk Rueter. I'll bet if the voters had picked him a few years ago, they'd keep picking him every year just like they do with Maddux, because if Rueter had once been deemed the best, he's definitely doing enough to reinforce the view that he still is.
This year, Rueter handled 61 chances without an error and took part in eleven (!) double plays. Among players with at least 50 balls hit into his zone, he ranked #1 in converting those chances into outs. And he was almost impossible to run on.
Last year, Rueter handled 52 chances without an error and took part in four double plays. He converted an extremely high number of batted balls into outs and was almost impossible to run on. In 1999, Rueter handled 45 successful chances but made one error.
Over the past five years, Maddux has made 14 errors in 424 chances for a fielding percentage of .967. In the same span, Rueter has made 3 errors in 265 chances for a fielding percentage of .989. Rueter has been involved in seven more double plays (26 to 19) despite pitching about 240 fewer innings. Rueter has converted a noticeably higher percentage of batted balls into outs. The only area where Maddux has the edge is raw totals, and that's only because he generates so many more come- backers than the average pitcher.
Getting back to the 2001 season, the pitchers who bested Maddux in converting opportunities into outs are Adam Eaton, Rueter, Chris Reitsma, Livan Hernandez, Russ Ortiz, Tom Glavine, Javier Vazquez, and Mike Hampton, in that order.
Eaton only pitched for half the season and made two errors, so I don't consider him to be in the same league as the others, though he's someone to watch for the future. Rueter, Reitsma, Hernandez, Glavine, Vazquez, and Hampton each handled more than fifty chances without making an error.
Maddux was a good choice. Any of these guys I just mentioned would have been a slightly better choice. Rueter was the best of the bunch and deserved the Gold Glove this year. Just as he did last year.
Catchers. Ivan Rodriguez is the owner of one of the best throwing arms in history, and has been a lock for this award for many years. He had another great throwing year, and even though he missed a third of the season due to injury, and he's the hands-down choice again this year. For some reason, the best arms have found their way into the other league in the past few years, and there's nobody left in the AL to challenge him.
A year ago, I argued that Brad Ausmus should have been the choice in the AL, partly because he had a great year defensively and partly because Rodriguez missed half the season. Ausmus is now in the NL and had another good year throwing, though others bested him in that department, and backed it up by allowing only one passed ball (best in the majors) and making only three errors (tied for second best in the majors).
There were other candidates, of course. Jason LaRue, Mike Matheny, and Henry Blanco threw out a higher percentage of enemy base stealers. But LaRue allowed 15 passed balls, second most in baseball, despite starting only 95 games behind the plate. Blanco started only 94 games himself, and didn't quite match up to Ausmus at any rate.
In my eyes, it's almost impossible to choose between Ausmus and Matheny. Playing time was similar. Ausmus made one fewer error and was charged with five fewer passed balls. On the other hand, Matheny had a better year throwing, though he got more help from his pitchers than Ausmus did. All in all, I think Ausmus was a worthy victor.
First basemen. Based on our analysis, there are four men who could reasonably be thought of as viable candidates at this position, two in each league: Doug Mientkiewicz and Tino Martinez in the AL, Kevin Young and Todd Helton in the NL.
The voters got it right when they chose Mientkiewicz over Martinez. Doug had a better fielding percentage, turned a higher percentage of batted balls into outs, and led the majors in highlight-reel plays. It's actually an easy choice, but I wanted to mentioned Martinez because he's a very good fielder who had another very good year, and he deserves some recognition.
It's not quite so clear in the NL. The voters picked Helton, who I thought should have won the award over J. T. Snow in 2000, but Young had a terrific year, too. Both the Diamond Mind and STATS methods for assessing range give Young a slight edge over Helton. And after making a boatload of errors in 1999 and 2000, Young got his act together and finished around the league average in fielding percentage. Helton led the league in this category.
Over the past four years, Helton has shown more range than any other first baseman in baseball. Young is second. You rarely hear good things about Young's range because he made far too many errors in two of those four seasons. But the man can cover ground at first base.
Helton and Young were almost on par with each other this year, but I'd agree with the voters and choose Helton. He's been the best in the league since 1998 and this year sustained his high level of play over 157 starts (compared to only 125 for Young).
Second basemen. Here's some of what I wrote a year ago:
"Here we go again. Roberto Alomar won his ninth Gold Glove, and there isn't a baseball writer or television commentator who doesn't gush incessantly about Alomar's brilliance in the field. And I've seen him make some very spectacular plays myself. Problem is, year after year, our analysis (and other measures such as range factors and the STATS zone rating) shows that he doesn't make many more plays than the average second baseman.
Alomar was one of three Cleveland infielders to be rewarded with Gold Gloves this season. But that infield was below the league average in turning ground balls into outs. And according to the STATS Major League Handbook, they were fourth worst in the league in converting double plays when grounders were hit in double-play situations.
And even though they used a lot of different pitchers this year, I don't think you can argue that this defense was made to look worse by a lousy pitching staff. They did, after all, get almost 600 innings from three good starting pitchers (Burba, Colon, Finley) and a bunch more from a group of veteran relievers who have fared quite well playing in front of other defenses in the recent past.
The bottom line is that somebody isn't making nearly as many plays as people think ..."
I'm repeating so much of last year's comment because it's still relevant. This season, Cleveland's infield was 13th in the league in the percentage of ground balls turned into outs. And they were only a hair above the league average in double-play percentage.
You could argue that the infield looks bad because the corner guys -- Jim Thome at first, Travis Fryman and Russ Branyan at third -- don't cover much ground, and you'd be correct. Problem is, there's absolutely no evidence that their middle infielders are doing more than their share, either.
The best case for Alomar's Gold Glove is that he won the fielding percentage title by making only five errors all season. His nearest rivals, Ray Durham and Bret Boone, made ten errors each. But Alomar's range factor was .12 below the league average despite playing behind a ground-ball staff. His STATS zone rating was thirty-five points below the norm for his position. According to our method, Alomar made 20 fewer plays than the average 2B, and he was consistently below average on all types of plays -- line drives, ground balls and popups. And he was 33 years old this year, an age when many middle infielders struggle to keep up with their younger rivals.
Those numbers are indicative of a player who deserves our Fair rating. But we gave him an Average rating anyway. Why? Because he has a great reputation and because it's possible that his pitching staff did indeed make him look worse that he really is.
This is the fifth time in the past nine years that we've given Alomar a rating that's better than our analysis shows is justified. Not once in those nine years has his play-making score been far enough above the league average to merit a Very Good rating.
But every year we say to ourselves that there must be some aspect of his ability that doesn't show up in fielding studies. But don't you think that if Alomar was truly the best at his position in the history of baseball, he'd score well at least once in nine years? Is it really possible that external factors or quirks in the data would make him look worse every single year?
I know that some people will look at this rating and conclude that (a) we're vastly underestimating his ability, (b) we have something against Alomar, and/or (c) we know nothing about baseball. Looking at all of the evidence, however, I have to say that, if anything, we've been generous in how we've rated him over the years.
I'll end this commentary with a quote from The New Bill James Historical Baseball Abstract:
"[Alomar is] an overrated fielder, in my opinion; a good fielder, even a very good one, but no better than some guys who don't win Gold Gloves, like Fernando Vina."
That was written before the 2001 data was available, and I agree with Bill's assessment of Alomar's career. We're now in the late stages of that career, however, and we're seeing evidence of a decline in Alomar's play-making ability.
Other worthy candidates for the AL Gold Glove were Adam Kennedy, Ray Durham, Bret Boone, and Jerry Hairston. Kennedy was the best of this group, but started only 123 games. Nevertheless, I'd go with Kennedy.
The other league's Gold Glove went to Fernando Vina. If Pokey Reese had played the entire year at second, instead of splitting his time between second and short, he would have gotten my vote. But he didn't, and that left things open for Vina, who I nominated as my choice a year ago.
Vina had another good year, with above-average range and a low error rate, and the Cardinals were second in the NL in double play percentage. Those are solid credentials. And he played a lot more than some of the other guys (Ron Belliard, Damian Jackson, Mark Grudzielanek) who could be considered viable candidates.
Third basemen. The voters got it right at this position. Scott Rolen was so amazing that he managed to stand out in a league featuring several other very good players who had very good years. His closest rivals were Robin Ventura and Jeff Cirillo. But Rolen was so good that if there was an award for defense -- an MVP or Cy Young for defense, single award that crosses all positions -- Rolen would be my choice for NL Defensive Player of the Year.
The AL produced three strong candidates, Eric Chavez (the winner), Corey Koskie, and David Bell. Of the three, Chavez was best in range and sure-handedness, and he played a lot more than Bell. So I agree with this selection, too.
Shortstops. As I mentioned above, the voters tend to settle on one guy and give him the award year after year as long as he doesn't blow it. By posting the second-best fielding percentage in the majors (.989, trailing only Rey Sanchez's .991), and by continuing to ply his trade with grace and style, Omar Vizquel did enough this year to keep the voters' trust, and he was rewarded with his ninth straight Gold Glove.
I'm not going to spend a lot more time writing about the Cleveland defense because I did that in the second base comment above. Suffice it to say that Vizquel's range wasn't all that good this year. If Rey Sanchez hadn't been traded out of the league, I'd nominate him, as he bested Vizquel in both range and steadiness. But Sanchez WAS traded out of the league, and in his stead, my vote goes to Toronto's Alex Gonzalez.
Interestingly, I don't recall hearing any gripes about Orlando Cabrera getting the nod in the NL. I figured that with Rey Ordonez healthy and playing a full season, some in New York would have pushed for him to get it back. But Ordonez' range was nothing special according to the measures we use, and it may be that the lingering effects of his arm and shoulder injuries affected his ability to make certain plays for at least part of the season.
On the other hand, Cabrera showed above-average range and was among the steadiest fielders in either league. Rich Aurilia also looked quite good, but in my opinion, Cabrera was a deserving winner.
Outfielders. There are a lot of good outfield candidates this year, and with one major exception, all of the winners were drawn from that pool. In other words, five of the six choices were at least in the right ballpark.
According to our analysis, five center fielders stood out this year, and all of them are in the AL. They are, from top to bottom, Chris Singleton, Kenny Lofton, Mike Cameron, Darin Erstad, and Torii Hunter. Bobby Higginson and Jacque Jones were the two left fielders who separated themselves from the pack. In right, the top performers were in the NL, with Jermaine Dye and Ichiro Suzuki being the best of the AL contenders.
The voters and I agree on Mike Cameron, so I'll focus on the voters' selection of Torii Hunter and Ichiro.
Given that center field is the most demanding outfield position and that we have a large number of deserving candidates there, I see no reason to choose a corner outfielder. Furthermore, according to our analysis, Ichiro had above-average range and an above-average arm, but he wasn't as far above average as the media would have you believe.
Ichiro's range factor was .26 above the norm, but he played behind a pitching staff that produced almost 200 more fly balls than the average AL team (according to the STATS Player Profiles book). His STATS zone rating was seven points below the major-league average for right fielders.
Nevertheless, based on his reputation and the fact that our fielding analysis shows that Ichiro would almost certainly have made more plays if he wasn't playing next to Cameron, we believe he's worthy of a Very Good rating. But we don't see evidence of Gold Glove range here.
In addition, he had only 8 assists, a below-average number for a RF who played as much as he did. And it's not as if nobody was willing to test him. Runners tried to advance on him a little less often than against the average RF, but not that much less. It does appear as if runners got a little more wary of his arm as the season progressed, but not a lot more wary. So we've rated him Very Good in throwing as well.
The media seems to be saying that Ichiro is unquestionably excellent in all phases of the game. According to our methods, he's excellent at a lot of things (hitting for average, hitting in the clutch, sacrifice bunting, running the bases, stealing bases, avoiding errors, staying healthy), very good at some things (getting to balls in right and keeping runners from taking extra bases), and below average in some ways (drawing walks, hitting for power). That's quite a package, and I'd definitely want this guy on my team. But I just don't see the evidence that he's among the top defensive outfielders in the game.
So, if Ichiro doesn't get my vote, then who does deserve the other two outfield Gold Gloves for the AL? Singleton topped the charts in plays-made-per-opportunity, but he only started 102 games. Lofton only started 123 games. Singleton and Hunter have subpar throwing arms. (Hunter tied for the league lead in assists by a CF with 14, but several of those came on plays where the lead runner scored, and he allowed lots of runners to take extra bases.) Hunter plays in a tough park -- it's easy to lose balls in the Metrodome roof -- so he's better than his numbers suggest, and his numbers are very good to begin with. Erstad made only one error all season, leading all major-league CFs in fielding percentage.
It's a very close call, but there are some big differences in playing time to consider. Performance rates are very important, but when it comes to seasonal awards, the volume of performance is more important. So when someone performs at a high level for 145 games, that trumps someone else who performed at a slightly higher level for 120 games. On that basis, my other two votes would go to Erstad and Hunter.
Over in the NL, the top candidates (in my mind) were Geoff Jenkins in left, Andruw Jones in center, plus Larry Walker, Vladimir Guerrero, and Brian Jordan in right. J. D. Drew would have been on this list were it not for the injury that cost him about 50 games. The voters chose Walker, Jones, and Jim Edmonds.
I agree with the selections of Walker and Jones, but in my opinion, either Jenkins or Guerrero would have been a much better choice than Edmonds. Jenkins is a terrific left fielder, but I have to give it to Guerrero because (a) Jenkins started only 104 games, (b) Guerrero showed great range too, and (c) Guerrero has a cannon for an arm. Guerrero does make too many errors, but his range and arm more than compensate for them.
Jim Edmonds has made some of the most amazing plays I have ever seen, but he simply doesn't cover as much ground as some of the younger players at this position. This year, he was below average in range factor and the STATS zone rating, and according to our method, made 16 fewer plays than the average CF given the opportunities presented to him. He battled groin, toe and knee problems, and he's starting to get up in years. I just don't see any reason to believe that he's a more valuable outfielder than the other guys I mentioned.
Recap. Here's how my selections would agree or disagree with those of the voters:
Pos Voters Diamond Mind P Mussina, Maddux Garcia, Rueter C Rodriguez, Ausmus same 1B Mientkiewicz, Helton same 2B Alomar, Vina Kennedy, Vina 3B Chavez, Rolen same SS Vizquel, Cabrera Gonzalez, Cabrera OF Cameron, Walker same OF Hunter, Jones same OF Ichiro, Edmonds Erstad, Guerrero
We agree on twelve of the eighteen selections. I haven't been keeping track, but I'm guessing this represents the highest rate of agreement
since we began doing this.
Now that we've offered our two-cents worth on the Gold Glove winners, there are some other players worth mentioning:
Bobby Abreu, RF -- According to our system, Abreu's play-making scores have been very erratic lately -- quite good through 1998, subpar in 1999, very good in 2000, and average this year. Looked at in the context of the past three seasons, it now seems as if the Excellent rating we assigned for his performance last year was generous, even though he was clearly in the top tier statistically that season. I'm at a loss to explain these ups and downs.
Craig Biggio, 2B -- This former Gold Glover missed the last two months of the 2000 season with a knee injury that required surgery. In January, his general manager warned that Biggio's range and baserunning ability would most likely be limited, especially early in the year. Those comments proved to be accurate, as Biggio's range was far below its previous level and he stole only seven bases, down from 50 only three years ago. His baserunning instincts are still good, so he was a little above average in that regard, but nowhere near the Excellent level he sustained before he hurt his knee.
Tony Clark, 1B -- A great athlete who has earned our Very Good rating for defense the past two years, Clark has been battling back problems that have kept him out of the lineup and hurt his power and defense. We downgraded his range rating to Fair as a result, but if he regains his health, you can expect it to rebound next year.
Ken Griffey, CF -- Spent much of the season trying to play despite a torn hamstring and its after-effects, and it clearly showed. In a little more than half a season of playing time, Griffey made ten fewer plays than the average CF, thereby earning a Fair rating. Expect that to rise next year if he's back at 100%.
Derek Jeter, SS -- I know we're going to take some heat from New York fans on this one, but I assure you that there is no bias in our decision to assign Jeter a Fair range rating this year.
According to our analysis, Jeter made 32 fewer plays than the average shortstop given the opportunties presented to him. He was below average going to his right, below average going to his left, and below average on balls hit more or less at his position. His STATS zone rating was fifty points below average. His range factor was lowest in the majors among those who played at least 100 games at the position. At one time, Scott Brosius's superior range affected Jeter's numbers, but Brosius has declined from Excellent to Average in recent years and is no longer a factor in evaluating Jeter.
The New York infield ranked 10th in the league in the percentage of ground balls that were turned into outs. And it was 13th in double play percentage. Alfonso Soriano probably deserves most of the blame for the low DP rate, but if Jeter was an outstanding fielder, he would have compensated for Soriano's limitations to some extent, and the team would have been closer to the league average.
In his defense, he played behind a staff that produced 5% fewer ground balls than the average team, so his range factor was artificially depressed. Take that into account, and Jeter's range factor would have been only the second- or third-worst in the majors. And, of course, in the playoffs, he made a couple of very heady and gutsy plays that had everyone talking about his courage, his will to win, and his intelligence.
But a couple of attention-getting plays aren't enough, in my opinion, to offset the mountain of evidence indicating that Jeter simply didn't get to as many balls as most of the other shortstops in the game.
Ryan Klesko, 1B -- Earlier in his career, before he was traded to San Diego, Klesko didn't show much range at first base in the limited amount of time he played that position for Atlanta. In 2000, he showed average range in his first full season as a 1B. We gave him an average rating for that performance, even though we weren't certain that he had improved that much. But there was a major drop this year, and his Pr rating reflects that. Klesko has surprised a lot of people by stealing 23 bases in each of the past two seasons, but his career record is quite poor in both left field and at first base, so it seems as if his 2000 season was the anomaly.
Carlos Lee, LF -- Different fielding metrics suggest that Lee's range in left was anywhere from a little above average to a little below average. Yet his defense was sharply criticized in Sports Illustrated's pre-season baseball issue and again late in the season in a Baseball Weekly note. He was replaced defensively 39 times, and that normally happens only to players who are major liabilities in the field. In this case, however, the guys replacing him were superior defenders like Chris Singleton, so it doesn't necessarily mean that Lee was terrible, only that the other guys were better. We asked several people who follow the Sox, and their opinions ranged from "he's under-rated" to "he looks awkward but gets the job done" to "he's as bad as they say." We've chosen to assign him an Average rating this year. That may be a little generous, and I wouldn't be surprised if he slips back to a Fair rating next year.
Raul Mondesi, RF -- Has a very good reputation for defense, but that's mostly based on his great arm. In terms of range, our analysis shows that he's been slightly above average throughout his career. In the spring, it was reported that Mondesi came to camp carrying some extra weight, and his defensive numbers took a big dive. Coincidence? Maybe, but we felt a Fair rating was an accurate reflection of his 2001 performance. He could easily rebound next year.
Todd Zeile, 1B -- A year ago, we wrote that his Excellent range came as a complete surprise even though third basemen often move across the diamond and look very good relative to the men who play first. But we were skeptical. He's never had a reputation as a good fielder, and we wondered whether he'd be able to keep it up. He didn't, so it may be that last year was a fluke or a case where the various fielding measures over-stated his value for some reason. We rated him Average this year.
]]>Tom Tippett
July 21, 2003
In January, 2001, Voros McCracken published an article that shook the baseball analysis community.
In an attempt to better understand how to separate the contributions of pitching and defense, McCracken divided the traditional pitching stats into two groups -- those that are under the direct control of the pitcher (hit batsmen, walks, strikeouts, homers) and those that aren't (hits on balls in play). He called the first group defense-independent pitching stats, or DIPS for short.
I'll get into the details shortly, but before I do, the reason McCracken's work caused such a stir is that he reached a conclusion that seems very counter-intuitive and, if true, extremely important. In his own words, he stated his major finding in these two ways, once at the beginning of the article and once at the end:
"hits allowed are not particularly meaningful in the evaluation of pitchers"
"major-league pitchers don't appear to have the ability to prevent hits on balls in play"
McCracken wasn't able to give a reason why this would be true, but stated rather emphatically that it is true.
Ever since I read that article, I've been wondering how this could possibly be. It seems so obvious that certain pitchers must be able to get more than their share of easy outs. Doesn't Greg Maddux produce more than his share of routine ground balls? Doesn't Mariano Rivera's cutter eat up opposing hitters even when they don't strike out? Doesn't a flame-thrower like Roger Clemens induce a lot of weak swings from hitters who are down in the count? Wouldn't a knuckleball lead to more lazy popups from hitters who are just guessing at where that pitch will dance next?
McCracken's analysis used a stat that I'll call in-play average (or IPAvg), which he defined as (H - HR) / (BF - HR - HBP - BB - K). That's just non-homer hits divided by balls in play, and because all but a handful of homers leave the yard, it's a good reflection of how well pitchers and defenses are able to turn batted balls (that stay in the field of play) into outs.
He found that:
My reaction was to think that McCracken was on to something but may have gone too far, so I began to think about how to dig a little deeper.
McCracken appears to have done most of his work using stats from two seasons. I wasn't sure whether those two seasons were representative or not, so I decided to apply his method to all pitcher-seasons since 1913. Why 1913? Because that's the first year my historical database has all of the stats needed to compute IPAvg and the DIPS for every pitcher. And I figured that 90 years would be more than enough to prove the point one way or the other.
After compiling this information and studying it for a while, I discovered a pair of columns by Rob Neyer of ESPN.com. In the first column, Rob described the McCracken article. In the second one, which appeared a couple of days later, Rob included email messages from Craig Wright and Bill James with their take on McCracken's assertion.
Wright described his own work in this area:
"Like McCracken, I've studied hits allowed per ball in play (though with the small difference that I subtract sacrifice hits) ... I agree that this type of hit rate is not as heavily influenced by the pitcher as is commonly believed, but at the same time I am distinctly uncomfortable with McCracken's conclusion."
James wrote that he hadn't studied this issue, but that he shared Wright's reservations and suggested that someone do a large-scale study to find out whether the idea would hold up. It appears that the work I had just finished doing was exactly what Bill was proposing.
In addition, Bill wrote about McCracken's work in the New Bill James Historical Baseball Abstract. Based on a review of an unspecified number of pitching careers and about 400 pitcher-seasons, he concluded that pitchers do have an influence on these outcomes but confirmed McCracken's finding that there's still a lot of random variation in single-season performances.
Finally, in recent months, I've seen more and more references to McCracken's assertion in various baseball articles and posts to baseball research forums. There's enough momentum building behind this idea that a few of our customers have asked how we might change the design of our Diamond Mind Baseball game to reflect this new knowledge about how baseball works.
Before making any changes to our game or our method for projecting player performance, I figured it was worth spending some time looking at this question.
NOTE: In an article published on Baseball Primer last year, McCracken softened his original conclusion a little, saying that there are small differences among pitchers in their ability to prevent hits on balls in play, and those differences are "statistically significant if generally not very relevant." Except for the regulars on Baseball Primer, I don't think many people in the baseball research community are aware of this update to McCracken's thinking.
For every pitcher who appeared in the big leagues since 1913, I computed his HBP rate, walk rate, strikeout rate, homerun rate, and IPAvg for each of his seasons. The first four numbers are computed quite simply -- take the relevant stat and divide by batters faced. The IPAvg figures were computed according to McCracken's formula, which I wrote out a few paragraphs back.
To establish a baseline against which to evaluate those figures, I also computed those stats for each league-season and each team-season since 1913.
This enables us to evaluate every pitcher relative to the norms for his league. Last year, for example, Roger Clemens faced 768 batters and fanned 192 of them. That's a strikeout rate of .250 in a league where the average was only .163. His advantage over the league can be stated in two ways: (a) his rate was .077 higher than the league, and (b) he had 67 more strikeouts than the league-average pitcher would have had if he faced the same number of batters as Clemens. The same method was used to determine how many hit batsmen, walks, and homeruns each pitcher yielded above or below the league average.
For balls in play, I compared the in-play batting average for each pitcher and subtracted from that the corresponding in-play batting average for the league. As was the case with strikeouts, the result can be expressed either as a number of batting average points above/below the league or a number of hits above/below the league.
But hits on balls in play are subject to some outside influences that make comparisons with the league average a little suspect. Some parks (like Coors Field) tend to inflate batting averages. Some defenses are much better than others. If Jamie Moyer allows 15 fewer hits than normal, how can we decide whether to give Moyer the credit or chalk it up to Safeco Field and the talents of Mike Cameron and Ichiro?
To account for the effects of park and defense, I also computed the in-play average for each team-season in the period from 1913 to 2002. If McCracken is correct when he says that pitchers have virtually no influence over these outcomes, every pitcher on a given team should have roughly the same IPAvg. After all, those pitchers share a common park and a common defense.
If we then (a) compute the IPAvg for each team, (b) compare the IPAvg for each pitcher to that of his team, and (c) study those differences, we should find that the differences in IPAvg between a pitcher and his teammates are random. In other words, those differences should be centered around zero, equally likely to be above zero as below zero, and have no predictive value from one year to the next.
If we find that these differences are not random, there must be another factor, apart from defense and park effects, that accounts for them. And it follows that the missing factor must be an attribute of the pitcher. Because if the pitcher had nothing to do with it, there'd be no reason for that external factor to be evident only for this pitcher.
At this stage of the process, we now know how much a pitcher exceeded or fell short of his league in five categories -- HBP, BB, K, HR and hits on balls in play -- for every season of his career. And we also know how much a pitcher exceeded or fell short of his teammates on in-play hits for every season of his career. The last step is to sum these values to obtain career totals (from 1913 forward) for every pitcher.
McCracken asserted that pitchers have a lot of control over the defense-independent pitching stats, so I would expect to see substantial differences among pitchers in their career HBP, walk, strikeout, and homerun rates, even after normalizing all of these figures against the league averages for each season.
After crunching the numbers for a total 29,973 seasons by 6,004 pitchers, we did indeed find very large differences among pitchers in some of the defense-independent statistics, especially walks and strikeouts. That's not likely to surprise any of you. It didn't surprise me, and it's entirely consistent with McCracken's findings.
More importantly, McCracken asserted that pitchers have almost no control over balls in play. If he's right, we would expect to see essentially random values for the career rates of in-play hits, especially for net in-play hits relative to the team baseline.
But we also found meaningful differences in the number of hits allowed on balls in play. In other words, a large number of pitchers consistently demonstrated the ability to limit the number of those hits. Their influence on these outcomes isn't as great as it is on the defense-independent stats, but it is real, and it is large enough to be important.
Here's a partial list of the top pitchers based on the number of career hits they saved relative to the IPAvg of their teams. The list includes two figures for each pitcher, the first without adjustments for park and defense and the second with those adjustments:
Pitcher IPHits vsLg IPHits vsTm ----------------- ----------- ----------- Charlie Hough -371 -299
Walter Johnson -277* -214*
Tom Seaver -269 -201
Catfish Hunter -296 -185
Warren Spahn -266 -183
Fergie Jenkins -128 -182
Pete Alexander -197* -177*
Phil Niekro -147 -172
Jim Palmer -315 -170
Ned Garver -71 -168 * excludes seasons before 1913
Charlie Hough has prevented more hits on balls in play than any other pitcher in our study, and our sample includes the last ninety years, so we've covered most of baseball history. Compared with the league-average pitcher, Hough has allowed 371 fewer hits on balls in play. Compared with his teammates, that figure drops to 299 hits, suggesting that his parks and defenses deserve some of the credit.
How important is 299 hits? Hough would have given up an extra run every three games or so if he had allowed hits on balls in play at the same rate as his teammates over the course of his career. That's a pretty big deal.
Could this happen by chance? No, it couldn't. Hough allowed batters to put 11,586 balls in play over the course of his career. If these results were random, there'd be a 95% chance that his net hits allowed would fall between +93 and -93 and a 99% chance they would fall between +116 and -116. The probability that a pitcher could reduce hits by 299 totally by chance is exceedingly small. (For the statisticians among you, Hough was more than six standard deviations from the mean.)
And Hough wasn't the only one, not by a long shot. In a sample of 351 pitchers with at least 6000 career balls in play, more than 12% of them posted results that would happen less than 1% of the time by chance. And that understates the case, too, because you get to keep pitching if you're that much better than the league, but you usually don't make it to 6000 balls in play if you're that much worse than the league. If one end of the distribution hadn't been truncated by job losses, approximately 20% of those pitchers would have fallen outside the range that can be explained by chance.
There are two knuckle-ballers on this list, and while you can't see it here, I can tell you that if I had run this list a little further, you'd have seen 6 knuckle-ballers in the top 35. (The other four are Eddie Rommel, Ted Lyons, Hoyt Wilhelm and Tim Wakefield.)
NOTE: The observation that knuckleball pitchers are especially good in this area is not new. Craig Wright noted the same thing in his email to Rob Neyer in January, 2001, and McCracken made this point in an article on Baseball Primer last year.
Some pitchers got a lot of help from their defense and park -- almost half of Jim Palmer's hits saved can be attributed to his defense (mostly) and his park -- while others look even better after the defense/park adjustment.
Of course, when you rank players based on counts, rather than averages, you're going to see a lot of guys with very long careers at the top of the list. So let's rank them again, this time dividing career hits saved by career balls in play, and setting a minimum of 5000 balls in play:
Pitcher IPAvg vs Lg IPAvg vs Tm ----------------- ----------- ----------- Charlie Hough -.032 -.026 Don Wilson -.015 -.023 Andy Messersmith -.033 -.021 Ned Garver -.008 -.020 Tim Wakefield -.020 -.019 Catfish Hunter -.028 -.017 Bud Black -.020 -.017 Oral Hildebrand -.015 -.017 Walter Johnson -.021 -.016 Dave Stieb -.022 -.016
Hough remains the career leader by holding enemy hitters to an in-play batting average that was 26 points lower than that of the pitchers on his teams. That's a very substantial advantage, and one that is entirely inconsistent with McCracken's conclusion.
To recap, this examination of career totals suggests very strongly that a meaningful number of pitchers have demonstrated the ability to reduce the rate of hits on balls in play.
By comparing the results for two seasons, McCracken concluded that "there is little correlation between what a pitcher does one year in the stat and what he will do the next." I'll start by looking at a few of the pitchers mentioned in the McCracken article, then expand the study and get a little more scientific.
McCracken pointed out that Greg Maddux had one of the league's best marks in baseball in 1998, then had one of the worst in 1999, and bounced back with a good in-play average in 2000. The following chart shows his entire career, with bars going up indicating an IPAvg that was worse than average and the bars going down indicating a lower-than-average rate of hits on balls in play:
The wild swings of 1998-2000 look like an anomaly when you examine Maddux's entire career. In fact, it appears that he struggled a bit as a youngster, reeled off a decade of good-to-great performances, then began to lose it as he got into his mid-30s. That sounds like a pretty normal career progression to me.
Pedro Martinez was another pitcher who gave up a lot of in-play hits in 1999 but bounced back in 2000. It should be noted that Pedro had a 2.07 ERA despite all those in-play hits in 1999, so we can only imagine what he would have done if he'd been a little less unlucky. Here's Pedro's career:
There's really only one bad year in this line, but it happened to fall in one of the years McCracken looked at. I think it's fair to say that Pedro has shown an above-average ability to prevent hits on balls in play, but his influence on these results is much less than on strikeouts, where he consistently mowed down an extra 90 or more hitters a year, and an incredible 181 more than average in 1999.
McCracken wrote that "You'll often hear people use names like Randy Johnson, Jamie Moyer and Andy Pettitte [as being very good at preventing hits on balls in play], but by any definition you want to use, these guys are not particularly good in the stat." Here's Moyer's career:
Moyer wasn't very good in this respect, or in most other respects, for the first half of his career. But he figured something out in 1996 and has been consistently better than the league ever since, with the exception of 2000. If I was McCracken and I was looking at the 1999 and 2000 seasons, I would have concluded that Moyer isn't particularly effective in preventing hits, but his last seven years say otherwise.
By the way, it's tempting to assume that Safeco Field and a very good Seattle defense are responsible for these recent successes, but that wouldn't be true. First of all, the 1996-1999 numbers were accumulated in a mix of Fenway Park, the Kingdome, and Safeco, with only the second half of 1999 in Safeco. More importantly, these numbers are relative to the in-play average for his teams, so they already factor out the impact of the park and the defense. The bottom line is that Jamie Moyer has been a master at preventing hits on balls in play since 1996.
How about Andy Pettitte? Here's his career:
McCracken was quite correct in pointing out that Pettitte is not a pitcher who prevents hits on balls in play. On the other hand, he's a very good counter-example regarding the claim that pitchers are not consistent in this regard.
Randy Johnson is the third pitcher mentioned by McCracken in the quote I cited above. Here's how Johnson has fared on balls in play over his career:
That's nine straight seasons at or better than the league average, followed by five seasons that were league-average or worse. The shift occurred at the very moment that he moved from the AL to the NL. I'm not sure whether that's meaningful, or whether it has more to do with the fact that he turned 35 in 1998. Like Pedro, Johnson's main asset is not his ability to prevent hits on balls in play, it's his ability to prevent balls in play in the first place. But Johnson was pretty good on those balls in play for nine years.
McCracken also claimed that "Randy Johnson gives up fewer hits than Scott Karl. That's not because batters hit the ball harder off Karl than Johnson, but because they hit the ball more often off Karl than Johnson." Here's Karl's career:
You might be able to make the case that Karl in his prime wasn't any worse than Randy Johnson in his late 30s, but if you compare the two pitchers at the same age, there's a noticeable edge for Johnson.
While we're on the subject of consistency from year to year, let's take a look at some of the knuckleballers, starting with Charlie Hough:
This chart is a little misleading in one respect. There are two bars for 1980, one for each of the teams he played for that year. Hough's IPAvg was awful in his 32 innings with the Dodgers and quite good in his 61 innings with Texas. Overall, he was a little worse than average for the year. The bottom line is that Hough was remarkably good at preventing hits on balls in play for a very long time.
Here's another knuckleballer, Tim Wakefield:
And a third knuckleballer, Phil Niekro:
Hough and Wakefield were remarkably good throughout their careers, and if you ignore the years after his 43rd birthday (1983 to the end), you could say the same about Niekro, too.
Number two on the all-time list was Walter Johnson, whose career looked like this:
Remember, I cut things off at 1913, so this leaves out his early years. It's quite possible that he would have been the all-time leader if those seasons had been included.
Sandy Koufax got some help from Dodger Stadium, but that wasn't the only reason he was so dominant during the last five years of his career. Even with the park and defense factored out, his IPAvg was consistently good during those years:
Finally, here's Jim Palmer, another Hall-of-Famer who was consistently good on balls in play during his career, except for the very beginning and end of his time in the big leagues:
If I had run Palmer's chart showing his performance relative to the league average (instead of his team), it would have been twice as impressive.
We could go on and do a lot more pitchers, but I think we've seen enough to make the point that it's not too hard to find examples where these in-play averages appear to be anything but random. In other words, this is highly persuasive evidence that these pitchers did indeed have the ability to prevent hits on balls in play.
It goes without saying that one cannot prove or disprove the idea that "there is little correlation between what a pitcher does one year in the stat and what he will do the next" by examining only ten or twelve careers.
To get a better handle on this phenomenon, I compiled a database consisting of all pairs of consecutive seasons in which a pitcher faced at least 400 batters in each season. Using this sample of 7,486 season-pairs, I computed the correlation coefficient for the net HBP rate, BB rate, K rate, HR rate, and in-play hit rate.
I found the highest correlation (.73) for strikeout rates. Walk rates (.66) were also highly correlated. The correlation coefficients dropped to .36 for hit batsmen, .29 for homeruns, and .16 for in-play batting average relative to the league. The lowest correlation (.09) was seen for in-play batting average relative to the team.
It may appear to be contradictory to say that certain pitchers appear to be consistently good while the overall correlation rate is quite low. But that's not necessarily so.
If McCracken is right, the difference between a pitcher's IPAvg and that of his team should vary randomly around zero as he moves through his career, and the correlation would be quite weak.
But if pitchers do have some influence over these outcomes, they could still exhibit a weak correlation by varying around some value other than zero that reflects the ability of the pitcher.
Most of our work to this point has focused on pitchers who had long and mostly successful careers in the big leagues. How do the DIPS and IPAvg stats of these players compare to those of players who weren't good enough to last that long?
The following table shows how eleven groups of pitchers compared with the overall averages. The first row includes all pitchers who faced less than 1,000 batters in their careers. The second row includes all pitchers who faced at least 1,000 batters but less than 2,000 batters during their careers. And so on.
Career BF BF HBP BB K HR vsLg vsTm 1 - 999 401,138 .002 .027 -.017 .002 .017 .015 1000 - 1999 931,981 .001 .013 -.009 .001 .006 .004 2000 - 2999 1,105,712 .001 .007 -.005 .000 .002 .001 3000 - 3999 1,179,916 .000 .006 -.003 .000 .000 .000 4000 - 4999 906,271 .000 .002 -.002 .000 .000 .001 5000 - 5999 920,680 .000 .001 .000 .000 .000 .000 6000 - 6999 647,553 .000 -.004 -.002 .001 -.001 -.001 7000 - 7999 843,937 .000 -.003 .000 .000 -.002 -.001 8000 - 8999 716,200 -.001 -.005 .005 .000 -.002 -.002 9000 - 9999 788,532 .000 -.008 -.001 -.001 -.002 -.001 10000+ 2,589,409 -.001 -.010 .008 -.001 -.004 -.003
Let's walk through the first row so it's clear how to read this table. Those pitchers, as a group:
As you can see from the table, the pitchers with longer careers were progressively better than their shorter-career counterparts in every respect. They walked fewer batters, struck out more hitters, gave up fewer homeruns, and gave up fewer hits on balls in play. The ability to prevent hits on balls in play appears to be as much of a skill as anything else.
It might be easier to see this in chart form, so here are the walk rate, strikeout rate, homerun rate, and in-play averages for these groups of pitchers:
Another interesting aspect of this breakdown by career length is the total number of batters faced by each group. Only a very small percentage of batters are faced by pitchers with short careers. Of the roughly 11 million plate appearances since 1913 (including the Federal League of 1914-15), only 3.6% featured pitchers who finished their careers with less than 1000 batters faced.
In fact, the midpoint falls in the 6000-6999 group. A little more than half of the plate appearances since 1913 have been initiated by a pitcher who faced at least 6000 hitters in his career. We, along with other baseball analysts, often compare pitchers to the league average. Those league averages reflect the fact that the majority of plate appearances involve pitchers who are good enough to face thousands of big-league hitters.
That's a very high standard. And that may explain why it's difficult for any pitcher to consistently perform at a level higher than the league average. The table shows that the pitchers with the longest careers are only a little better than average. (They peak at a higher level, of course, but if you take their entire careers, there's not a huge difference.)
A better indicator may be the comparison of the short-career pitchers to the league averages. The chart shows that these marginal hurlers are far worse than the average in every way. In particular, they give up a lot more hits on balls in play than do the pitchers who are good enough to be big-league regulars for several years.
At this point, we've seen (a) career totals that demonstrate that pitchers do influence these outcomes over the course of their careers, (b) several examples of pitchers who have been very consistent in IPAvg during their careers, and (c) that pitchers with longer careers are better than pitchers with shorter careers in every respect, including IPAvg.
In other words, pitchers do affect the rate of hits on balls in play. That means we can no longer use the team's IPAvg as a baseline against which to evaluate a pitcher. McCracken asserted that the team's IPAvg depended only on the park and the defense, but we've found that it depends on the park, the defense, and the quality of the pitchers on that team. If we use team IPAvg as the baseline, a good pitcher on a good staff is going to look worse than he really is. A good pitcher on a bad staff is going to look better than he really is. A good pitcher on an average team is still going to look a little worse than he really is because his own good performance is included in the team's IPAvg.
That leads to a good question, one that is not easily resolved. Is it better to compare a pitcher's IPAvg to that of his league or his team? If we use the league IPAvg as our baseline, we leave out the impact of the park and the defense. If we use the team's IPAvg as the baseline, we adjust for the park and the defense, but we introduce the quality of the fellow pitchers as a variable that can skew the results.
Neither approach is completely satisfactory. It's probably best to evaluate each pitcher's IPAvg against that of his team but make some accommodation for the quality of the pitching staff before making any judgments about that pitcher and before making any predictions about future performance.
In addition to ranking pitchers on IPAvg, this exercise provides a different way of looking at pitching careers. By putting each pitcher's career totals for net HBP, BB, K, HR, and IPHits side by side, we get a very clear picture of the reasons why they were successful.
Let's do a few, starting with Roger Clemens:
How's that for a picture of all-around greatness? Sure, he hit a few more batters than the average pitcher, but compared to the league averages, he walked 173 fewer and struck out 1,355 more, allowed 138 fewer homers, and surrendered 101 fewer hits on balls in play. (The IPHits figures include the defense/park adjustments for all of these profiles.)
Pedro Martinez shows a very similar pattern to that of Roger Clemens, but based on less than half of Clemens' batters faced.
Greg Maddux demonstrates awesome control, an above-average K rate, and the ability to keep the ball in the park. He had some influence on IPAvg, but that was only a part of his success.
By the way, some of those 69 hits saved might be attributable to his own defensive skill rather than his pitching skill. It's also quite possible that the -69 figure signficantly understates his contribution. Maddux saved 97 hits relative to the league averages, and now that we've shown that the team IPAvg reflects the ability of the other pitchers on the staff, that figure may represent Maddux's talent more accurately.
This line shows only one dominating characteristic -- the strikeouts. But if you're going to dominate in one area, that's a good one, because they can't get a hit if they can't put the ball in play. Fortunately for Johnson, his control is only a little worse than the norm, and got better in the later stages of his career.
Guys with below-average strikeout rates aren't supposed to be successful, but Moyer's exceptional control and low IPAvg have been the keys, especially in the later stages of his career.
Now here's a guy who didn't strike anyone out and gave up a lot of hits on balls in play, but survived because he had excellent control and kept the ball in the park. In particular, he kept the ball on the ground, meaning that a lot of those extra hits were singles and that a good number of potential rallies were killed by double plays.
John's profile made me think that it would have been a good idea to extend McCracken's work to measure GDP rates, but that notion didn't hit me until it was too late. Some day, I'll go back and add that to the study and see what pops out.
We can't leave this section without looking at the all-time leader in in-play hits saved. As you can see, Hough hit more batters, walked more batters, struck out only a few more batters, and gave up more homers than the average pitcher. His ability to prevent hits on balls in play is the biggest reason he had a long and successful career.
Is there really any doubt that Don Sutton is a Hall-of-Famer when you look at this profile?
We could go on forever this way, so let's speed things up by looking at groups of pitchers with similar styles. Maybe we'll see some patterns.
HBP BB K HR IPHits Nolan Ryan +44 +878 +2578 -117 -133 Randy Johnson +44 +107 +1769 -52 -10 Roger Clemens +17 -173 +1355 -138 -101 Dazzy Vance +19 -65 +1122 -20 -19 Steve Carlton -49 -1 +1042 +5 -31 Bob Feller -1 +149 +1022 -42 -53 Sandy Koufax -35 +64 +1015 -12 -94 Pedro Martinez +27 -152 +974 -60 -47
Obviously, the defining characteristic of these pitchers was their ability to retire batters without help from anyone else. As a group, with the exception of Ryan, they had average control. All of them were better than average on hits per ball in play, but that wasn't the main reason for their success.
HBP BB K HR IPHits Rich Gossage +10 +90 +492 -31 -57 Lee Smith -17 +25 +447 -21 +12 Tom Henke -10 -24 +391 -12 -20 Rollie Fingers +4 -109 +358 -16 +12 Armando Benitez -3 +78 +332 +1 -41 Trevor Hoffman -17 -34 +317 -8 -49 John Wetteland -5 -25 +310 -5 -39 Billy Wagner 0 +17 +295 -6 -23 Robb Nen -18 -3 +283 -27 +12 Troy Percival 0 +30 +279 -9 -55 Bruce Sutter -4 -48 +269 +1 -54
This is just a special case of the power pitchers group, but it's interesting to see how many of these guys have posted impressive IPHits numbers even though they pitch many fewer innings than do the power pitchers in the previous table.
HBP BB K HR IPHits Robin Roberts -40 -772 -15 +56 -82 Pete Alexander -50 -570 +247 -1 -177 Jim Kaat +19 -566 -264 -4 +144 Ferguson Jenkins -12 -534 +635 +125 -182 Greg Maddux -4 -507 +150 -147 -69 Ted Lyons -43 -481 -366 -7 -121 Dutch Leondard +7 -477 -100 -53 -64 Don Sutton -25 -476 +512 +42 -138 Lew Burdette -9 -445 -611 -13 +32 Walter Johnson +25 -442 +847 -20 -214
Some of these guys (Roberts, Jenkins, and Sutton) gave up more than their share of homers, but with control this good, plus the ability to reduce hits on balls in play, a lot of those homers were solo shots.
HBP BB K HR IPHits Warren Spahn -63 -437 -36 -44 -183 Bud Black 2 -110 -204 +23 -114 Randy Jones -18 -189 -346 -13 -97 Wilbur Wood 0 -238 -135 -13 -84 John Tudor -3 -146 -50 +5 -82 Kenny Rogers +3 -39 -105 -40 -74 Larry Gura +4 -127 -276 +21 -72 Jim Deshaies -7 +21 -34 +44 -72 Jamie Moyer 0 -238 -153 +15 -65 Don Carman +4 +44 -4 +36 -65
This is a list of left-handed pitchers with below-average strikeout rates. Most had very good control, but six of them were at least as susceptible to the long ball as the average pitcher. A significant part of their success is/was the ability to keep hitters off balance and keep their in-play batting averages down.
We've seen that there's more than one way to succeed as a big-league pitcher. Robin Roberts walked 772 fewer batters than his peers. Roger Clemens struck out 1355 more batters than average. Greg Maddux yielded 147 fewer homeruns. And Charlie Hough prevented somewhere between 299 and 371 hits on balls in play.
So what's the most important element of a pitcher's repertoire?
Well, the value of various baseball events depends on the era. When scoring is up, as it has been in recent years, an extra baserunner comes around to score more often than during a period like the 1960s. In The Hidden Game of Baseball, Pete Palmer provided a table of run values for various periods in the 20th century, and I'll use those values to evaluate these events.
Palmer puts the value of a walk at about a third of a run, so the 772 walks saved by Robin Roberts are worth about 250 runs over the course of a career. That's not bad.
Clemens struck out 1355 more batters, but if he hadn't, some of those batters would have reached base, and some would have been retired in other ways. If his strikeout rate had been at the league average, it's possible that he would have allowed another 125 walks, 35 homers, and 320 more hits on balls in play. Using Palmer's run values and reasonable assumptions about the distribution of those hits among singles, doubles, and triples, those strikeouts are worth about 250-280 runs.
Palmer puts the value of a homer at about 1.4 runs, so Maddux saved about 200 runs by keeping his homerun rate down.
And the 300+ hits saved by Hough are worth about 150-175 runs.
Those are impressive figures, and they'd be even more impressive if we were evaluating them against replacement level pitchers instead of the league average. As we noted before, the league average is a very high standard.
The bottom line is that success in all four areas is important. You can have a good career if you're average in all four areas or if you can offset one weak area with a strength in another. You can have a very good career if you have no major weaknesses and you have a special ability in one of these respects. And you can have a great career if you're better than average in all four areas.
Having completed this study, I can sum up my own beliefs as follows:
1. Pitchers have more influence over in-play hit rates than McCracken suggested. In fact, some pitchers (like Charlie Hough and Jamie Moyer) owe much of their careers to the ability to excel in this respect.
2. Their influence over in-play hit rates is weaker than their influence over walk and strikeout rates. The most successful pitchers in history have saved only a few hits per season on balls in play, when compared with the league or team average. That seems less impressive than it really is, because the league average is such a high standard. Compared to a replacement-level pitcher, the savings are much greater.
3. The low correlation coefficients for in-play batting average suggest that there's a lot more room for random variation in these outcomes than in the defense-independent outcomes. I believe this follows quite naturally from the physics of the game. When a round bat meets a round ball at upwards of 90 miles per hour, and when that ball has laces and some sort of spin, miniscule differences in the nature of that impact can make the difference between a hit and an out. In other words, there's quite a bit of luck involved.
4. Year-to-year variations in IPAvg-versus-team can occur if the quality of a pitcher's teammates varies from year to year, even if that pitcher's performance is fairly consistent.
5. The fact that there's room for random variation doesn't necessarily mean a pitcher doesn't have any influence over the outcomes. It just means that his year-to-year performances can vary randomly around value other than zero, a value that reflects his skills.
6. Unusually good or bad in-play hit rates aren't likely to be repeated the next year. This has significant implications for projections of future performance.
7. Even if a pitcher has less influence on in-play averages than on walks and strikeouts, that doesn't necessarily mean that in-play outcomes are less important. Nearly three quarters of all plate appearances result in a ball being put in play. Because these plays are much more frequent, small differences in these in-play hit rates can have a bigger impact on scoring than larger differences in walk and strikeout rates.
The process of separating pitching stats into defense-independent and defense-dependent groups is illuminating. The notion that pitchers don't have as much control over in-play outcomes as they do over defense-independent outcomes is both obvious (in retrospect) and very important. Voros McCracken deserves a lot of credit for introducing this way of thinking.
The bottom line, though, is that I am convinced that pitchers do influence in-play outcomes to a significant degree. There's a reason why Charlie Hough and Jamie Moyer and Phil Niekro and Tom Glavine and Bud Black have had successful careers despite mediocre strikeout rates. There's a reason why the top strikeout pitchers have also suppressed in-play hits at a good rate. Using power or control or deception or a knuckleball, pitchers can keep hitters off balance and induce more than their share of routine grounders, popups, and lazy fly balls.
]]>Written by Tom Tippett
September 15, 2003
After Oakland's 8-6 win over the Red Sox on August 20th, these two quotes appeared in ESPN.com's game story:
"I feel like we stole two games," Oakland third baseman Eric Chavez said. "These aren't the kind of games we're going to win down the line."
"We felt like we had the right people up there at the right time at several points in the game, but we couldn't get more runs across," Boston manager Grady Little said.
Chavez talked about stealing the game because Boston outhit the A's 18 to 11 and drew seven walks to only one for Oakland. Add up the total bases and walks (TBW) for both teams and you find that Boston outproduced the visitors 28 bases to 19. But 17 Red Sox runners were stranded, Oakland bunched their hits with a key Boston error in a four-run eighth inning, and the visiting team went home with the win.
That got us thinking. How often does this happen? How often does a team win the statistical battle yet lose the final-score war?
For several years, we've been looking at measures of team production to learn more about why a season played out the way it did and to get a sense for each team's chances the next year. (For our recap of the 2002 season , see Measuring Team Efficiency).
One of those measures is total bases plus walks. By comparing the TBW produced by each team's hitters with the TBW allowed by its pitchers, we get a good indication of the strength of that team.
Most times, those TBW figures flow quite naturally into runs, which flow quite naturally into wins, and you can see the statistical underpinning for a team's performance. For instance, the 2002 Yankees produced 558 more TBW than they allowed, outscored their opponents by 200 runs, and finished with the AL's best record.
Sometimes, however, these relationships don't hold up. The 2002 Angels were exceptionally good at converting offensive events into runs, compiling a run margin that was a little better than New York's even though their TBW differential was less than half that of the Yankees. By taking full advantage of their opportunities, they finished with 99 wins, beat New York in the divisional series, and didn't stop until they'd won it all.
We've been wondering whether we'd learn anything by applying this approach to the results of individual games. How often does the team with the higher TBW figure actually win the game? And do the games that go the other way have a significant effect on the standings?
While the TBW differential is a very good measure of team performance over a season and has the advantage of being easy to figure, it isn't perfect. Among other things, it doesn't include events like hit batsmen and errors.
Most of the time, we can safely ignore those events when evaluating full seasons. The difference between bases gained by a team and given to its opponents in these ways is usually very small and doesn't affect any conclusions one might draw from the TBW differentials.
In a single game, though, HBP and errors can make the difference, so we added them for this project. For every game in the last ten years (through the end of August, 2003) we computed the number of bases produced by each team on hits, walks, HBP and errors that allowed their batters to reach base.
It turns out that the team that produced more bases in these ways was the victor 82% of the time. In 4% of the games, the teams tied in bases produced, so the win could have gone to either team. That leaves 14%, or about one game in seven, in which a team was outproduced but found a way to win anyway.
In a little more than half of the games that went to the less productive team, the winners were outproduced by only one or two bases, leaving about 7% of the games in which one team overcame a deficit of at least three bases. For the rest of this discussion, we'll focus on this subset, and for lack of a better term, we'll call them "stolen games".
Two of the biggest steals of the 2003 season came in back-to-back games involving Anaheim and Texas.
On April 15th, at Texas, the Angels drew four walks and pounded out out ten hits, including a triple and a pair of homers, for a total of 22 bases on hits and walks. Meanwhile, Jarrod Washburn and Brandon Donnelly held the Rangers to six hits (two doubles), three walks, and a hit batsman, for a total of 12 bases. But Texas won 5-4 because the Anaheim hits were scattered and much of the Texas action was crammed into a single five-run inning.
The tables were turned a day later. Both teams had 13 hits, allowed one hitter to reach on an error, and drew four walks. But the Rangers blasted four homers to none for the Angels. Add it all up and the Texas hitters accounted for 12 more bases. All that production went for naught, however, when Anaheim bunched their hits in a seven-run eighth inning that gave them an 8-7 win. This deficit of 12 bases was the season's largest for a winning team.
In the past ten years, only 17 games (of 22,334 that were played) have exhibited a larger deficit, topped by a pair of games in which the winner overcame an 18-base shortfall.
With 7% of all wins going to a team that overcame a deficit of at least three bases, we'd expect each club to have about five wins and five losses of this type through the end of August.
And most did. Twenty-four teams had between 3 and 7 stolen wins, while twenty-six teams gave away between 3 and 7 losses of this type.
The Cincinnati Reds were far and away the biggest winners in the 2003 stolen-game sweepstakes. Twelve times the Reds picked up a victory in a game in which they were outproduced by at least three bases. Only once did they lose a game in this fashion. That's why they were able to hang around .500 for a few months despite having the worst run margin and the worst TBW differential in the NL.
Montreal has also improved its standing by winning eight and losing only three of these games. But, like the Reds, the Expos faded after a promising start and are no longer serious contenders for a postseason berth.
Three teams have lost more than their share of these games, but two of them are Detroit (five more stolen losses than wins) and San Diego (six more). Nothing of great importance there, at least in terms of postseason implications.
And then there are the Boston Red Sox. Only three teams had more than seven stolen losses, and Boston heads that list with twelve. With only four stolen wins to their credit, Boston has lost eight more stolen games than they've won, easily the worst imbalance in the majors.
In case you want to check out the boxscores and game logs, here are the games:
Date Opp Bases Score Comment 5/11 @Min 31-24 8-9 Rally from 8-0 deficit falls short 5/21 NYY 17-13 2-4 5/31 @Tor 27-23 7-10 Five Tor hits bunched in 5-run sixth 6/10 StL 32-29 7-9 6/12 StL 37-31 7-8 Nixon leaves based loaded four times 6/28 Flo 33-22 9-10 Marlins score four each in 8th and 9th 7/3 @Tam 26-20 5-6 7/25 NYY 19-16 2-4 8/8 Bal 24-20 4-10 O's get 6 of their 13 hits in 7-run inning 8/8 Bal 15-11 2-4 8/10 Bal 23-17 3-5 8/20 Oak 28-19 6-8 Boston strands 17 runners
The four-game series against Baltimore in early August was particularly disheartening for Boston fans. The home team outproduced the O's in every game but still managed to lose the series three games to one.
In 17 of the 19 games against the Yankees (including the three games in September), the more productive side emerged victorious. But both of the stolen games went in New York's favor. So the season series, won 10-9 by the Yankees, turned on these stolen games.
Remember that these twelve losses were in games where Boston outproduced their opponents by at least three bases. They also lost five games in which they had an edge of one or two bases, and their overall record in these games was 5-17. That's a very big deal.
This isn't the only statistical evidence to support the idea that Boston hasn't taken full advantage of its opportunities this year. Their run margin is right up there with Seattle's for the league lead. And their TBW differential (+539 through 9/14) is far better than New York's (+403).
In fact, the Sox are on pace to post the fifth-best TBW differential in the past thirty years. The only teams ahead of them on that list are the 1998 Braves (who finished with a 106-56 record), the 1998 Yankees (114-48), the 2001 Mariners (116-46), and the 1995 Indians (100-44 in a shorter season). That's great company. In other words, this Boston team is a statistical juggernaut that should be leading the league in wins.
Note: These measures of team performance exclude stolen bases. Looking over the boxscores for the dozen games listed above, I found only one game where steals might have made the difference. Boston has been a good running team this season, and I don't believe the conclusions would have changed if we had included stolen bases in our measure of a team's performance in a game.
After they blew the August 20th game against Oakland, I thought the Red Sox were done. Time after time, they had been able to bounce back from tough losses, and they've earned a lot of praise for being a resilient team. But you can only dig a hole and climb out of it so often, and I thought they may have used up their quota.
To their credit, they won the series finale against Oakland, swept the Mariners at home, and took two of three from New York in Yankee Stadium the next weekend. During the toughest part of the schedule, they played their best baseball of the season.
So they're still in position to be playing in October. But had the Red Sox been able to play .500 ball in these stolen games, their magic number for clinching a playoff spot would be in the low single digits right now. Instead, they're fighting tooth and nail just to get in.
Does their poor record in these games point to a weakness in the makeup of this team? Or was it just a run of bad luck? I don't know the answer to these questions. I can say that the Red Sox are a very strong team statistically, and if they can put all of this behind them and start posting a win-loss record that is consistent with their production, they can be very dangerous in October.
That, of course, is a very big if. And if Boston doesn't make the playoffs, you can bet that New England can anticipate a winter full of hot-stove conversations about how the Yankees "know how to win" and how the local nine is missing something.
]]>Batters | |||||||||||||||
Name | UID | Tm | AVG | G | AB | H | 2B | 3B | HR | R | RBI | HBP | BB | K | SB |
----------------------- | ----- | --- | ----- | --- | --- | --- | -- | -- | -- | --- | --- | --- | --- | --- | --- |
Hanser Alberto | 29443 | TEX | 0.222 | 41 | 99 | 22 | 2 | 1 | 0 | 12 | 4 | 0 | 2 | 17 | 1 |
Dariel Alvarez | 29456 | BAL | 0.241 | 12 | 29 | 7 | 1 | 0 | 1 | 3 | 1 | 0 | 2 | 8 | 0 |
Nevin Ashley | 29471 | MIL | 0.100 | 12 | 20 | 2 | 1 | 0 | 0 | 2 | 1 | 1 | 0 | 8 | 0 |
Jett Bandy | 29474 | ANA | 0.500 | 2 | 2 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 |
Austin Barnes | 29477 | LAN | 0.207 | 20 | 29 | 6 | 2 | 0 | 0 | 4 | 1 | 1 | 6 | 6 | 1 |
Steve Baron | 29478 | SEA | 0.000 | 4 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
Greg Bird | 29498 | NYA | 0.261 | 46 | 157 | 41 | 9 | 0 | 11 | 26 | 31 | 1 | 19 | 53 | 0 |
Carson Blair | 29505 | OAK | 0.129 | 11 | 31 | 4 | 0 | 0 | 1 | 3 | 3 | 0 | 4 | 18 | 0 |
Ryan Brett | 29519 | TBA | 0.667 | 3 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Socrates Brito | 29523 | ARI | 0.303 | 18 | 33 | 10 | 3 | 1 | 0 | 5 | 1 | 0 | 1 | 7 | 1 |
Trevor Brown | 30198 | SFN | 0.231 | 13 | 39 | 9 | 3 | 0 | 0 | 1 | 5 | 0 | 3 | 8 | 1 |
Keon Broxton | 29527 | PIT | 0.000 | 7 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 1 |
Kris Bryant | 29529 | CHN | 0.275 | 151 | 559 | 154 | 31 | 5 | 26 | 87 | 99 | 9 | 77 | 199 | 13 |
Byron Buxton | 29536 | MIN | 0.209 | 46 | 129 | 27 | 7 | 1 | 2 | 16 | 6 | 1 | 6 | 44 | 2 |
Ramon Cabrera | 29538 | CIN | 0.367 | 13 | 30 | 11 | 1 | 0 | 1 | 4 | 3 | 0 | 0 | 5 | 0 |
Orlando Calixte | 29539 | KCA | 0.000 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Mark Canha | 29540 | OAK | 0.254 | 124 | 441 | 112 | 22 | 3 | 16 | 61 | 70 | 8 | 33 | 96 | 7 |
Daniel Castro | 29550 | ATL | 0.240 | 33 | 96 | 23 | 2 | 1 | 2 | 14 | 5 | 0 | 3 | 15 | 0 |
Darrell Ceciliani | 29555 | NYN | 0.206 | 39 | 68 | 14 | 2 | 0 | 1 | 5 | 3 | 2 | 4 | 25 | 5 |
Dusty Coleman | 30174 | KCA | 0.000 | 4 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 |
Michael Conforto | 30199 | NYN | 0.270 | 56 | 174 | 47 | 14 | 0 | 9 | 30 | 26 | 1 | 17 | 39 | 0 |
Carlos Correa | 29571 | HOU | 0.279 | 99 | 387 | 108 | 22 | 1 | 22 | 52 | 68 | 1 | 40 | 78 | 14 |
Kaleb Cowart | 29573 | ANA | 0.174 | 34 | 46 | 8 | 2 | 0 | 1 | 8 | 4 | 0 | 5 | 19 | 1 |
Cheslor Cuthbert | 29584 | KCA | 0.217 | 19 | 46 | 10 | 2 | 1 | 1 | 6 | 8 | 0 | 4 | 9 | 0 |
Cody Decker | 29597 | SDN | 0.000 | 8 | 11 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 |
Delino DeShields | 29600 | TEX | 0.261 | 121 | 425 | 111 | 22 | 10 | 2 | 83 | 37 | 3 | 53 | 101 | 25 |
Alex Dickerson | 29603 | SDN | 0.250 | 11 | 8 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 |
Wilmer Difo | 29605 | WAS | 0.182 | 15 | 11 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 |
Danny Dorn | 28080 | ARI | 0.167 | 23 | 30 | 5 | 1 | 0 | 0 | 0 | 3 | 0 | 2 | 10 | 0 |
Brandon Drury | 29613 | ARI | 0.214 | 20 | 56 | 12 | 3 | 0 | 2 | 3 | 8 | 1 | 2 | 8 | 0 |
Matt Duffy | 29615 | HOU | 0.375 | 8 | 8 | 3 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 2 | 0 |
Allan Dykstra | 29621 | TBA | 0.129 | 13 | 31 | 4 | 0 | 0 | 1 | 3 | 4 | 1 | 6 | 12 | 0 |
Ed Easley | 29623 | SLN | 0.000 | 4 | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
Taylor Featherston | 29634 | ANA | 0.162 | 101 | 154 | 25 | 5 | 1 | 2 | 23 | 9 | 3 | 7 | 46 | 4 |
Daniel Fields | 29640 | DET | 0.333 | 1 | 3 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 |
Ramon Flores | 29645 | NYA | 0.219 | 12 | 32 | 7 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 4 | 0 |
Rocky Gale | 29650 | SDN | 0.100 | 11 | 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Joey Gallo | 29652 | TEX | 0.204 | 36 | 108 | 22 | 3 | 1 | 6 | 16 | 14 | 0 | 15 | 57 | 3 |
Adonis Garcia | 30173 | ATL | 0.277 | 58 | 191 | 53 | 12 | 0 | 10 | 20 | 26 | 0 | 5 | 35 | 0 |
Dustin Garneau | 29659 | COL | 0.157 | 22 | 70 | 11 | 3 | 0 | 2 | 6 | 8 | 0 | 6 | 14 | 0 |
Slade Heathcott | 29708 | NYA | 0.400 | 17 | 25 | 10 | 2 | 0 | 2 | 6 | 8 | 0 | 2 | 5 | 0 |
Austin Hedges | 29709 | SDN | 0.168 | 56 | 137 | 23 | 2 | 0 | 3 | 13 | 11 | 1 | 8 | 38 | 0 |
Oscar Hernandez | 29717 | ARI | 0.161 | 18 | 31 | 5 | 1 | 0 | 0 | 4 | 1 | 1 | 3 | 15 | 0 |
Odubel Herrera | 29719 | PHI | 0.297 | 147 | 495 | 147 | 30 | 3 | 8 | 64 | 41 | 8 | 28 | 129 | 16 |
John Hicks | 29722 | SEA | 0.063 | 17 | 32 | 2 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 18 | 1 |
Travis Jankowski | 29739 | SDN | 0.211 | 34 | 90 | 19 | 2 | 2 | 2 | 9 | 12 | 0 | 4 | 24 | 2 |
Micah Johnson | 29746 | CHA | 0.230 | 36 | 100 | 23 | 4 | 0 | 0 | 10 | 4 | 2 | 9 | 30 | 3 |
Jung Ho Kang | 29756 | PIT | 0.287 | 126 | 421 | 121 | 24 | 2 | 15 | 60 | 58 | 17 | 28 | 99 | 5 |
Max Kepler | 29762 | MIN | 0.143 | 3 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 |
Kyle Kubitza | 29771 | ANA | 0.194 | 19 | 36 | 7 | 0 | 0 | 0 | 6 | 1 | 0 | 3 | 15 | 0 |
Tyler Ladendorf | 29773 | OAK | 0.235 | 9 | 17 | 4 | 0 | 1 | 0 | 3 | 2 | 0 | 1 | 2 | 0 |
Ryan LaMarre | 29774 | CIN | 0.080 | 21 | 25 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 9 | 0 |
Francisco Lindor | 29793 | CLE | 0.313 | 99 | 390 | 122 | 22 | 4 | 12 | 50 | 51 | 1 | 27 | 69 | 12 |
Ryan Lollis | 30171 | SFN | 0.167 | 5 | 12 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
Dixon Machado | 29804 | DET | 0.235 | 24 | 68 | 16 | 3 | 0 | 0 | 6 | 5 | 0 | 7 | 14 | 1 |
Mikie Mahtook | 29806 | TBA | 0.295 | 41 | 105 | 31 | 5 | 1 | 9 | 22 | 19 | 3 | 6 | 31 | 4 |
Luke Maile | 30209 | TBA | 0.171 | 15 | 35 | 6 | 3 | 0 | 0 | 2 | 2 | 0 | 0 | 8 | 0 |
Deven Marrero | 29817 | BOS | 0.226 | 25 | 53 | 12 | 0 | 0 | 1 | 8 | 3 | 0 | 3 | 19 | 2 |
Jefry Marte | 30172 | DET | 0.213 | 33 | 80 | 17 | 4 | 0 | 4 | 9 | 11 | 0 | 8 | 22 | 0 |
Ketel Marte | 29818 | SEA | 0.288 | 57 | 219 | 63 | 15 | 3 | 2 | 25 | 17 | 0 | 24 | 43 | 8 |
Max Muncy | 29872 | OAK | 0.206 | 45 | 102 | 21 | 8 | 1 | 3 | 14 | 9 | 0 | 9 | 31 | 0 |
Danny Muno | 29873 | NYN | 0.148 | 17 | 27 | 4 | 1 | 0 | 0 | 2 | 0 | 0 | 4 | 11 | 1 |
Tom Murphy | 29877 | COL | 0.257 | 11 | 35 | 9 | 1 | 0 | 3 | 5 | 9 | 0 | 4 | 10 | 0 |
Rey Navarro | 29882 | BAL | 0.276 | 10 | 29 | 8 | 2 | 0 | 1 | 5 | 3 | 0 | 0 | 3 | 0 |
Rico Noel | 30213 | NYA | 0.500 | 15 | 2 | 1 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 5 |
Peter O'Brien | 29899 | ARI | 0.400 | 8 | 10 | 4 | 1 | 0 | 1 | 1 | 3 | 0 | 2 | 5 | 0 |
Hector Olivera | 29908 | ATL | 0.253 | 24 | 79 | 20 | 4 | 1 | 2 | 4 | 11 | 2 | 5 | 12 | 0 |
Paulo Orlando | 29912 | KCA | 0.249 | 86 | 241 | 60 | 14 | 6 | 7 | 31 | 27 | 2 | 5 | 53 | 3 |
Jarrett Parker | 29916 | SFN | 0.347 | 21 | 49 | 17 | 2 | 0 | 6 | 11 | 14 | 0 | 5 | 21 | 1 |
Jose Peraza | 29920 | LAN | 0.182 | 7 | 22 | 4 | 1 | 1 | 0 | 3 | 1 | 0 | 2 | 2 | 3 |
Carlos Perez | 29921 | ANA | 0.250 | 86 | 260 | 65 | 13 | 0 | 4 | 20 | 21 | 0 | 19 | 49 | 2 |
Stephen Piscotty | 29934 | SLN | 0.305 | 63 | 233 | 71 | 15 | 4 | 7 | 29 | 39 | 1 | 20 | 56 | 2 |
Kevin Plawecki | 29935 | NYN | 0.219 | 73 | 233 | 51 | 9 | 0 | 3 | 18 | 22 | 4 | 17 | 60 | 0 |
Michael Reed | 30216 | MIL | 0.333 | 7 | 6 | 2 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 0 |
Rob Refsnyder | 29955 | NYA | 0.302 | 16 | 43 | 13 | 3 | 0 | 2 | 3 | 5 | 0 | 3 | 7 | 2 |
Yadiel Rivera | 29969 | MIL | 0.071 | 7 | 14 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 |
Eddie Rosario | 29987 | MIN | 0.267 | 122 | 453 | 121 | 18 | 15 | 13 | 60 | 50 | 0 | 15 | 118 | 11 |
Addison Russell | 29993 | CHN | 0.242 | 142 | 475 | 115 | 29 | 1 | 13 | 60 | 54 | 3 | 42 | 149 | 4 |
Tyler Saladino | 29994 | CHA | 0.225 | 68 | 236 | 53 | 6 | 4 | 4 | 33 | 20 | 2 | 12 | 51 | 8 |
Miguel Sano | 30003 | MIN | 0.269 | 80 | 279 | 75 | 17 | 1 | 18 | 46 | 52 | 1 | 53 | 119 | 1 |
Scott Schebler | 30008 | LAN | 0.250 | 19 | 36 | 9 | 0 | 0 | 3 | 6 | 4 | 1 | 3 | 13 | 2 |
Kyle Schwarber | 30013 | CHN | 0.246 | 69 | 232 | 57 | 6 | 1 | 16 | 52 | 43 | 4 | 36 | 77 | 3 |
Corey Seager | 30016 | LAN | 0.337 | 27 | 98 | 33 | 8 | 1 | 4 | 17 | 17 | 1 | 14 | 19 | 2 |
Pedro Severino | 30217 | WAS | 0.250 | 2 | 4 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
Richie Shaffer | 30024 | TBA | 0.189 | 31 | 74 | 14 | 3 | 0 | 4 | 11 | 6 | 3 | 10 | 32 | 0 |
Travis Shaw | 30025 | BOS | 0.270 | 65 | 226 | 61 | 10 | 0 | 13 | 31 | 36 | 2 | 18 | 57 | 0 |
Cody Stanley | 30054 | SLN | 0.400 | 9 | 10 | 4 | 1 | 0 | 0 | 2 | 3 | 0 | 0 | 3 | 0 |
Ryan Strausborger | 30219 | TEX | 0.200 | 31 | 45 | 9 | 0 | 0 | 1 | 9 | 3 | 0 | 3 | 11 | 2 |
Darnell Sweeney | 30070 | PHI | 0.176 | 37 | 85 | 15 | 4 | 1 | 3 | 9 | 11 | 0 | 13 | 27 | 0 |
Blake Swihart | 30071 | BOS | 0.274 | 84 | 288 | 79 | 17 | 1 | 5 | 47 | 31 | 1 | 18 | 77 | 4 |
Travis Tartamella | 30220 | SLN | 0.500 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Trayce Thompson | 30083 | CHA | 0.295 | 44 | 122 | 36 | 8 | 3 | 5 | 17 | 16 | 0 | 13 | 26 | 1 |
Yasmany Tomas | 30087 | ARI | 0.273 | 118 | 406 | 111 | 19 | 3 | 9 | 40 | 48 | 2 | 17 | 110 | 5 |
Kelby Tomlinson | 30088 | SFN | 0.303 | 54 | 178 | 54 | 6 | 3 | 2 | 23 | 20 | 1 | 14 | 40 | 5 |
Ronald Torreyes | 30089 | LAN | 0.333 | 8 | 6 | 2 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
Devon Travis | 30094 | TOR | 0.304 | 62 | 217 | 66 | 18 | 0 | 8 | 38 | 35 | 2 | 18 | 43 | 3 |
Preston Tucker | 30097 | HOU | 0.243 | 98 | 300 | 73 | 19 | 0 | 13 | 35 | 33 | 3 | 20 | 68 | 0 |
Trea Turner | 30101 | WAS | 0.225 | 27 | 40 | 9 | 1 | 0 | 1 | 5 | 1 | 0 | 4 | 12 | 2 |
Giovanny Urshela | 30106 | CLE | 0.225 | 81 | 267 | 60 | 8 | 1 | 6 | 25 | 21 | 2 | 18 | 58 | 0 |
Mason Williams | 30136 | NYA | 0.286 | 8 | 21 | 6 | 3 | 0 | 1 | 3 | 3 | 0 | 1 | 3 | 0 |
Mac Williamson | 30140 | SFN | 0.219 | 10 | 32 | 7 | 0 | 1 | 0 | 2 | 1 | 1 | 0 | 8 | 0 |
Pitchers |
Name | UID | Tm | G | GS | W | L | S | ERA | Inn | H | R | ER | BB | K | HR |
----------------------- | ----- | --- | --- | -- | -- | -- | -- | ------ | ----- | --- | --- | --- | --- | --- | -- |
Scott Alexander | 29447 | KCA | 4 | 0 | 0 | 0 | 0 | 4.50 | 6 | 5 | 3 | 3 | 3 | 3 | 0 |
Miguel Almonte | 29453 | KCA | 9 | 0 | 0 | 2 | 0 | 6.23 | 8.7 | 7 | 6 | 6 | 7 | 10 | 4 |
Cody Anderson | 29458 | CLE | 15 | 15 | 7 | 3 | 0 | 3.05 | 91 | 77 | 32 | 31 | 24 | 44 | 9 |
Matt Andriese | 29461 | TBA | 25 | 8 | 3 | 5 | 2 | 4.11 | 66 | 69 | 32 | 30 | 18 | 49 | 8 |
Elvis Araujo | 29465 | PHI | 40 | 0 | 2 | 1 | 0 | 3.38 | 35 | 29 | 17 | 13 | 19 | 34 | 1 |
Shawn Armstrong | 29468 | CLE | 8 | 0 | 0 | 0 | 0 | 2.25 | 8 | 5 | 2 | 2 | 2 | 11 | 1 |
Jonathan Aro | 30186 | BOS | 6 | 0 | 0 | 1 | 0 | 6.97 | 10 | 15 | 8 | 8 | 4 | 8 | 2 |
Alec Asher | 29470 | PHI | 7 | 7 | 0 | 6 | 0 | 9.31 | 29 | 42 | 30 | 30 | 10 | 16 | 8 |
Manny Banuelos | 29475 | ATL | 7 | 6 | 1 | 4 | 0 | 5.13 | 26 | 30 | 17 | 15 | 12 | 19 | 4 |
Kyle Barraclough | 30195 | MIA | 25 | 0 | 2 | 1 | 0 | 2.59 | 24 | 12 | 8 | 7 | 18 | 30 | 1 |
Yhonathan Barrios | 30196 | MIL | 5 | 0 | 0 | 0 | 0 | 0.00 | 6.7 | 3 | 0 | 0 | 0 | 7 | 0 |
Chris Beck | 29484 | CHA | 1 | 1 | 0 | 1 | 0 | 6.00 | 6 | 10 | 5 | 4 | 4 | 3 | 0 |
Andrew Bellatti | 30179 | TBA | 17 | 0 | 3 | 1 | 0 | 2.31 | 23 | 16 | 7 | 6 | 10 | 18 | 4 |
Matt Boyd | 30178 | TOR | 2 | 2 | 0 | 2 | 0 | 14.85 | 6.7 | 15 | 11 | 11 | 1 | 7 | 5 |
Matt Boyd | 30178 | DET | 11 | 10 | 1 | 4 | 0 | 6.57 | 51 | 56 | 39 | 37 | 19 | 36 | 12 |
Silvino Bracho | 30197 | ARI | 13 | 0 | 0 | 0 | 1 | 1.46 | 12 | 9 | 2 | 2 | 4 | 17 | 2 |
Archie Bradley | 29516 | ARI | 8 | 8 | 2 | 3 | 0 | 5.80 | 36 | 36 | 23 | 23 | 22 | 23 | 3 |
Jake Brigham | 29521 | ATL | 12 | 0 | 0 | 1 | 0 | 8.64 | 17 | 28 | 16 | 16 | 8 | 12 | 1 |
Mike Broadway | 29525 | SFN | 21 | 0 | 0 | 2 | 0 | 5.19 | 17 | 20 | 10 | 10 | 7 | 13 | 1 |
Danny Burawa | 29530 | NYA | 1 | 0 | 0 | 0 | 0 | 54.00 | 0.7 | 3 | 4 | 4 | 1 | 1 | 1 |
Danny Burawa | 29530 | ATL | 12 | 0 | 0 | 0 | 0 | 3.65 | 12 | 8 | 5 | 5 | 4 | 10 | 1 |
Enrique Burgos | 29533 | ARI | 30 | 0 | 2 | 2 | 2 | 4.67 | 27 | 27 | 15 | 14 | 15 | 39 | 2 |
Angel Castro | 29549 | OAK | 5 | 0 | 0 | 1 | 0 | 2.25 | 4 | 8 | 1 | 1 | 3 | 4 | 1 |
Miguel Castro | 30169 | COL | 5 | 0 | 0 | 1 | 0 | 10.13 | 5.3 | 6 | 6 | 6 | 4 | 6 | 2 |
Miguel Castro | 30169 | TOR | 13 | 0 | 0 | 2 | 4 | 4.38 | 12 | 15 | 7 | 6 | 6 | 12 | 2 |
A.J. Cole | 29565 | WAS | 3 | 1 | 0 | 0 | 1 | 5.79 | 9.3 | 14 | 11 | 6 | 1 | 9 | 1 |
Adam Conley | 29567 | MIA | 15 | 11 | 4 | 1 | 0 | 3.76 | 67 | 65 | 28 | 28 | 21 | 59 | 7 |
Tim Cooney | 29568 | SLN | 6 | 6 | 1 | 0 | 0 | 3.16 | 31 | 28 | 12 | 11 | 10 | 29 | 3 |
Scott Copeland | 29569 | TOR | 5 | 3 | 1 | 1 | 0 | 6.46 | 15 | 24 | 11 | 11 | 2 | 6 | 1 |
John Cornely | 30185 | ATL | 1 | 0 | 0 | 0 | 0 | 36.00 | 1 | 3 | 4 | 4 | 1 | 1 | 1 |
Caleb Cotham | 30200 | NYA | 12 | 0 | 1 | 0 | 0 | 6.52 | 9.7 | 14 | 7 | 7 | 1 | 11 | 4 |
Tyler Cravy | 29577 | MIL | 14 | 7 | 0 | 8 | 0 | 5.70 | 43 | 47 | 29 | 27 | 22 | 35 | 5 |
Brandon Cunniff | 29583 | ATL | 39 | 0 | 2 | 2 | 0 | 4.63 | 35 | 27 | 20 | 18 | 22 | 37 | 4 |
Zach Davies | 29588 | MIL | 6 | 6 | 3 | 2 | 0 | 3.71 | 34 | 26 | 14 | 14 | 15 | 24 | 2 |
Abel de los Santos | 30201 | WAS | 2 | 0 | 0 | 0 | 0 | 5.40 | 1.7 | 2 | 1 | 1 | 1 | 3 | 1 |
Jose De Paula | 29595 | NYA | 1 | 0 | 0 | 0 | 0 | 2.70 | 3.3 | 2 | 1 | 1 | 4 | 2 | 1 |
Oliver Drake | 29612 | BAL | 13 | 0 | 0 | 0 | 0 | 2.87 | 16 | 16 | 7 | 5 | 9 | 17 | 1 |
Tyler Duffey | 29614 | MIN | 10 | 10 | 5 | 1 | 0 | 3.10 | 58 | 56 | 20 | 20 | 20 | 53 | 4 |
Ryan Dull | 29618 | OAK | 13 | 0 | 1 | 2 | 1 | 4.24 | 17 | 12 | 8 | 8 | 6 | 16 | 4 |
Carl Edwards | 29624 | CHN | 5 | 0 | 0 | 0 | 0 | 3.86 | 4.7 | 3 | 3 | 2 | 3 | 4 | 0 |
Jerad Eickhoff | 29627 | PHI | 8 | 8 | 3 | 3 | 0 | 2.65 | 51 | 40 | 16 | 15 | 13 | 49 | 5 |
Brian Ellington | 30202 | MIA | 23 | 0 | 2 | 1 | 0 | 2.88 | 25 | 17 | 10 | 8 | 13 | 18 | 1 |
Andrew Faulkner | 29633 | TEX | 11 | 0 | 0 | 0 | 0 | 2.79 | 9.7 | 8 | 3 | 3 | 3 | 10 | 2 |
Michael Feliz | 29635 | HOU | 5 | 0 | 0 | 0 | 0 | 7.88 | 8 | 9 | 7 | 7 | 4 | 7 | 2 |
Jeff Ferrell | 30184 | DET | 9 | 0 | 0 | 0 | 0 | 6.35 | 11 | 12 | 8 | 8 | 4 | 6 | 3 |
Kendry Flores | 29644 | MIA | 7 | 1 | 1 | 2 | 0 | 4.97 | 13 | 16 | 8 | 7 | 4 | 9 | 0 |
Jason Garcia | 29656 | BAL | 21 | 0 | 1 | 0 | 0 | 4.25 | 30 | 25 | 19 | 14 | 17 | 22 | 3 |
Sean Gilmartin | 29668 | NYN | 50 | 1 | 3 | 2 | 0 | 2.67 | 57 | 50 | 17 | 17 | 18 | 54 | 2 |
Mychal Givens | 30190 | BAL | 22 | 0 | 2 | 0 | 0 | 1.80 | 30 | 20 | 7 | 6 | 6 | 38 | 1 |
Zack Godley | 30203 | ARI | 9 | 6 | 5 | 1 | 0 | 3.19 | 37 | 29 | 13 | 13 | 17 | 34 | 4 |
David Goforth | 29672 | MIL | 20 | 0 | 1 | 0 | 0 | 4.01 | 25 | 32 | 13 | 11 | 8 | 24 | 4 |
Chi Chi Gonzalez | 29674 | TEX | 14 | 10 | 4 | 6 | 0 | 3.90 | 67 | 49 | 33 | 29 | 32 | 30 | 6 |
Severino Gonzalez | 29676 | PHI | 7 | 7 | 3 | 3 | 0 | 7.92 | 31 | 44 | 27 | 27 | 7 | 28 | 5 |
Nick Goody | 30204 | NYA | 7 | 0 | 0 | 0 | 0 | 6.35 | 5.7 | 6 | 4 | 4 | 3 | 3 | 0 |
Trevor Gott | 30194 | ANA | 48 | 0 | 4 | 2 | 0 | 3.02 | 48 | 43 | 17 | 16 | 16 | 27 | 2 |
Matt Grace | 29679 | WAS | 26 | 0 | 2 | 1 | 0 | 4.24 | 17 | 26 | 11 | 8 | 8 | 14 | 0 |
J.R. Graham | 29680 | MIN | 39 | 1 | 1 | 1 | 0 | 4.95 | 64 | 73 | 41 | 35 | 21 | 53 | 10 |
Jon Gray | 29681 | COL | 9 | 9 | 0 | 2 | 0 | 5.53 | 41 | 52 | 26 | 25 | 14 | 40 | 4 |
Mayckol Guaipe | 29684 | SEA | 21 | 0 | 0 | 3 | 0 | 5.40 | 27 | 34 | 19 | 16 | 13 | 22 | 5 |
Deolis Guerra | 28057 | PIT | 10 | 0 | 2 | 0 | 0 | 6.48 | 17 | 26 | 12 | 12 | 3 | 17 | 5 |
Junior Guerra | 30188 | CHA | 3 | 0 | 0 | 0 | 0 | 6.75 | 4 | 7 | 3 | 3 | 1 | 3 | 1 |
Jason Gurka | 30205 | COL | 9 | 0 | 0 | 0 | 0 | 9.39 | 7.7 | 16 | 8 | 8 | 2 | 7 | 1 |
Cody Hall | 29697 | SFN | 7 | 0 | 0 | 0 | 0 | 6.48 | 8.3 | 10 | 6 | 6 | 4 | 7 | 1 |
Mitch Harris | 30189 | SLN | 26 | 0 | 2 | 1 | 0 | 3.67 | 27 | 30 | 14 | 11 | 13 | 15 | 4 |
Marcus Hatley | 29704 | SLN | 2 | 0 | 0 | 0 | 0 | 0.00 | 1.3 | 1 | 0 | 0 | 2 | 2 | 0 |
Keith Hessler | 30206 | ARI | 18 | 0 | 0 | 1 | 0 | 8.03 | 12 | 16 | 11 | 11 | 4 | 12 | 4 |
Dalier Hinojosa | 30181 | BOS | 1 | 0 | 0 | 0 | 0 | 0.00 | 1.7 | 0 | 0 | 0 | 3 | 2 | 0 |
Dalier Hinojosa | 30181 | PHI | 18 | 0 | 2 | 0 | 0 | 0.78 | 23 | 15 | 3 | 2 | 8 | 21 | 1 |
Adrian Houser | 30207 | MIL | 2 | 0 | 0 | 0 | 0 | 0.00 | 2 | 1 | 0 | 0 | 2 | 0 | 0 |
Edgar Ibarra | 29733 | ANA | 2 | 0 | 0 | 0 | 0 | 2.25 | 4 | 4 | 1 | 1 | 3 | 3 | 0 |
Raisel Iglesias | 29735 | CIN | 18 | 16 | 3 | 7 | 0 | 4.15 | 95 | 81 | 45 | 44 | 28 | 104 | 11 |
Jay Jackson | 29736 | SDN | 6 | 0 | 0 | 0 | 0 | 6.23 | 4.3 | 7 | 3 | 3 | 1 | 4 | 0 |
Luke Jackson | 29737 | TEX | 7 | 0 | 0 | 0 | 0 | 4.26 | 6.3 | 5 | 3 | 3 | 2 | 6 | 1 |
Brian Johnson | 29745 | BOS | 1 | 1 | 0 | 1 | 0 | 8.31 | 4.3 | 3 | 4 | 4 | 4 | 3 | 0 |
Taylor Jungmann | 29753 | MIL | 21 | 21 | 9 | 8 | 0 | 3.77 | 119 | 106 | 55 | 50 | 47 | 107 | 11 |
Keone Kela | 29758 | TEX | 68 | 0 | 7 | 5 | 1 | 2.39 | 60 | 52 | 18 | 16 | 18 | 68 | 4 |
Ryan Kelly | 30191 | ATL | 17 | 0 | 0 | 0 | 0 | 7.02 | 17 | 21 | 14 | 13 | 6 | 10 | 5 |
Guido Knudson | 29769 | DET | 4 | 0 | 0 | 0 | 0 | 18.00 | 5 | 13 | 10 | 10 | 3 | 6 | 5 |
John Lamb | 29775 | CIN | 10 | 10 | 1 | 5 | 0 | 5.80 | 50 | 58 | 32 | 32 | 19 | 58 | 8 |
Raudel Lazo | 30208 | MIA | 7 | 0 | 0 | 0 | 0 | 3.18 | 5.7 | 5 | 2 | 2 | 2 | 5 | 1 |
Jack Leathersich | 29784 | NYN | 17 | 0 | 0 | 1 | 0 | 2.31 | 12 | 12 | 3 | 3 | 7 | 14 | 0 |
Zach Lee | 29786 | LAN | 1 | 1 | 0 | 1 | 0 | 13.50 | 4.7 | 11 | 7 | 7 | 1 | 3 | 1 |
Arnold Leon | 29790 | OAK | 19 | 0 | 0 | 2 | 0 | 4.39 | 27 | 30 | 14 | 13 | 9 | 19 | 3 |
Adam Liberatore | 29791 | LAN | 39 | 0 | 2 | 2 | 0 | 4.25 | 30 | 26 | 14 | 14 | 9 | 29 | 3 |
Jacob Lindgren | 29792 | NYA | 7 | 0 | 0 | 0 | 0 | 5.14 | 7 | 5 | 4 | 4 | 4 | 8 | 3 |
Jorge Lopez | 29800 | MIL | 2 | 2 | 1 | 1 | 0 | 5.40 | 10 | 14 | 6 | 6 | 5 | 10 | 0 |
Michael Lorenzen | 29801 | CIN | 27 | 21 | 4 | 9 | 0 | 5.40 | 113 | 131 | 70 | 68 | 57 | 83 | 18 |
Sugar Ray Marimon | 29812 | ATL | 16 | 0 | 0 | 1 | 0 | 7.36 | 26 | 30 | 21 | 21 | 14 | 14 | 3 |
Matt Marksberry | 30210 | ATL | 31 | 0 | 0 | 3 | 0 | 5.01 | 23 | 22 | 16 | 13 | 16 | 21 | 2 |
Cody Martin | 29819 | OAK | 4 | 2 | 0 | 2 | 0 | 14.00 | 9 | 16 | 14 | 14 | 5 | 3 | 4 |
Cody Martin | 29819 | ATL | 21 | 0 | 2 | 3 | 0 | 5.40 | 22 | 24 | 13 | 13 | 7 | 24 | 4 |
Rafael Martin | 29822 | WAS | 13 | 0 | 2 | 0 | 0 | 5.11 | 12 | 12 | 9 | 7 | 5 | 25 | 4 |
Steven Matz | 29824 | NYN | 6 | 6 | 4 | 0 | 0 | 2.27 | 36 | 34 | 9 | 9 | 10 | 34 | 4 |
Cory Mazzoni | 29829 | SDN | 8 | 0 | 0 | 0 | 0 | 20.77 | 8.7 | 23 | 22 | 20 | 5 | 8 | 2 |
Lance McCullers | 29832 | HOU | 22 | 22 | 6 | 7 | 0 | 3.22 | 126 | 106 | 49 | 45 | 43 | 129 | 10 |
Scott McGough | 30211 | MIA | 6 | 0 | 0 | 0 | 0 | 9.45 | 6.7 | 12 | 7 | 7 | 4 | 4 | 0 |
Andrew McKirahan | 29837 | ATL | 27 | 0 | 1 | 0 | 0 | 5.93 | 27 | 40 | 18 | 18 | 10 | 22 | 2 |
Alex Meyer | 29846 | MIN | 2 | 0 | 0 | 0 | 0 | 16.88 | 2.7 | 4 | 5 | 5 | 3 | 3 | 2 |
Frankie Montas | 29860 | CHA | 7 | 2 | 0 | 2 | 0 | 4.80 | 15 | 14 | 8 | 8 | 9 | 20 | 1 |
Mike Montgomery | 29862 | SEA | 16 | 16 | 4 | 6 | 0 | 4.60 | 90 | 92 | 49 | 46 | 37 | 64 | 11 |
Diego Moreno | 30183 | NYA | 4 | 0 | 1 | 0 | 0 | 5.23 | 10 | 9 | 6 | 6 | 3 | 8 | 1 |
Adam Morgan | 29865 | PHI | 15 | 15 | 5 | 7 | 0 | 4.48 | 84 | 88 | 45 | 42 | 17 | 49 | 14 |
Akeel Morris | 30223 | NYN | 1 | 0 | 0 | 0 | 0 | 67.50 | 0.7 | 3 | 5 | 5 | 3 | 0 | 1 |
Jon Moscot | 29870 | CIN | 3 | 3 | 1 | 1 | 0 | 4.63 | 12 | 11 | 6 | 6 | 5 | 6 | 2 |
Toru Murata | 30193 | CLE | 1 | 1 | 0 | 1 | 0 | 8.10 | 3.3 | 4 | 5 | 3 | 1 | 2 | 2 |
Colton Murray | 30212 | PHI | 8 | 0 | 0 | 1 | 0 | 5.87 | 7.7 | 11 | 5 | 5 | 2 | 9 | 2 |
Angel Nesbitt | 29886 | DET | 24 | 0 | 1 | 1 | 0 | 5.40 | 22 | 22 | 14 | 13 | 8 | 14 | 2 |
Justin Nicolino | 29891 | MIA | 12 | 12 | 5 | 4 | 0 | 4.01 | 74 | 72 | 33 | 33 | 20 | 23 | 8 |
Aaron Nola | 29894 | PHI | 13 | 13 | 6 | 2 | 0 | 3.59 | 78 | 74 | 31 | 31 | 19 | 68 | 11 |
Scott Oberg | 30192 | COL | 64 | 0 | 3 | 4 | 1 | 5.09 | 58 | 58 | 35 | 33 | 31 | 44 | 10 |
Nefi Ogando | 29905 | PHI | 4 | 0 | 0 | 0 | 0 | 9.00 | 4 | 7 | 5 | 4 | 2 | 2 | 0 |
Tyler Olson | 29910 | SEA | 11 | 0 | 1 | 1 | 0 | 5.40 | 13 | 18 | 8 | 8 | 10 | 8 | 2 |
Ryan O'Rourke | 29903 | MIN | 28 | 0 | 0 | 0 | 0 | 6.14 | 22 | 16 | 15 | 15 | 15 | 24 | 3 |
Josh Osich | 30176 | SFN | 35 | 0 | 2 | 0 | 0 | 2.20 | 29 | 24 | 12 | 7 | 8 | 27 | 4 |
Roberto Osuna | 30170 | TOR | 68 | 0 | 1 | 6 | 20 | 2.58 | 70 | 48 | 21 | 20 | 16 | 75 | 7 |
Henry Owens | 29915 | BOS | 11 | 11 | 4 | 4 | 0 | 4.57 | 63 | 62 | 35 | 32 | 24 | 50 | 7 |
James Pazos | 30214 | NYA | 11 | 0 | 0 | 0 | 0 | 0.00 | 5 | 3 | 0 | 0 | 3 | 3 | 0 |
Ariel Pena | 29919 | MIL | 6 | 5 | 2 | 1 | 0 | 4.28 | 27 | 24 | 14 | 13 | 14 | 27 | 2 |
Williams Perez | 29924 | ATL | 23 | 20 | 7 | 6 | 1 | 4.78 | 117 | 130 | 66 | 62 | 51 | 73 | 13 |
Branden Pinder | 29932 | NYA | 25 | 0 | 0 | 2 | 0 | 2.93 | 28 | 28 | 9 | 9 | 14 | 25 | 4 |
Noe Ramirez | 29946 | BOS | 17 | 0 | 0 | 1 | 0 | 4.15 | 13 | 13 | 12 | 6 | 7 | 13 | 3 |
Josh Ravin | 30187 | LAN | 9 | 0 | 2 | 1 | 0 | 6.75 | 9.3 | 13 | 7 | 7 | 4 | 12 | 3 |
Colin Rea | 30215 | SDN | 6 | 6 | 2 | 2 | 0 | 4.26 | 32 | 29 | 16 | 15 | 11 | 26 | 2 |
Chris Rearick | 29951 | SDN | 5 | 0 | 0 | 0 | 0 | 12.00 | 3 | 6 | 4 | 4 | 2 | 4 | 2 |
Chris Reed | 29954 | MIA | 2 | 0 | 0 | 0 | 0 | 4.50 | 4 | 6 | 2 | 2 | 1 | 1 | 0 |
Felipe Rivero | 29971 | WAS | 49 | 0 | 2 | 1 | 2 | 2.79 | 48 | 35 | 15 | 15 | 11 | 43 | 2 |
Ken Roberts | 30177 | PHI | 6 | 0 | 1 | 0 | 0 | 10.38 | 4.3 | 9 | 5 | 5 | 1 | 1 | 0 |
Ken Roberts | 30177 | COL | 9 | 0 | 0 | 1 | 0 | 5.79 | 9.3 | 13 | 6 | 6 | 2 | 5 | 0 |
Hansel Robles | 29974 | NYN | 57 | 0 | 4 | 3 | 0 | 3.67 | 54 | 37 | 27 | 22 | 18 | 61 | 8 |
Carlos Rodon | 29975 | CHA | 26 | 23 | 9 | 6 | 0 | 3.75 | 139 | 130 | 63 | 58 | 71 | 139 | 11 |
Eduardo Rodriguez | 29977 | BOS | 21 | 21 | 10 | 6 | 0 | 3.85 | 122 | 120 | 55 | 52 | 37 | 98 | 13 |
David Rollins | 29983 | SEA | 20 | 0 | 0 | 2 | 0 | 7.56 | 25 | 37 | 21 | 21 | 8 | 21 | 3 |
Joe Ross | 29989 | WAS | 16 | 13 | 5 | 5 | 0 | 3.64 | 77 | 64 | 33 | 31 | 21 | 69 | 7 |
Nick Rumbelow | 29991 | NYA | 17 | 0 | 1 | 1 | 0 | 4.02 | 16 | 16 | 8 | 7 | 5 | 15 | 2 |
Keyvius Sampson | 29998 | CIN | 13 | 12 | 2 | 6 | 0 | 6.54 | 52 | 67 | 43 | 38 | 26 | 42 | 7 |
A.J. Schugel | 30011 | ARI | 5 | 0 | 0 | 0 | 0 | 5.00 | 9 | 17 | 13 | 5 | 5 | 5 | 2 |
Luis Severino | 30022 | NYA | 11 | 11 | 5 | 3 | 0 | 2.89 | 62 | 53 | 21 | 20 | 22 | 56 | 9 |
Josh Smith | 30042 | CIN | 9 | 7 | 0 | 4 | 0 | 6.89 | 33 | 42 | 27 | 25 | 21 | 30 | 5 |
Sammy Solis | 30051 | WAS | 18 | 0 | 1 | 1 | 0 | 3.38 | 21 | 25 | 11 | 8 | 4 | 17 | 2 |
Giovanni Soto | 30218 | CLE | 6 | 0 | 0 | 0 | 0 | 0.00 | 3.3 | 3 | 0 | 0 | 0 | 0 | 0 |
Noah Syndergaard | 30072 | NYN | 24 | 24 | 9 | 7 | 0 | 3.24 | 150 | 126 | 60 | 54 | 31 | 166 | 19 |
Ryan Tepera | 30079 | TOR | 32 | 0 | 0 | 2 | 1 | 3.27 | 33 | 23 | 14 | 12 | 6 | 22 | 8 |
Matt Tracy | 30091 | NYA | 1 | 0 | 0 | 0 | 0 | 0.00 | 2 | 2 | 3 | 0 | 2 | 1 | 0 |
Jose Urena | 30103 | MIA | 20 | 9 | 1 | 5 | 0 | 5.25 | 62 | 73 | 37 | 36 | 25 | 28 | 5 |
Jose Valdez | 30107 | DET | 7 | 0 | 0 | 1 | 0 | 4.00 | 9 | 10 | 4 | 4 | 4 | 4 | 2 |
Vincent Velasquez | 30112 | HOU | 19 | 7 | 1 | 1 | 0 | 4.37 | 56 | 50 | 28 | 27 | 21 | 58 | 5 |
Pat Venditte | 30114 | OAK | 26 | 0 | 2 | 2 | 0 | 4.40 | 29 | 22 | 14 | 14 | 12 | 23 | 3 |
Logan Verrett | 30115 | TEX | 4 | 0 | 0 | 1 | 0 | 6.00 | 9 | 11 | 7 | 6 | 4 | 3 | 1 |
Logan Verrett | 30115 | NYN | 14 | 4 | 1 | 1 | 1 | 3.03 | 39 | 23 | 13 | 13 | 11 | 36 | 5 |
Tyler Wagner | 30121 | MIL | 3 | 3 | 0 | 2 | 0 | 7.24 | 14 | 22 | 11 | 11 | 7 | 5 | 1 |
Ryan Weber | 30221 | ATL | 5 | 5 | 0 | 3 | 0 | 4.76 | 28 | 25 | 15 | 15 | 6 | 19 | 3 |
Tyler Wilson | 30144 | BAL | 9 | 5 | 2 | 2 | 0 | 3.50 | 36 | 39 | 14 | 14 | 11 | 13 | 1 |
Daniel Winkler | 30148 | ATL | 2 | 0 | 0 | 0 | 0 | 10.80 | 1.7 | 2 | 2 | 2 | 1 | 2 | 2 |
Matt Wisler | 30151 | ATL | 20 | 19 | 8 | 8 | 0 | 4.71 | 109 | 119 | 59 | 57 | 40 | 72 | 16 |
A.Wojciechowski | 30153 | HOU | 5 | 3 | 0 | 1 | 0 | 7.16 | 16 | 23 | 13 | 13 | 7 | 16 | 2 |
Mike Wright | 30157 | BAL | 12 | 9 | 3 | 5 | 0 | 6.04 | 45 | 52 | 30 | 30 | 18 | 26 | 9 |
Tony Zych | 30222 | SEA | 13 | 1 | 0 | 0 | 0 | 2.45 | 18 | 17 | 6 | 5 | 3 | 24 | 1 |
Some people argue that it's impossible to measure the defensive performance of baseball players because the statistics available for that purpose are woefully inadequate. If you're talking about traditional fielding stats -- games, putouts, assists, errors, double plays -- I wouldn't go so far as to say that it's impossible, but I would agree that it's not easy.
In this article, we'll look at those traditional fielding stats and talk about what you can and cannot learn from them. We'll look at more modern fielding statistics such as Pete Palmer's Fielding Runs, the zone ratings from STATS Inc., and Bill James Win Shares. As the providers of a computer baseball game, one of our ongoing tasks is rating players in all phases of the game, including defense, and we'll talk about how we use detailed play-by-play data from STATS to improve our understanding.
Even with these advances, evaluating defense is not an exact science. If you're a the-glass-is-half-empty sort of person, you could take that to mean it's not worth the effort. But I believe the availability of play-by-play data has raised the level of the water so the glass is now about 90% full, and if you're interested in joining me for a little stroll through the evolution of fielding analysis, I think you'll end up with a better idea of what we can and cannot learn about defense.
The idea of using statistical measures to assess the ability to succeed in a certain phase of the game is not a radical one. Baseball people have been doing this for over a century to measure batting and pitching performances. They don't, after all, give the batting title to the guy with the prettiest swing, they give it to the player who hit for the highest average. They don't give the Cy Young to the pitcher with the best mechanics or the guy who throws the hardest, they give it to the one who was deemed to be most effective. They look at results, not form or effort or attitude or any of the other things that a player brings to the game.
But for the most part this tradition has extended only to hitting and pitching. Today's announcers and analysts make increasing use of modern measures like on-base percentage and inherited runners to shed more light on those areas of the game, but you never hear a television or radio analyst talk about meaningful measures of baserunning, throwing or defense. Instead, they talk about their impressions of the player -- how fast he looks, his quickness, strength and athleticism -- and say simplistic things like "they're the best fielding team in a league because they lead in fielding percentage."
Because we do our own analysis, we sometimes find players whose performance is better or worse than you would guess by watching them a few times a year. And while most of our ratings are consistent with the opinions expressed by baseball's leading writers and TV personalities, sometimes we conclude that a player is actually performing at a higher or lower level than his reputation would suggest.
Because we try very hard to provide the most accurate and realistic baseball simulation available, we can't afford to give in to public opinion and rate someone higher than his performance justifies. If we did that for defensive ratings, we'd have these options:
We don't think it's fair to downgrade teammates so we can give a popular player a better rating than he deserves. And we don't think our customers would want us to disregard the side effects and publish a season disk with players and teams who will overperform. So we do our best to rate players based on their actual performance.
For a few years now, I've wanted to write a little piece about how difficult it is to judge defensive ability, or any baseball skill for that matter, just by watching a lot of games. Then I found an essay by Bill James in his 1977 Baseball Abstract (a self-published book that predated his debut in bookstores by about five years) that says it far, far better than I ever could.
Here are a few excerpts from this wonderful essay, starting with a comment on how differently most people tend to approach the assessment of hitters and fielders:
"While we might not all be able to agree who the greatest-hitting first baseman ever was, the record books will provide us with a reasonably brief list to choose from: Gehrig, Anson, Foxx, Sisler. That's about it. Nobody's going to argue that it was Joe Judge or Moose Skowron, because the record books simply will not permit it . . .
Fielding statistics provide no such limited clarity. Talk about the greatest fielding shortstops ever . . . and the basic argument for everybody is 'One time he made a play where...'
Suppose we turn that same argument back to hitting. Now Moose Skowron hit some baseballs a long way, but nobody is going to say that he was the greatest hitting first baseman ever because 'One time I saw him hit a baseball so far that..." It is understood, about hitters, that the important question is not how spectacularly but how often. Brooks Robinson is known as a great fielding third baseman not because of the number of plays that he makes, but because he looks so good making them. Nobody talks anymore about what a great hitter Jim Northrup was, although to tell you the truth I never saw anybody who looked better at the plate. It is understood that, notwithstanding appearances, he wasn't an especially good hitter. Hitters are judged on results; fielders, on form."
And he talks about the difficulty of trying to judge effectiveness simply by watching:
"One absolutely cannot tell, by watching, the difference between a .300 hitter and a .275 hitter. The difference is one hit every two weeks. It might be that a reporter, seeing every game the team plays, could sense the difference over the course of the year if no records were kept, but I doubt it . . . the difference between a good hitter and an average hitter is simply not visible."
"a fielder's visible fielding range, which is his ability to move to the ball after it is hit, is vastly less important than his invisible fielding range, which is a matter of adjusting his position a step or two before the ball is hit."
In that essay, Bill went on to propose a scoring system that accomplishes essentially what STATS Inc. is doing now -- recording the location of every batted ball so that we could build a record of fielding performances similar to the statistical records that we use to judge batting and pitching performances.
I'm not saying that it doesn't matter whether you watch games or not. I'm just saying that I agree with Bill that it's very difficulty to rate players solely by watching games. We also need useful measures of what they accomplished.
Defensive range is the ability to cover ground and get to more balls than the average fielder, and it's one of the hardest elements of fielding performance to measure.
Official fielding stats provide information such as games played, putouts, assists, errors, double plays, and fielding percentage. But using these numbers to assess player skills is extremely difficult, if not impossible. The list of reasons is very long, but they all boil down to the fact that they don't tell you how many chances to make plays were presented to each fielder.
In 2002, for example, Jose Vidro led the majors in assists by a second baseman. Does this mean he was the best seconde baseman in baseball, or was this just because:
Baseball analysts, ourselves included, have made many attempts to devise methods that deal with some of these other factors so that we can isolate the contribution the player is making. Let's review them, and then talk about some newer methods that we've been using.
In the 1970s, Bill James introduced the idea of range factors to compensate for playing time. A player's range factor is generally computed as successful chances (putouts plus assists) per game. This was a good first step, even though Bill acknowledged at the time that it wasn't meaningful for pitchers, catchers and first basemen.
One thing that frustrated Bill was the fact that not all games played are equal. Some players play almost every inning of their games. Others split the playing time with a platoon partner. Late-inning defensive specialists often pick up a lot of games played without actually playing a lot. For a while, Bill devised methods to estimate how many innings each fielder was actually in the game at his position, but this is very hard to do. Fortunately, companies like STATS have been publishing accurate counts of defensive innings for the last ten years. So we can now compute range factors on a per-nine-innings basis, just like we do for earned run averages.
Using a range factor based on defensive innings, Pokey Reese moves to the top of the list of 2002 second basemen with 5.86 successful chances per nine innings. Vidro drops to seventh.
Whether you use games or innings as the basis of a range factor calculation, there's another critical problem with range factors. By measuring plays made per game or per nine innings, the method takes no account of the length of those innings. Consider the following two innings that start out the same way and feature the same mix of batted balls, only with different results:
In the first version of this inning, the official fielding stats record a putout for the catcher (on the strikeout), one assist (on the inning-ending ground out) and one putout (on the popup) for the third baseman, and one putout (on the grounder) for the first baseman. In the second version of this inning, the official fielding stats are exactly the same. The fact that the defense allowed three more hits in the first one is completely lost.
In this example, there's no way to tell which team defense and which individual fielders were more effective just by looking at the official fielding stats. In the more general case, the best fielders will generally end up making more plays than the poorest defenders. But the number of putouts in a nine-inning game adds up to 27 no matter how many hits are allowed, and the number of assists is mostly a product of the number of ground balls, not the skill of the infielders. So we can't use range factors to evaluate team defense at all, and they don't tell us nearly enough about individual fielders either.
Even if we use defensive innings to measure playing time, we still haven't taken into account (a) the number of opportunities presented to each fielder and (b) the fact that some putouts and assists are harder to come by than others. Back in the 1980s, I developed a new type of range factor that adjusts for many of these variables in the following ways:
Traditional range factors compute plays made per game or per nine innings. This method computes plays made per 100 batted balls, meaning that we can use it to get a better handle on both team and individual defense. If one team gives up a lot more hits than another, it will need more balls in play to get through a game, and the adjusted range factors for the poor fielding team will be lower.
Here's how these factors affected Vidro:
Based on adjusted range factors, Vidro was a little below average among all major-league 2Bs this year, and while we can't finish our assessment of his play without using more advanced methods, we've already seen enough to conclude that his MLB-leading assist total is highly misleading.
This approach produces much better information than does an ordinary range factor, but we're still left with the fact that we're using these adjustments to make an educated guess at how many opportunities each fielder had to make plays. It goes without saying that it's possible to do better when we have access to play-by-play accounts that note the location of every batted ball.
Before moving on, let me take a moment to say that the Fielding Runs numbers in the Total Baseball encyclopedia can be extremely misleading. I don't enjoy saying this, because they were developed by Pete Palmer, and Pete's a friend and one of the nicest guys I've ever met.
The first problem I have with fielding runs is that they're just a glorified range factor, with different weights for different events. Like range factors, you cannot interpret them accurately unless you know the strikeout rate and groundball/flyball ratio of the pitching staff and what percentage of left-handed batters the fielder faced. For a good example of the distortions that often creep into the fielding runs numbers, see the comments on Frank White and Ryne Sandberg in an article I wrote for ESPN.com in September, 1998.
In addition, I don't agree with some of the formulas, mainly because they put too much weight on certain events. For example, the formula for outfielders is .20(PO + 4A - E + 2DP), meaning that catching a fly ball with the bases empty earns you .20 fielding runs, while catching the same fly ball and throwing out a runner for a double play earns you 1.4 fielding runs. In both cases, the fielder made the best play available, but one counts seven times as much as the other. And suppose one center fielder reaches a ball but muffs it for a one-base error, while another lets it go up the gap for a double -- the guy who reached the ball has .20 fielding runs deducted and the second guy isn't penalized at all.
Finally, the fielding runs formula mixes range, errors and throwing into one number, which is appropriate for what Total Baseball is trying to accomplish (an overall player rating), but useless for what we do, which is to assign separate ratings for these skills.
The next logical step beyond range factors is a system that counts actual opportunities to make plays. We weren't able to do that until 1989, because nobody tracked the location of every batted ball until then. The folks at STATS were the first to do it, and they developed the zone rating to take advantage of this new information.
STATS says the "zone rating measures all the balls hit in the area where a fielder can reasonably be expected to record an out, then counts the percentage of outs actually made." Instead of having to estimate the number of opportunities to make plays from defensive innings, percentages of balls in play, the left-right composition of the pitching staff, and the staff groundball/flyball ratio, we can actually count the balls hit to each fielder while they are in the game.
The zone rating could have been a tremendous breakthrough, but we disagree with some of the details of their implementation.
First, they don't count all the balls. For example, no infielder is charged with an opportunity when a grounder is hit down the lines, in the holes, or up the middle. The only plays that go into the zone ratings are the ones where the ball is hit more or less at a fielder. The net result is a system that places more emphasis on good hands than range.
Even if you didn't know this, you could infer from their numbers. The league average zone ratings range from .763 to .885 depending on the position, suggesting that fielders are turning well over 80% of all batted balls into outs. But the truth is that only about 70% of all batted balls become outs. It's clear that the most challenging opportunities, the ones that separate the best fielders from the ordinary ones, are left out of their system.
The second issue is that errors are mixed in with the ability to get to the ball in the first place. Let's suppose a player is credited with 500 opportunties in a season, and let's suppose he was very reliable, making 8 fewer errors than the average player with that many plays to make. Those 8 errors become 8 outs and produce a zone rating that is .016 above the league average. Without taking the errors into account, you might conclude that he has above-average range, when in fact he has average range and very good hands.
The third issue no longer applies but needs to be mentioned. Through the 1999 season, when an infielder started a ground ball double play, STATS credited him with two outs and one opportunity. Starting double plays is an important skill for an infielder, but this approach gives a significant boost to infielders who play behind pitchers who put lots of runners on base and/or with a pivot partner who turns the DP well, and it clouds the effort to measure defensive range. STATS doesn't do this any more, but if you have copies of the STATS Player Profiles books from the 1990s, you'll be looking at zone ratings that double-count these DPs.
Once again, let me say that the idea behind the STATS zone rating is sound and has value even with these issues. If you're looking for an overall measure of fielding performance that includes both range and errors, it won't matter to you that they're lumped together. And folks like us who are interested in separating these skills can make an adjustment for error rates to isolate the range portion.
The zones are smaller than we'd like, but my guess is that STATS did this on purpose to avoid running into two other issues that we'll talk about in a bit. First, some batted balls are playable by more than one fielder, and keeping the zones on the small side reduces the number of opportunities for one fielder to affect his neighbors. Second, outfield zones that cover the entire field make the system more vulnerable to distortions arising from different ballpark dimensions and characteristics. Our zone-oriented analysis does cover the whole field, so we've developed some methods for handling the interaction among fielders and accounting for park effects.
For a few years in the early 1990s, we used a type of zone rating called Defensive Average (DA) . It was developed by Pete DeCoursey and Sherri Nichols and used play-by-play data from The Baseball Workshop. Like the STATS zone rating, defensive average used the same principle of counting batted balls hit into each fielder's zone and counting the number of plays he made. But it covered the whole field and didn't mix apples and oranges by double-counting GDPs. As a result, we felt we got better results from defensive average than from the STATS zone ratings.
When assigning responsibility for balls hit between fielders, the STATS and DA systems are similar if an out is made. Both systems credit the fielder with one opportunity and one play. But things get tricky when the ball falls in for a hit.
If the ball falls into one of the STATS zones, the fielder responsible for that zone is charged with an opportunity. If it falls outside the STATS zones, the play is ignored, and no fielder bears responsibility for the hit.
In the DA system, each player gets charged with half an opportunity when there's a hit that lands between two fielders. That means that someone playing next to a weak fielder tends to look worse than he is, because if the other guy makes the play, there is no opportunity charged, but if the ball falls in, he's charged with half an opportunity even if it's the sort of play the other fielder would be expected to make at least some of the time.
During the years in which we used the Defensive Average system, we were aware of this limitation and did our best to make intelligent adjustments to compensate for it when assigning player ratings. But we always wanted to see if we could do better.
In 1996, we began using a collection of old methods and new tools to expand our look at defensive performance, and we have been refining and improving these methods ever since. We believe that by using these tools to look at player performance from several angles, we can learn a lot more about who accomplished what in a given season.
To one degree or another, our best tools take advantage of the fact that STATS has been recording the type (grounder, fly ball, line drive, popup, bunt) and location (direction and distance) of every batted ball since the late 1980s. Using this information, our analysis programs aren't vulnerable to the potential biases in traditional fielding stats. We know exactly how often each player was in the field, how often the ball was hit near him, and how many plays he made on those balls.
The field is divided into approximately 80 zones. We count the number of balls hit into that zone, the number of times each fielder made an out, and the number of singles, doubles, triples, and errors that resulted. When we're done, we look at the zone data for all of the major leagues and see how often the players at each position were able to make plays on those balls.
For example, on the 6939 grounders up the middle to the shortstop side of the bag during the 2002 season, MLB shortstops turned 64.4% of those balls into outs and made errors 1.9% of the time. Second basemen ranged to the other side of the bag to make the play 0.8% of the time. Almost of the remaining grounders in this zone resulted in singles, with a handful of doubles and fielders choice plays to round things out.
This gives us a baseline that we can use to evaluate performance on balls hit into this zone. Repeating this process for all batted ball types and every zone gives us an overall measure of the playmaking ability of a team and its players.
With one exception, our zone-oriented approach includes the entire field and all types of batted balls. Early on, it became clear that we needed to screen out infield popups because they don't tell us anything. Over 99% of these plays result in an out, so they don't distinguish the good fielders from the not-so-good. And because these plays are easy to make, most popups can be handled by any of several players, making the successful completion of this play as much (or more) a matter of preference than one of skill.
As I mentioned previously, we need to use measures of team defense to help us deal with the interactions among fielders. If one player doesn't get credit for making a play, it may be because another fielder beat him to it, and the first guy shouldn't be punished for playing next to a superior defender. It's only by looking at measures of team defense that we can distinguish the cases where another guy made the play from those when the ball fell for a hit. So let's take a moment to discuss team defense metrics.
We usually start by computing the percentage of batted balls, excluding homers, that were turned into outs by the team. This percentage was labelled the Defense Efficiency Record (DER) by Bill James when he wrote about it in the 1980s, and you can find DER information on the Baseball Prospectus web site during the season.
I'm not completely sold on DER as the ultimate measure of team defense, however. For one thing, I've always been troubled by the fact that it's just a variation on batting average, with strikeouts and homeruns removed, and with the focus on the out percentage instead of the hit percentage. But league batting averages have ranged from a low in the .230s to a high in the .300s in the past 80 years, so they don't just measure batting skill. They also embody the impact of the rules of the game (strike zone, mound height), the equipment (dead ball, lively ball, juiced ball), and the changing nature of ballparks. Similarly, the league DER figures have risen and fallen by large amounts, indicating that factors other than fielding skill are built into these numbers, too.
A second question about DER is the extent to which it measures pitching versus fielding. I've always believed that DER measures some of both. There is a strong (but not perfect) correlation between a team's rankings in ERA and DER, suggesting that (a) good pitchers make their fielders look better and/or (b) the team's rank in ERA is in large part due to the quality of its defense. It's hard to know which way to look at it, but I believe it works in both directions.
Recent work by Voros McCracken and Dick Cramer suggests that pitchers have little or nothing to do with the percentage of balls in play that are turned into outs. To put it another way, the defense is entirely responsible for a team's DER ranking. I'm not ready to accept that pitchers have nothing to with these outcomes. While I haven't had time to do any detailed studies in this area, some very preliminary work suggests that good pitchers do improve a team's DER, though only by a few points. But because pitchers allow a very large number of batted balls over the course of a season, these small improvements can have a large effect on the pitcher's ERA.
Another issue with DER is that park effects can play a large role. It's clear that the enormous impact that Coors Field has on scoring isn't entirely due to homeruns. A much higher percentage of balls that stay in the field of play are falling in for hits, too, and that makes Colorado's team defense look much worse than it really is. This is the most extreme example, of course, but there are other parks that make a difference.
In other words, we start our process by computing the DER for each team, but we don't take that figure as a precise measure of the team's ability to make plays in the field. We keep the potential distortions in mind as we go through our rating process.
Our zone-oriented analysis provides us with another way of rating team defenses. We can go zone by zone and compute how many more (or fewer) plays were made by this team than the average team, then do a weighted average of all of the zones to get an overall score for the team. That overall score is expressed as the number of plays made above or below the average. In 2002, for example, Anaheim's defense led the majors by making 120 more plays than the average team (in 4228 opportunties). These figures are not park adjusted, so they're not definitive, but they definitely add value in the process.
To isolate portions of a team's defense, we rate the infields by computing the percentage of ground balls turned into outs and the outfields based on the percentage of fly balls and line drives that were caught.
Because we use a collection of overall measures (like DER), mid-level measures (such as out rates on grounders), and detailed zone-based analysis, we can examine team defense at several levels of detail. That helps us determine which fielders are getting the job done and which are letting the team down.
We can't leave the subject of team defense without looking more closely at the parks.
We mentioned Coors Field a moment ago, but Dodger Stadium is another good example. From 2000 to 2002, that park depressed batting averages by 21 points, making it one of the best pitchers' parks in the game. And it wasn't just because of strikeouts and homers, either. Focusing only on balls hit into the field of play, Dodger Stadium took away 97 hits a year in that period. If half of them came with the Dodgers on defense, measures that ignore park effects (like DER) make LA's team defense appear to be 48 plays better than it really is.
Using play-by-play data, we can also compare the hit rate on different types of batted balls. Dodger Stadium dramatically reduces the percentage of ground balls that go for hits. It also cuts the hit rate on fly balls, but not by a whole lot. Because virtually all of the park's effect is concentrated in the infield, it would be especially easy to overrate the LA infield if we ignored this information.
Most of our work at the player level uses zone-based data. We compare the rate at which each fielder turned batted balls into outs in each zone with the overall averages. If a player made more than the normal number of plays, he gets a plus score for that zone. If he fell short of the overall average, he gets a minus score. By computing a weighted average of all of his zones, we get a figure that tells us how many more (or fewer) plays he made than the average defender. We call this figure "net plays".
In a typical season, the top fielders at each position make 25-30 more plays than the average. Exceptional fielders have posted marks as high as 40-60 net plays, but those are fairly uncommon. Recent examples include Darin Erstad in 2002, Scott Rolen just about every year, and Andruw Jones in his better seasons. The worst fielders tend to be in the minus 25-40 range.
As a reality check, we look at other measures like range factors, adjusted range factors, STATS zone ratings, and our own version of the STATS zone ratings (with larger zones). More often than not, these measures tell similar stories. When they disagree, we look for external factors that might be skewing those other measures. In the end, we put the most weight on our net plays analysis.
But the net plays figures are starting points, not the final answer, because we have several other things to consider before we assign a rating. We've already talked about park effects, so I won't dwell on that any more.
As with the STATS zone ratings, our net plays analysis can be influenced by error rates. So we always look to see whether a fielder is making more plays mainly because he has better hands. Mike Bordick and Alex Rodriguez are two good examples from the 2002 season. In some cases, a player will have a mediocre net plays figure because he made a lot of errors, and we may bump up his range rating to account for the fact that he's getting to more balls in the first place.
For infielders, we have another analysis program that measures their ability to start double plays and get force outs when such opportunities exist. Especially for corner infielders, the ability to make the tough plays can separate the men from the boys. If a first baseman always takes the ball to the bag and doesn't start his share of double plays and force plays, he's not helping the team, even if he does record a normal number of outs.
For middle infielders, we also look at how often they are able to make the pivot on the double play. This is an important part of the second baseman's job, and he can make up for ordinary range by turning two more often. It isn't talked about very often, but we also see differences in the ability of shortstops to complete these plays.
For shortstops, we look at the zone data to see if their net plays score has been artificially depressed by sharing the left side of the infield with an especially talented third baseman. For example, Scott Rolen is way above average on balls to his left, and that cuts down on the number of plays his shortstops can make. If the overall team defense in that zone is still very good, there's no reason to penalize the shortstop. Similarly, we look for first basemen who are taking plays away from the man at second. By looking at the zone data for individual fielders and for the team as a whole, we can tell whether plays not made by one fielder are getting made by someone else.
The same is true in the outfield. For balls hit in the gaps, we look at the zone data to see if an exceptional fielder might be taking plays away from his neighbors.
Another of our analysis programs counts the number of times a player is used as a defensive sub or is removed for a defensive sub. This information doesn't tell us anything about performance, of course, but it is very helpful to know that one fielder was regarded by his manager as being superior to another.
Like many of you, we read a lot, we watch games on local TV and satellite and the highlight shows on ESPN and Fox, because it helps to have an image of a player when we evaluate the performance data. And we compile an extensive database of player notes, so we know who's coming off a knee injury or a shoulder problem that might have affected their ability to make plays.
And when the evidence doesn't match the player's reputation, we double-check our work and look very, very hard for the reasons why. Whenever possible, we talk to people -- local writers, broadcasters and sophisticated fans -- who have seen the player quite a bit to see if we can gain some additional insight into each player's performance.
After rating all of the players, we go back and double-check these individual ratings to see if they add up to something resembling the team's park-adjusted defensive performance. If not, we go back over everything we know about those players and keep at it until it makes sense.
In his recent book called Win Shares (published by STATS in 2002), Bill James developed a method for apportioning each team's wins to the players who were most responsible for creating them. A big part of that method involves evaluating defense at both the team and individual level. We're still in the process of evaluating this new approach, but we can point out a few things that you might want to keep in mind as you ponder the role that system should have in evaluating players:
The bottom line is that we will continue to rate fielders for modern seasons based on our analysis of play-by-play data. But we're always on the lookout for new and better ways to evaluate fielders, and if our review suggests that the fielding portion of the Win Shares model provides us with some new tools, we'll use them.
We know that a lot of our customers like our products precisely because we do our own analysis instead of rating everyone based on prevailing opinions. At the same time, we know that there are other people who don't buy our products because Tim McCarver says that someone is a brilliant fielder, and because McCarver is a well-known TV analyst and ex-player, he must therefore know a lot more about this stuff than we do.
Let's suppose, for the sake of argument, that we wanted to ditch all of our analysis and rate players based upon what we read and hear from the media. That's a lot harder to do than you might think, for a whole host of reasons.
When someone in the media says "he's the best second baseman in baseball," it's not always clear what it means. It could mean he's the best overall player at his position (including hitting, running, etc.). It could mean he has great hands. It could mean he turns the double play well or that he has great range. Even if it means all of these things to some degree, an overall evaluation doesn't help us. We have separate ratings for separate skills, and we need objective evaluations of each skill.
The media doesn't talk about all the players. We have 1200+ players to rate each year, and only a fraction of them are regularly discussed. Some players may be overrated because they play for teams in media-intensive cities or teams that got a lot of exposure in the playoffs, while good players on small-market teams may be overlooked.
It often seems as if it takes a year or two for someone's reputation to catch up with a change in his performance, for better or worse. In the 15+ years we've been rating players, we've often identified someone who has been making a lot of plays without getting noticed. It's not unusual to see that player start to win Gold Gloves two years later. And then keep winning Gold Gloves for a few years after their performance no longer merits them.
Managers and general managers make public comments about players all the time, but their remarks can be influenced by the needs of the team. Sometimes it's to their advantage to talk about players in certain ways, whether it's to hype someone for marketing purposes or to talk them down in a salary squabble. It's hard to tell when we can take a comment at face value and when we need to discount it because of a hidden agenda.
I'd love to incorporate the opinions of professional baseball scouts because they are trained to see things that other people don't see. But it's difficult to find a collection of scouts who have seen every player and can make their evaluations available to people outside the organizations they work for.
We could base our judgments on how often someone shows up on SportsCenter. But the photogenic play isn't always the best play. The exact same fly ball might produce a routine play for a great fielder, a diving catch for the average fielder, or a single for the poor fielder. The diving catch is the only one that makes the highlight films. The majority of highlight-film plays are made at the edge of the fielder's effective range, whatever that range happens to be.
(A few years ago, I saw a game in Baltimore in which the right fielder broke back on a line drive, realized it wasn't hit that hard, reversed course and recovered in time to make a nice shoestring catch. What should have been a very easy play wound up being shown dozens of times as CNN's Play of the Day.)
We could place a lot of weight on the Gold Glove voting. Putting aside the question of how well the voters do that job, there are still several obstacles. They don't announce the voting, so we have no idea who came second or how close the vote may have been. And even if we were to accept all Gold Glovers as top fielders, we can't award them all our top range rating because Gold Gloves are given for overall fielding performance, and we have to rate players separately for range, throwing, and avoiding errors. For some Gold Glovers, the most accurate way to rate them would be to assign an excellent throwing rating, a very low error rate, and an average range rating.
We do our very best to rate players based on performance, not reputation. To that end, we license play-by-play data and spend a lot of time developing new ways to analyze that information and interpreting that information in light of everything we know about that player's performance. The phrase "everything we know" includes our own analysis of team and player fielding skill, other measures like range factors and STATS zone ratings, injury reports, park effects, plus what we see and hear and read as we follow baseball on a daily basis. We hope you like the results as much as we enjoy doing this work.
]]>By Tom Tippett
December 5, 2002
It goes without saying that wins and losses are the most important things to consider when judging a team's performance. They are, after all, what the game is all about and what determines who gets to keep playing until there's only one winner left.
The next most important things are runs scored and runs allowed. You win games by outscoring your opponents, so the connection between runs and wins is very strong. It's not perfect, though, and every season produces a few teams that win more or less than you'd expect given their run differential.
If runs are one step removed from wins, then the baseball events that produce runs are two steps removed from wins. You score runs by putting together singles and walks and doubles and steals and homers, and you prevent runs by holding the other team to a minimum of those things.
In most cases, there's a very direct relationship between wins and runs and the underlying events that produce runs. But that's not always the case, and in this review of the 2002 season, we'll identify teams where those relationships didn't hold up. If the past is any guide, this will give us some very strong hints about what is likely to happen with those teams in the future.
To explore the relationship between runs and wins, we'll use the pythagorean method that was developed by Bill James. To explore the relationship between offensive events and runs, I'll introduce a new statistic that I'll call the run efficiency average. This number will tell us which teams were unusually good at turning offensive events into runs and unusually good at keeping the other team from doing the same.
We'll end up with three measures for each team -- one for offensive efficiency, one for defensive efficiency, and one for pythagorean efficiency -- that will tell us which teams squeezed more wins out of the hits and walks and homers and other events that occurred during their games. And which teams squandered their output to the greatest degree.
And we'll take a look at some history. We'll see that teams that are unusually efficient (or ineffecient) have exhibited a very strong tendency to revert back to the norm the next year. In other words, if your team was especially inefficient in 2002, there is every reason to believe things will be better next year. And the opposite is true, too. If your team was very efficient this year, don't count on a repeat performance next year.
That's good news for the Cubs, Brewers, Devil Rays and Tigers. And bad news for the Angels, Braves, and Twins. It's way too early to start predicting what's going to happen in 2003, and all thirty teams are quite capable of improving or regressing based on their off-season moves and the development of their younger players and prospects. But we can say that these seven teams (and a few others to a lesser degree) go into the offseason in better or worse condition than it might seem based solely on their 2002 win-loss records.
Others, notably Rob Neyer and the Baseball Prospectus crew, have written extensively on ESPN.com about the Bill James pythagorean method, a well-established formula that says that a team's winning percentage is tightly coupled with runs scored and runs allowed. The expanded standings on ESPN.com include run margins and expected win-loss records that are derived using this formula, and Rob's home page showed pythagorean standings every day. So I'm not going to go over that ground again.
I will, however, try to put the 2002 results into historical context. For instance, the Red Sox and Cubs won 8 fewer games than their run margin would normally produce, while three teams (Minnesota +7, Oakland +6, and Detroit +6) won at least six more than expected. How unusual is this? And what tends to happen to teams that stray from their expected win totals?
I started by computing the expected and actual win totals for every team since 1962, the first year the 162-game schedule was used in both leagues. The more games you play, the larger the differences between expected and actual wins, so I didn't want to mix seasons with different schedules. For that reason, I left out the strike-shortened 1972, 1981, 1994, and 1995 campaigns, leaving a total of 72 league-seasons.
In those 72 league-seasons, the average team "missed" its pythagorean projection by only 3.2 games, indicating that there is indeed a very strong relationship between runs and wins. How does 2002 compare? This year, AL teams were off by an average of 4.1 games while their NL counterparts missed by an average of 3.0 games. Overall, the 30 teams had an average difference of 3.5 games, slightly higher than the historical average but well within the normal year-to-year fluctuations.
The wackiest season, in the pythagorean sense, was the 1978 National League, whose teams missed their projections by an average of 5.3 games. In the NL West that year, the Dodgers led the league in both scoring and fewest runs allowed, outscoring their opponents by 154 runs, while the Reds were only +22 on run differential. But Cincinnati won nine more games than expected and the Dodgers five fewer, turning what could have been a runaway win by the Dodgers into a close battle that saw LA win by 2-1/2 games. In the NL East, the story was much the same, as the Phillies (-6) edged the Pirates (+2) by two games in a race that was much closer than it could have been. That same year, the Expos matched the Reds with a run margin of +22, but Cincinnati (+9) won 92 games and Montreal (-8) only 76. (The Expos run margin foreshadowed their improvement; they went on to win 95 games in 1979.)
In contrast, the 1991 American League was closest to pythagorean form, with an average difference of only 1.8 wins.
What tends to happen to teams with large pythagorean differences? Here's a list of the 22 teams that have exceeded their projected win total by at least 8 games, along with their differences in the next year:
Team Diff Next --------------- ---- ---- 1974 Padres +12 + 7 1984 Mets +12 + 1 1970 Reds +11 - 3 1963 Astros +10 + 4 1997 Giants +10 - 3 1998 Royals +10 -11 1970 Phillies + 9 + 3 1977 Orioles + 9 + 6 1978 Reds + 9 - 1 1978 Athletics + 9 + 4 2001 Mets + 9 - 4 1971 Braves + 8 + 5 1973 Tigers + 8 + 8 1974 Tigers + 8 + 2 1976 Cubs + 8 + 5 1977 Mariners + 8 0 1979 Astros + 8 + 5 1982 Giants + 8 - 1 1987 Expos + 8 - 5 1989 Astros + 8 + 5 1992 Astros + 8 - 6 1997 Reds + 8 - 3
As you can see, only a few teams came close to matching their pythagorean differences in the next season. In fact, these 22 teams were collectively 18 wins above their projection the year after, an average of less than one win per team.
(Just to be clear, these next-year numbers don't represent the change in actual win-loss record from the year before, so they don't measure the team's tendency to get better or worse. They represent the difference between actual and pythagorean wins the next season. In other words, they measure the tendency to consistently win more or fewer games than the run margin suggests, not the tendency to produce a better or worse run margin in the first place.)
On the flip side, here are the teams with the biggest negative differences since 1962:
Team Diff Next --------------- ---- ---- 1962 Mets - 8 + 3 1968 Pirates - 8 - 2 1974 Angels - 8 + 3 1975 Dodgers - 8 + 2 1978 Expos - 8 0 1983 Cubs - 8 + 4 1983 Rangers - 8 - 5 1985 Indians - 8 + 4 1986 Giants - 8 - 4 2000 Astros - 8 + 4 1962 Cardinals - 9 - 2 1963 Twins - 9 - 9 1964 Twins - 9 + 1 1966 Yankees - 9 + 5 1974 Athletics - 9 - 1 1975 Yankees - 9 - 1 1980 Brewers - 9 + 4 1984 Astros - 9 0 1985 Red Sox - 9 + 4 1990 Mets - 9 - 3 1980 Cardinals -10 + 3 (103 games in 1981) 1993 Padres -10 - 5 1997 Astros -10 - 6 2001 Rockies -10 + 4 1970 Cubs -10 + 3 1975 Astros -11 + 3 1999 Royals -11 + 1 1967 Orioles -12 - 2 1984 Pirates -13 - 6 1986 Pirates -13 + 1 1993 Mets -14 + 1
These 31 teams were collectively 4 wins above their projection the year after, about as close to zero wins per team as you can get.
These extreme teams do leave us with a few unanswered questions. Why are there more NL teams than AL teams on these lists? Why do the Astros show up as often as they do? How miserable must the mid-1980s Pirates fans have been when their team posted a three-year pythagorean difference of -32 wins from 1984 to 1986? These answers, if they exist, will have to wait for another day.
I'm not going to suggest that I have proven this beyond a reasonable doubt, but I believe luck plays a large part. If you wanted to argue that the over-achievers had big pluses because their manager was especially astute or their roster was full of clutch players, it would be a tough case to make based on the next-year records of these teams. And looking at the under-achievers, it would be even tougher to argue that their manager and players are fundamentally flawed based on their next-year results.
But teams change from year to year, and the under-achievers are much more likely to fire their managers and turn over half their rosters. Perhaps those changes were responsible for bringing them back to pythagorean normalcy. Even though I don't believe this argument would hold up under closer examination, it muddies the water a little.
Still, if managerial skill and clutch performance were the biggest piece of this puzzle, why wouldn't the over-achievers, the teams that would not be making many changes from year to year, be able to maintain their performance to a much greater extent?
In the previous section, we took one step back from wins and losses to examine runs. In this section, we'll take another step back and look at the offensive events -- the hits and walks that lead to the runs that generate the wins -- that were produced and allowed by each team.
Just as there is a strong relationship between runs and wins, it's almost always true that the more hits and walks you produce, the more runs you'll score. Sometimes a productive team comes up short on the scoreboard because they didn't hit in the clutch or just because they happened to hit line drives right at people in key situations. Or the opposite could be true. But this relationship holds up most of the time.
To shed some light on this relationship, we need a way to take batting stats and turn them into a measure of overall offensive production. There are several good options here, including Runs Created (Bill James), Batting Runs (Pete Palmer), Equivalent Average (Clay Davenport), and OPS (on-base average plus slugging average). But many of them require a computer, and although we do computer analysis all the time, we also like to use simpler measures that anyone can use whenever they have a page of stats in front of them. The best of these simple methods give up very little accuracy in return for a big gain in usability.
For this exercise, I'll use the sum of total bases and walks, or TBW for short. TBW is not a perfect measure, but it does have a few things going for it. It captures the most important things a team does to produce runs -- singles, extra-base hits, and walks. It's easy to figure without a computer. In the past, I've used both TBW and OPS for this type of analysis, and the results are almost exactly the same, so the accuracy is more than acceptable.
And sometimes it just seems to tell a story more clearly. For instance, the 2002 Yankees had a team OPS of .809 compared to the .769 mark of the Mariners. Even though I've been working with OPS figures for a number of years, I still need to stop and think about what a 40-point advantage means. But if you tell me that the Yankees produced 224 more total bases and walks than the Mariners, that's something I can grasp right away.
The following table shows the offensive and defensive TBW figures for the American League, along with the difference between these two figures and each team's league rank based on those differences. It also shows runs for and against, the run differential, and the rankings based on run differential. Finally, because we're trying to trace a path from TBW to runs to wins, it also lists the team's win total for the year.
---------- TBW ---------- ------- Runs -------- AL Off Def Diff Rank Off Def Diff Rank W NY 3187 2629 +558 1 897 697 +200 2 103 Bos 3050 2532 +518 2 859 665 +194 3 93 Tor 2921 2995 - 74 8 813 828 - 15 8 78 Bal 2665 2981 -316 11 667 773 -106 11 67 Tam 2640 3219 -579 13 673 918 -245 13 55 Min 2911 2789 +122 7 768 712 + 56 7 94 Chi 3028 2839 +189 6 856 798 + 58 6 81 Cle 2774 2930 -156 10 739 837 - 98 10 74 KC 2728 3189 -461 12 737 891 -154 12 62 Det 2414 3006 -592 14 575 864 -289 14 55 Oak 3009 2595 +414 3 800 654 +146 4 103 Ana 2918 2647 +271 4 851 644 +207 1 99 Sea 2963 2710 +253 5 814 699 +115 5 93 Tex 3112 3205 - 93 9 843 882 - 39 9 72
As you can see, the team rankings using TBW and those using run differentials are very similar. In fact, they're identical except for Anaheim's move from fourth in TBW to first in run margin. The Angels were very efficient on both sides of the ball, finishing 4th in scoring (and only 8 runs out of second) despite trailing six other teams in TBW, and leading the league in fewest runs allowed even though three other teams gave up fewer TBW. (That efficiency didn't carry over to the relationship of runs to wins, however, as they led the league in run margin but were only third in wins.)
In terms of raw production, the Red Sox nearly matched the Yankees, but still managed to come up ten short in the win column. (The same thing happened in 2001.) This comes as no surprise to the long-suffering Boston fans or the incredibly smug New Yorkers who just knew the Sox would find a way to lose despite all their talent.
It's interesting to note that the White Sox were a match for the Twins in production even though Minnesota ran away with the division. For all the talk about the Twins superior pitching and defense and the problems the White Sox had in those areas, Chicago gave up only 50 more TBW, roughly one base every three games.
And we see yet another example of how strong the AL West was this year, with three teams in the league's top five in TBW and run differentials and the Rangers only a little below the league average. Oakland was a clear winner in TBW but trailed the amazing Angels in run margin, and it took an excellent 32-14 record in one-run games to keep the A's in first place.
Let's take a quick look at the National League before pausing to put these TBW numbers in historical context.
---------- TBW ---------- ------- Runs -------- NL Off Def Diff Rank Off Def Diff Rank W Atl 2808 2529 +279 3 708 565 +143 3 101 Mon 2865 2857 + 8 9 735 718 + 17 7 83 Phi 2970 2769 +201 5 710 724 - 14 9 80 Flo 2810 2927 -117 12 699 763 - 64 11 79 NY 2657 2758 -101 10 690 703 - 13 8 75 StL 2879 2630 +249 4 787 648 +139 4 97 Hou 2886 2793 + 93 6 749 695 + 54 6 84 Cin 2815 2928 -113 11 709 774 - 65 12 78 Pit 2566 2860 -294 13 641 730 - 89 13 72 Chi 2853 2814 + 39 8 706 759 - 53 10 67 Mil 2613 3086 -473 16 627 821 -194 16 56 Ari 2974 2608 +366 2 819 674 +145 2 98 SF 3045 2524 +521 1 783 616 +167 1 95 LA 2701 2653 + 48 7 713 643 + 70 5 92 Col 2826 3195 -369 14 778 898 -120 14 73 SD 2649 3040 -391 15 662 815 -153 15 66
While it's clear that Atlanta was the division's top team, their TBW differential wasn't much better than that of the Phillies, who somehow managed to turn a big edge in raw production into a negative run differential and a losing season. Most of the problem was on offense, where the Phils were 3rd in TBW but only 8th in runs scored. (Before the season, our computer simulations had the Phillies finishing a close second behind the Braves. In the real season, they were a very close second statistically, but that didn't translate into the things that really matter, runs and wins.)
The biggest surprise in the Central division was the Cubs. In fact, most of what I just wrote about Philly applies here, too. Our preseason simulations put Chicago third with a .500 record, and the real Cubs put up TBW numbers that were entirely consistent with being a .500 team. But they ranked a few places lower in runs than in TBW on both sides of the ball and they couldn't win the close games (18-36 in contests decided by one run). By the way, the 2001 Cubs were the division's best team statistically (+175 TBW) but failed to win the pennant; with two straight seasons like this, it's no surprise that a managerial change was made, regardless of whether the manager was to blame.
In the West, San Francisco outproduced Arizona but came up a little short in the standings. Both teams were very strong across the board, however, and the Giants showed during the second season that they really were the best team in the league. Statistically speaking, Los Angeles was much closer to a .500 team than their 92-70 record suggests. In fact, the Dodgers were the anti-Phillies, turning a 12th-place ranking in offensive TBW into a 7th-place finish in scoring. (Warning to LA fans: the Padres had the most efficient offense in baseball in 2001 -- 13th in OPS, 6th in runs -- and look what happened to them in 2002.)
I've been putting these tables together for a few years now, and I can tell you that TBW differentials are usually in the plus or minus 300 range. With eight teams more than 400 from the midpoint this year, I wondered how these figures stacked up against other teams from the past. Thanks to Retrosheet's database of play-by-play accounts, I ran the numbers for all seasons back to 1974. (It would be nice to go back further, but the official stats don't include doubles and triples allowed by pitchers.)
Here are the top twenty teams from that 29-year period:
Team Net TBW Comment ---------------- ------- -------------------------------- 1998 Braves + 664 1998 Yankees + 662 Won WS 2001 Mariners + 603 1997 Braves + 568 2002 Yankees + 558 1976 Reds + 550 Won WS 1995 Indians + 536 144-game schedule (+603 per 162) 2001 Athletics + 534 1984 Tigers + 530 Won WS 1988 Mets + 524 2002 Giants + 521 2002 Red Sox + 518 Failed to qualify for postseason 1974 Dodgers + 509 1978 Brewers + 493 1996 Indians + 471 1986 Mets + 466 Won WS 1999 Yankees + 454 Won WS 1996 Braves + 449 1977 Dodgers + 448 1979 Orioles + 448
Notes: Only one of these strong teams, the 2002 Red Sox, failed to make the postseason ... only five won the World Series, a reminder that surviving the expanded postseason format is very tough ... could have been a great 1998 World Series if the Braves hadn't lost to the Padres ... the 1978 Brewers also had the best run margin in the AL that year, so this could have been one of the great three-way pennant races in history.
And the bottom twenty teams:
Team Net TBW Comment ---------------- ------- -------------------------------- 1996 Tigers - 727 1979 Athletics - 683 1998 Marlins - 676 Defending WS champs 1974 Padres - 630 Expansion 2002 Tigers - 592 1978 Blue Jays - 590 Expansion 1979 Blue Jays - 586 Expansion 2002 Devil Rays - 579 Expansion 1977 Braves - 539 1977 Mariners - 528 Expansion 1980 Mariners - 522 Expansion 1977 Blue Jays - 507 Expansion 1993 Rockies - 487 Expansion 1978 Mariners - 486 Expansion 1999 Twins - 484 2001 Devil Rays - 475 Expansion 2002 Brewers - 473 2002 Royals - 461 1989 Tigers - 447 1982 Athletics - 443
Notes: Eleven of these twenty teams were expansion franchises in the first seven years of their existence ... four 2002 teams made this list ... maybe two rounds of expansion since 1993 is the reason so many recent teams made both lists ... Billy Martin took over as manager of the A's after their disastrous 1979 season, led them to a winning record in both 1980 and 1981, then made this list again in 1982 before being fired ... the Twins have come a long way since 1999 ... in 1982, Bill James wrote that the Blue Jays might be the worst expansion team in history, but they got better in a hurry after that, so maybe there's reason for Devil Rays fans to have some hope as their young prospects move up.
Earlier in this article, when discussing the relationship between runs and wins, we saw that teams sometimes win quite a few more or less games than their run margin would normally produce. And that those differences don't tend to repeat the next year. It's very rare for a team to over-achieve (or fall short) two years in a row, and there's a very strong tendency to revert to a normal runs-to-wins relationship. Is this also true of TBW and runs?
To identify teams with particularly efficient or inefficient offenses, ones that produce more or less than the expected number of runs given the TBW they produced, I divided runs by TBW to get something I'll call the run efficiency average (REA). As you can see in the following chart, which plots TBW versus runs scored for every full team season since 1974, there's a very strong straight-line relationship between TBW and runs. In other words, we can predict runs scored from TBW with a high degree of accuracy.
It turns out that run efficiency averages look an awful lot like team batting averages. From 1974 to 2002, team batting averages ranged from a low of .229 to a high of .294 with a midpoint of .261. Baseball fans know from experience that a team batting average of .280 or higher is very good, and one below the .245 mark is woeful.
In this time period, run efficiency averages have ranged from .225 to .305 with a midpoint of .264. The midpoint and the spread are slightly higher than for team batting averages, but the benchmarks are basically the same. Anything over .280 indicates a very efficient offense, while anything under .245 indicates a team that squandered a lot of its chances.
Like team batting averages, run efficiency averages tend to be higher in the American League (because pitchers don't bat) and rise and fall by a few points from season to season. (They also appear to be higher in good hitters parks, but I'm leaving park effects out of the equation for the time being. I'll be looking at both offense and defense, so the park effects should cancel out when we subtract one from the other.)
So the best way to evaluate teams is to compare their run efficiency averages to the norm for their league that season and to rank them based on those differences. Here are the offenses that were most efficient in the 1974 to 2002 period, relative to their leagues, and what they did the following year:
Team REA Diff Next ------------------ ---- ------ ------ 2000 Rockies .305 + .035 + .019 1996 Rockies .303 + .032 + .020 1977 Twins .298 + .032 - .010 1987 Cardinals .293 + .030 - .008 1975 Reds .287 + .029 + .019 1985 Cardinals .281 + .026 + .002 1982 Brewers .288 + .026 + .005 1992 Brewers .287 + .025 + .008 1976 Phillies .282 + .025 + .017 2000 White Sox .301 + .025 - .003 2001 Mariners .295 + .024 + .005 1999 Indians .299 + .024 + .006 1998 Yankees .297 + .023 + .003 1974 Dodgers .283 + .023 - .008 1993 Rangers .292 + .022 + .003 1991 Brewers .287 + .022 + .025 2000 Royals .299 + .022 - .002 1981 Brewers .280 + .022 + .026 1981 Phillies .273 + .021 + .001 2002 Angels .292 + .021 ???
A majority of these teams were above average again the next year, but all but one made a move back toward the middle of the pack. On average, they lost 19 points relative to the league. On a base of 2800 TBW, that's a loss of 53 runs, enough to cost a team five to six wins.
Here are the least efficient offenses of this period:
Team REA Diff Next ------------------ ---- ------ ------ 1988 Orioles .226 - .038 + .003 1998 Devil Rays .238 - .036 - .003 2002 Tigers .238 - .032 ??? 1983 Mariners .234 - .032 - .005 1982 Reds .228 - .031 - .007 1981 Blue Jays .227 - .031 - .006 1974 Padres .230 - .029 - .020 1978 Athletics .231 - .028 - .026 1981 Mets .225 - .027 - .004 1996 Phillies .244 - .027 - .010 1978 Athletics .246 - .026 - .005 1985 Rangers .240 - .026 + .002 1993 Marlins .243 - .025 - .019 1976 Expos .233 - .025 - .019 1980 Blue Jays .242 - .024 - .031 2001 Mets .242 - .023 + .003 1996 Angels .256 - .023 + .011 1980 White Sox .243 - .023 + .019 1995 Blue Jays .252 - .023 - .013 1989 Indians .242 - .022 + .021
Again, all but one team moved up the next year, with an average improvement of 22 points. It's abundantly clear that extreme REA values don't repeat themselves; no matter what the environment, and no matter how good or bad the team, the REA tends to make a big move toward the norm the next year.
This is good news for teams that were the most inefficient this year. Detroit, Baltimore, Philadelphia, Milwaukee, and Tampa Bay can expect to improve their efficiency in 2003. Of course, it's bad news for this year's over-achievers, namely Anaheim, Arizona, Colorado, St. Louis, and the White Sox.
I won't take the space to show top-20 lists for pitching efficiency, but I can tell you that the same pattern held on the other side of the ball. The twenty most efficient pitching staffs moved an average of 21 points toward the norm the next year, while the least efficient improved by 22 points. Not a single team on either list moved further away.
The five most efficient pitching staffs this year, and the five most likely to struggle to match that performance, were Atlanta (which had the lowest run efficiency average in this 29-year period), Anaheim, Oakland, Minnesota, and Los Angeles. On the other hand, improvement is bound to be in store for Colorado, Detroit, Cleveland, Tampa Bay, and the Cubs.
By the way, Detroit was very inefficient on both offense and defense this year, and while their park might have something to do with that, I don't think it's a major factor. If the Tigers move 20 points toward the norm on both sides of the ball, they're looking at a favorable swing of 108 runs, or about 11 wins, even if nothing else changes. (Of course, moving from 54 wins to 65 wins isn't anything to write home about. They need to do even better than that.)
In contrast, Anaheim was on both top-five lists for 2002, and they stand to move back toward the pack offensively and defensively in 2003. That could take a 99-win team and bring them back to the high 80s.
Let's try to wrap all of this up into one neat package. We started by showing that runs scored and runs allowed are an accurate predictor of wins and losses. Teams that deviate from this prediction usually revert to form the next year.
Then we showed that offensive production (as measured by total bases plus walks) is an accurate predictor of runs scored. Likewise for defensive production and runs allowed. For both offense and defense, teams that deviate from the predicted number of runs tend to move significantly toward the norm the next year.
In other words, these three forms of efficiency -- which I'll call pythagorean efficiency (turning runs into wins), offensive efficiency (turning TBW into runs scored), and defensive efficiency (limiting runs allowed per TBW allowed) -- can have a major impact on the standings in any one season. But that effect isn't likely to carry over to the next year.
Pythagorean efficiency is already expressed in wins and losses. I'll translate offensive and defensive efficiency into wins by taking the surplus or deficit in runs and dividing by nine. Why nine? According to the pythagorean method, that's the number of runs it takes to add one win in a league where the average team scores about 750 runs. By converting all three types of efficiency to wins, we can add them up to see which teams gained or lost the most due to efficiency in 2002. Here are the figures for all thirty teams:
Actual ----- Efficiency ----- Adj Wins Pyth Off Def Tot Win New York 103 +3 +4 +1 + 8 95 Boston 93 -8 +4 +2 - 2 95 Toronto 78 -2 +3 -2 - 1 79 Baltimore 66 -2 -6 +4 - 4 70 Tampa Bay 55 -1 -4 -5 -10 65 Minnesota 94 +7 -2 +5 +10 84 Chicago 81 -6 +4 -3 - 5 86 Cleveland 74 +3 -1 -5 - 3 77 Kansas City 62 -4 0 -3 - 7 69 Detroit 55 +6 -9 -6 - 9 64 Oakland 103 +6 -2 +5 + 9 94 Anaheim 99 -4 +7 +8 +11 88 Seattle 93 0 +2 +4 + 6 87 Texas 72 -5 0 -2 - 7 79 Atlanta 101 +3 -1 +9 +11 90 Montreal 83 0 0 +2 + 2 81 Philadelphia 80 +1 -6 -2 - 7 87 Florida 79 +5 -2 -1 + 2 77 New York 75 -4 +1 0 - 3 78 St. Louis 97 0 +5 +3 + 8 89 Houston 84 -3 +1 +2 0 84 Cincinnati 78 +4 -1 -3 0 78 Pittsburgh 72 +2 -2 0 0 72 Chicago 67 -8 -3 -4 -15 82 Milwaukee 56 -4 -5 -3 -12 68 Arizona 98 +1 +6 -1 + 6 92 San Francisco 95 -4 0 +3 - 1 96 Los Angeles 92 +3 +2 +4 + 9 83 Colorado 73 +4 +6 -9 + 1 72 San Diego 66 +2 -2 -4 - 4 70
Let's work through a few examples to make sure it's clear what we're trying to say with this table:
Before leaving this topic, I want to emphasize that I'm not trying to diminish what the Angels accomplished this year by pointing out that their offensive and defensive stats are more consistent with those of an 88-win team. They did win 99 games in a very tough division by doing all the little things that count: putting the ball in play so even their outs were able to move runners over, hitting in the clutch, playing great defense, getting key outs when they needed them, and so on. They did all that again in the post season, when time after time they got themselves into a hole against very good teams and found a way to get the job done when it mattered most. It was a great run by a team that was awfully fun to watch.
The Angels remind me a lot of the New England Patriots. Both were expected to do very little before the start of the season. Both got off to slow starts and reached the playoffs by putting together winning streaks late in the year. Both were more impressive on the scoreboard than in the statistical leaderboards. Both were intelligent, fundamentally sound teams that had to scrap for everything they got and came up with big play after big play when things looked bleak. And because of all that, both teams were a lot of fun to watch and served as great examples of why championships are decided on the field, not on paper.
I could have used a more sophisticated statistic like Runs Created to measure the efficiency of each team's offense and defense, thereby factoring in things like stolen bases, hit batsmen, and a few other stats that contribute to success. But I'm partial to simpler measures like TBW that are easy to figure, easy to interpret, and tell essentially the same story as the more complicated stats. I especially like the fact that runs divided by TBW, what I'm calling the run efficiency average, produces a figure that looks a lot like a batting average, a happy coincidence that makes it easier to get a feel for what's good, what's normal, and what's bad.
It was also very interesting to discover the strong tendency of teams that are highly efficient or inefficient in these three areas to move significantly toward the norm the following season. It's very rare for teams to excel (or fall short) in this way two years in a row. That's a good thing for team executives to know as they plan for next season.
I recall being very impressed with the Houston Astros, who refused to panic after a disappointing 2000 season that saw them fall 8 games short of their pythagorean projection. Many teams would have fired the manager and turned over half the roster in a futile attempt to blame someone for their poor showing. Instead, they chalked it up to one of those years when things just didn't go right and were rewarded with a tie for the division title in 2001. (Of course, after the 2001 season, they fired the manager for failing to win in the postseason, but that's a topic for another day.)
A number of this year's most inefficient teams have changed managers in recent weeks, and some of those managers are going to look like geniuses when their clubs make big gains in the win column next year. I wouldn't mind being Dusty Baker right now, assuming the front office doesn't destroy the team with ill-advised personnel moves this winter. The Cubs are the team most likely to get a large efficiency-related bounce, and with one of baseball's best-regarded farm systems, they are poised for a strong run in the NL Central.
]]>By Jim Wheeler
December 10, 2012
Several years ago, Tom Tippett would regularly pen an article discussing the most recently concluded MLB season aimed at discovering which team was the most efficient in scoring and preventing runs along with creating wins. This is my attempt to resurrect these articles. Most of the thought and boilerplate of this article belongs to Tom Tippett and not myself. I am merely trying to keep the flame lit.
Every few seasons it seems like a special team comes from out of nowhere to rocket into the playoffs. In 2006, it was the Arizona Diamondbacks zooming into the NL playoff picture. In 2012, it was the Buck Showalter led Baltimore Orioles who won an amazing 93 games as opposed to a Pythagorean prediction of 82 wins. The Orioles were scintillating in one run games, going 29 and 9, a factor that no doubt helped them reach the American League playoffs. So the million dollar question then becomes; do you think that you can manage the Orioles with equal skill as Buck Showalter did in 2012? Can you replay the "El Birdos" 2012 season and maneuver them into the top of the AL pack? As explained below, doing so will be a real challenge. So as a famous man once said, "Do you feel lucky, punk? Well do you?"
In a nutshell, you win games by outscoring your opponents, so the connection between runs and wins is very strong, even though every season produces a few teams that win more or less than you'd expect given their run differential. To explore the relationship between runs and wins, we'll use the Pythagorean method that was developed by Bill James.
You score runs by putting together hits, walks, steals, and other offensive events, and you prevent runs by holding the other team to a minimum of those things. In most cases, there's a direct relationship between runs and the underlying events that produce runs.
We use the term efficiency to represent the ability to turn events into runs and runs into wins. An efficient team is one that produces more wins than expected given its run margin, produces more runs than expected given its offensive events, or allows fewer runs than expected given the hits and walks produced by their opponents.
In the 2002 edition of this article, we showed that teams that are unusually efficient (or inefficient) have exhibited a very strong tendency to revert back to the norm the next year. That's good news for some teams and bad news for others. If you'd like to find out who falls into which category, read on.
The Bill James Pythagorean method, a well-established formula based on the idea that a team's winning percentage is tightly coupled with runs scored and runs allowed. Bill's formula is quite simple ... take the square of runs scored and divide it by the sum of the squares of runs scored and runs allowed (RF = runs for, RA = runs allowed):
RF ** 2 Projected winning pct = ----------------- RF ** 2 + RA ** 2
In 2012, for instance, 21 of 30 teams finished with win-loss records within three games of their projected records, and 26 of 30 teams finished within five games. In 2010 and 2011, 27 of 30 teams finished within five games of their Pythagorean projection.
We had a very big exception this year. The Orioles won 11 more games than normal for a team with a run margin of +7. On a run-margin basis, they were 29-9 and they surprised everyone by finishing second in the AL East with 93 victories. Since 1962, when the 162-game schedule was first used in both leagues, no team had ever been more than 12 games better than their Pythagorean projection, so the Orioles came very close to tying this record which is also held by several teams.
But 50 years of baseball history tells us that such large deviations are unusual and tend not to be repeated the following year. In other words, the Orioles must dramatically improve their run margin in 2013 if they are to come close to matching this year's win total. The same is true of the Cincinnati Reds, who finished 6 wins to the good.
The teams that most underperformed their Pythagorean records were the Rays, Cardinals, Diamondbacks, Red Sox, and Rockies all with (-5) Pythagorean wins.
Just as there is a strong relationship between runs and wins, it's almost always true that the more hits and walks you produce, the more runs you'll score. Sometimes, of course, a productive team comes up short on the scoreboard because they didn't hit in the clutch, didn't run the bases well, or hit line drives right at people in key situations. But this relationship holds up most of the time.
To shed some light on this relationship, we need a way to take batting stats and turn them into a measure of overall offensive production. There are several good options here, including Runs Created (Bill James), Batting Runs (Pete Palmer), Equivalent Average (Clay Davenport), OPS (on-base average plus slugging average), and Base Runs (David Smyth).
For this exercise, we'll use the sum of total bases and walks, or TBW for short. TBW is not a perfect measure, but it does have a few things going for it. It captures the most important things a team does to produce runs -- singles, extra-base hits, and walks -- and it's easy to figure without a computer.
As with other statistics, a team's TBW total can be significantly influenced by its home park. For that reason, we focus on the difference between the TBW produced by a team's hitters and the TBW allowed by its pitchers. This effectively removes the park from the equation and helps us identify teams that out produced their opponents.
The following table shows the offensive and defensive TBW figures for the 2012 American League, along with the difference between these two figures and each team's league rank based on those differences. It also shows runs for and against, the run differential, and the rankings based on run differential. Finally, because we're trying to trace a path from TBW to runs to wins, it lists the team's win total and league rank for the year.
The 2012 American League:
TBW Runs Wins Team Off Def Diff Rank Off Def Diff Rank Num Rank New York (A) 3068 2753 315 2 804 668 136 1 95 1 Baltimore 2799 2769 30 8 712 705 7 8 93 3t Tampa Bay 2699 2374 325 1 697 577 120 2 90 5 Toronto 2704 2989 -285 13 716 784 -68 10 73 10 Boston 2753 2894 -141 10 734 806 -72 12 69 12 Detroit 2824 2645 179 5 726 670 56 7 88 7 Chicago (A) 2789 2718 71 7 748 676 72 5 85 8 Kansas City 2658 2899 -241 11 676 746 -70 11 72 11 Cleveland 2662 2951 -289 14 667 845 -178 14 68 13 Minnesota 2676 2942 -266 12 701 832 -131 13 66 14 Oakland 2781 2555 226 4 713 614 99 4 94 2 Texas 2971 2696 275 3 808 707 101 3 93 3t Los Angeles (A) 2845 2674 171 6 767 699 68 6 89 6 Seattle 2493 2608 -115 9 619 651 -32 9 75 9
The AL East produced some very intriguing results. The Yankees finished first in Runs Differential (RD) and as expected, won 95 games. Of course we have already discussed the Orioles. With a Runs Differential of 8, they ranked 8th in the American League. Their TBW differential also was a positive 30 which also placed them in 8th place in the AL. Neither stat bodes well for Orioles fans in 2013. The Rays proved to be the biggest losers in the 2012 race. With a Runs differential of 120 (2nd place) and TBW of plus 325 (1st place), you would have expected them to win more than 90 games.
Bringing up the AL East rear was the Blue Jays and Red Sox. Adding to the Red Sox misery is that they finished even worse than their Pythagorean with 5 less victories.
In the AL Central, the White Sox should have won 89 games and the division. But instead, the Tigers went 8-2 in their last 10 games while the White Sox were 4-6 and consequently the Tigers took the division by 3 games. The Tigers had an RD of +56 Runs and the White Sox +72. But Detroit was more efficient in TBW with a +108 advantage over the White Sox.
The remainder of the AL Central, KC, Cleveland and Minnesota were nothing specular. Despite the Indians lack of offense (-289 TBW), they managed to squeeze out 6 more victories than expected. KC, finished with the 11th ranked TBW team, the 11th ranked RD team and finally the 11th ranked team in wins. A trifecta of sorts!
Over in the AL West, the Rangers finished 3rd in TBW, 3rd in RD and tied for 3rd best in AL wins. Yet it was not enough to best the A’s who converted 4th place in both TBW and RD into a division winning 94 wins. Incidentally, those 94 wins represent the 2nd best total in the AL. The Angels never seemed to get rolling in 2012. Their +171 TBW and +68 in RD rated 6th best in the AL and they parlayed this into 89 victories, 1 win better than their expected win total.
The 2012 National League:
TBW Runs Wins Team Off Def Diff Rank Off Def Diff Rank Num Rank Washington 2880 2537 343 1 731 594 137 1 98 1 Atlanta 2678 2502 176 3 700 600 100 3 94 3t Philadelphia 2669 2656 13 9 684 680 4 9 81 9 New York (N) 2605 2673 -68 11 650 709 -59 11 74 12 Miami 2561 2692 -131 13 609 724 -115 13 69 13 Cincinnati 2730 2571 159 4 669 588 81 4 97 2 St. Louis 2900 2594 306 2 765 648 117 2 88 5 Milwaukee 2892 2842 50 6 776 733 43 7 83 7 Pittsburgh 2582 2616 -34 10 651 674 -23 10 79 10 Chicago (N) 2492 2864 -372 15 613 759 -146 15 61 15 Houston 2471 2906 -435 16 583 794 -211 16 55 16 San Francisco 2688 2646 42 7 718 649 69 5 94 3t Los Angeles (N) 2513 2495 18 8 637 597 40 8 86 6 Arizona 2823 2696 127 5 734 688 46 6 81 8 San Diego 2599 2716 -117 12 651 710 -59 12 76 11 Colorado 2884 3216 -332 14 758 890 -132 14 64 14
The old saying used to be, "Washington, First in war, First in Peace and last in the American League." Well guess what, Washington in now in the National League and in First place! The Nationals led all of baseball in 2012 with 98 wins, ranked 1st in DR (+137), and 1st in TBW differential (+343). Yet, the Cardinals who managed to under-perform by 6 Pythagorean wins (88 wins as opposed to 94 projected wins) were victorious in the play-offs over the Nationals. Which leads to one of the great questions of the 2012 season? Had Steven Stasburg been available for the play-offs rather than games in April and May, would the Nationals have done better in the playoffs? Well now you replay managers have your chance to see how history could have been altered with Strasburg in the Nationals playoff rotation.
The National League Central Division produced two teams that were among the leaders in baseball at being efficient and also inefficient. The Reds finished 1st in the division with 97 victories which is 6 better than their Pythagorean expectation. But the Reds were very efficient in producing wins despite rating 4th in the NL in RD (+81) and TWB (+159). Meanwhile, last year’s darlings the Cardinals should have run away with the pennant. The Redbirds had a very potent offense (+306) TWB plus a stingy defense and pitching staff, had a +117 in RD, both good enough for 2nd place in the National League. But when it came to converting these two positives into victories, the Cardinals were -6 games worse than their expected win total. The NL Central pennant race should have been much tighter than it was. It is worth noting that the Reds bested the Cardinals in one-run games going 31-21 compared to the Cardinals 21-26. Perhaps the difference comes down to the performance of "The Cuban Missile" Aroldis Chapman?
Once again the Pirates played well for the first 4 months of the season and then tailed off to finish up with 79 wins, one less than expected. But the Pirates did perform to the level that the stats predict: -34 TBW, -23 RD were both good enough for a 10th place rank amongst the National League. And their final win total was also in sync with their 79 wins. The Brewers were pretty much a mediocre team. They should have won 86 games but their lack of efficiency in TWB and RD doomed them to winning three fewer games and close the season with just 83 wins. The Cubs and Astros were nothing short of horrific in 2012. The Cubs allowed 146 more runs to score than their offense while the Astros had the worst RD in baseball with a -211 RD. Will the AL West be any easier for them in 2013?
The National League West mirrored the Central division in that the Giants won 5 more games than expected while the Diamondbacks lost 5 more games than expected. In fact, the Giants tied the Braves with the 3rd most wins in the NL at 94 and their expected wins total of 89 would still have been the best in the division. But neither the Dodgers nor the Diamondbacks could translate their positive TBW and RD into enough victories to give the Giants much of a challenge. Finishing on the two bottom rungs of the division as projected were the Padres and Rockies.
As we've pointed out, it's unusual for teams that are especially efficient or inefficient to sustain those levels the next year. Instead, they tend to revert to the normal relationships between TBW and runs and between runs and wins. That means we can identify teams that are likely to improve or fall back even if they don't make moves that change their talent level significantly.
For that reason, the Rays have some reason for optimism going into 2013. With Baltimore overachieving and the Yankees losing players to injury and free agency, the Rays are in prime position to improve on their 90 wins. The Blue Jays had added some new interesting players but have a huge gap to make up to reach the top of the division. Hopefully, with a new, less controversial manager at the helm, the Red Sox can produce a much better season in 2013.
The Royals have added some much needed pitching but will it be enough to topple the Tigers with Cabrera, Fielder, and Verlander? Royals RD of -70 could be quickly made up by the new arms of Shields and Davis. In the AL West, Oakland and Texas ran neck and neck in TBW and RD. The unknown status of the Rangers’ Josh Hamilton makes this race too close to call based on their 2012 performances.
Expect the Nationals to again dominate in the National League. If Strasburg has no innings pitched limit, the Nationals may be unstoppable. The NL Central looks like a two team race between the Reds and Cardinals. The Dodgers signing of Greinke has to put them in the early driver’s seat. However, the Giants and Diamondbacks are still close to striking distance and any swing towards their expected win totals of 89 and 86 will make this division race quite interesting.
A lot of things will change between now and Opening Day. This process of looking at TBW differentials and run margins doesn't tell us how the 2013 season will unfold, but it can identify some teams that might have more or less work to do this winter than you may have thought.
I think it's safe to say that the Rays, Rangers, and Cardinals are among the good teams most likely to add to their win totals next season, even without major roster changes. The Braves, Brewers and Diamondbacks are also in line for small efficiency-related bounces.
On the flip side, it will be fascinating to see whether the Orioles and Yankees can sustain their recent successes. Perhaps they were a little lucky. Or perhaps they've figured out how to maximize the impact of the things that don't show up in these measures of expected runs, things such as base running, timely pitching (including a strong bullpen), and the judicious use of one-run strategies.
]]>By Charles Wolfson and Tom Tippett
December 10, 2007
In the mid-to-late 1990’s, Tom started writing an annual essay about teams whose win-loss records were out of synch with their underlying stats. In the early years, these essays were largely unknown to the baseball community, because they were available only as part of the annual Diamond Mind Season Disk. That changed in 2002, when he wrote an article on the subject for ESPN.com, and they’ve been published on ESPN.com and/or the Diamond Mind website ever since.
The central idea is simple. Baseball analysts have developed a variety of methods for translating (a) hits and walks and other events into runs, and (b) runs into wins. One can use these methods to identify teams that scored more or fewer runs than they "should have," teams that allowed more or fewer runs than they "should have," and teams that won or lost more games than they "should have," given the runs they actually scored and allowed.
In the years since our Team Efficiency article first appeared on ESPN.com, others have picked up the ball and run with it. The folks at Baseball Prospectus created a team standings page, updated daily during the season, which shows the actual standings and the standings as they would appear if every team had "normal" relationships between events and runs and wins. Bill James is now providing information of this type for the annual Bill James Baseball Handbooks. And The Hardball Times Baseball Annual has included a chapter on this subject.
In a nutshell, you win games by outscoring your opponents, so the connection between runs and wins is very strong, even though every season produces a few teams that win more or less than you'd expect given their run differential. To explore the relationship between runs and wins, we use the pythagorean method that was developed by Bill James.
You score runs by putting together hits, walks, steals, and other offensive events, and you prevent runs by holding the other team to a minimum of those things. In most cases, there's a direct relationship between runs and the underlying events that produce runs.
We use the term efficiency to represent the ability to turn events into runs and runs into wins. An efficient team is one that produces more wins than expected given its run margin, produces more runs than expected given its offensive events, and/or allows fewer runs than expected given the hits and walks produced by their opponents.
In the 2002 edition of this article, we showed that what goes around, comes around: teams that are unusually efficient (or inefficient) have exhibited a very strong tendency to revert back to the norm the next year. That's good news for some teams and bad news for others. If you'd like to find out who falls into which category, read on.
The Bill James pythagorean method is a well-established formula based on the idea that a team's winning percentage is tightly coupled with runs scored and runs allowed. Bill's formula is quite simple ... take the square of runs scored and divide it by the sum of the squares of runs scored and runs allowed (RS = runs scored, RA = runs allowed):
RF^2 Projected winning pct = ------------ RF^2 + RA^2
The 2007 season was unusual in that just 15 of 30 teams finished with win-loss records within three games, and just 23 of 30 within five games, of their projected record, compared to 18 of 30 and 25 of 30, respectively, in 2006. From 2003 to 2005, 75 of 90 teams finished within five games of their pythagorean projection.
The great outlier in 2006 was the Indians, who won 12 less games than normal for a team with their +88 run differential. No team approached that level of frustration in 2007, but the Diamondbacks overachieved by nearly as big a margin, winning 11 more games than normal for a team outscored by 20 runs, a margin topped by just four teams since 1974. The Mariners were nearly as fortunate, winning 9 more games than their -19 run deficit warranted.
The Red Sox registered an unusual reversal of fortunes. In 2007 they won a major league best 96 games, despite underperforming their projecting win total by seven games, compared to the disappointing 2006 season in which they managed only 86 wins, but exceeded their projected win total by six.
Baseball history tells us that large deviations are unusual and tend not to be repeated the following year. In our 2006 article, we suggested that the Indians could well see a big improvement in their win-loss record in 2007, even without major roster changes; for the same reason, fans of the Diamondbacks and Mariners may have reason to view the upcoming 2008 season with some trepidation.
Just as there is a strong relationship between runs and wins, it's almost always true that the more hits and walks you produce, the more runs you'll score. Sometimes, of course, a productive team comes up short on the scoreboard because they didn't hit in the clutch, didn't run the bases well, or hit line drives right at people in key situations. But this relationship holds up most of the time.
To shed some light on this relationship, we need a way to take batting stats and turn them into a measure of overall offensive production. There are several good options here, including Runs Created (Bill James), Batting Runs (Pete Palmer), Equivalent Average (Clay Davenport), OPS (on-base average plus slugging average), and Base Runs (David Smyth).
For this exercise, we'll use the sum of total bases and walks, or TBW for short. TBW is not a perfect measure, but it does have a few things going for it. It captures the most important things a team does to produce runs -- singles, extra-base hits, and walks -- and it's easy to figure without a computer.
As with other statistics, a team's TBW total can be significantly influenced by its home park. For that reason, we focus on the difference between the TBW produced by a team's hitters and the TBW allowed by its pitchers. This effectively removes the park from the equation and helps us identify teams that outproduced their opponents.
The following table shows the offensive and defensive TBW figures for the 2007 American League, along with the difference between these two figures and each team's league rank based on those differences. It also shows runs for and against, the run differential, and the rankings based on run differential. Finally, because we're trying to trace a path from TBW to runs to wins, it lists the team's win total and league rank for the year.
---------- TBW ---------- ------- Runs -------- - Wins - AL Off Def Diff Rank Off Def Diff Rank Num Rank BOS 3170 2620 550 1 867 657 210 1 96 1t
NYA 3286 2907 379 2 968 777 191 2 94 3t TOR 2854 2633 221 4 753 699 54 6 83 7
BAL 2821 2987 -166 11 756 868 -112 12 69 12t
TBA 2969 3224 -255 13 782 944 -162 14 66 14
CLE 2987 2717 270 3 811 704 107 3 96 1t
DET 3109 2966 143 6 887 797 90 5 88 5t
MIN 2671 2831 -160 10 718 725 -7 7 79 8
CHA 2732 2946 -214 12 693 839 -146 13 72 11
KCA 2573 3011 -438 14 706 778 -72 11 69 12t
LAA 2824 2767 57 7 822 731 91 4 94 3t
SEA 2805 2964 -159 9 794 813 -19 9 88 5t
OAK 2934 2784 150 5 741 758 -17 8 76 9
TEX 2870 3022 -152 8 816 844 -28 10 75 10
In 2007 just seven of 14 AL teams had positive TBW differentials and just six outscored their opponents, compared to ten and nine, respectively, in 2006. The AL did not get quite as big a boost from interleague play in 2007, slipping from 154-98 overall vs. the NL in 2006 to 137-115 last season.
Boston dominated the rankings, leading the league in TBW differential, run margin, and wins. Indeed, their +550 TBW was the ninth best out of 928 team seasons since 1974. The Yankees were a solid second in both categories, and were slightly more efficient than the Red Sox in converting their +379 TBW into +191 runs.
As bad as things were for the bottom-ranked teams, for some they could have been even worse. Kansas City managed to better three other AL teams in run differential, despite a major league worst -438 TBW. The White Sox were a comparatively modest -214 TBW, but their -146 run differential was more than double that of the Royals. Nevertheless, Chicago won three more games than the Royals and six more than their second-worst-in-the-majors run differential predicted.
As already noted, Seattle turned in a remarkable performance in 2007, winning 88 games despite ranking ninth in the league in both TBW and run differential. Since 1974, no other team has managed at least 88 wins with a worse TBW than Seattle’s -159. In fact, only eight teams have managed the feat with a TBW in the red at all.
Minnesota and Los Angeles were notable overachievers, each ranking three spots higher in run differential than TBW. The Angels actually had a higher run differential (+91) than TBW (+57), the only team in 2007 to achieve that feat. Whether this run efficiency is a tribute to their “small ball” proficiency, or further obscures the true urgency of their need to boost their offenses, the 2008 season may reveal.
Oakland’s decline in the standings in 2007 was reflected in their inefficiency. The A’s ranked fifth in the league with a +150 TBW, but just eighth with their -17 run differential. Should the Angels do no more than add Torii Hunter, the Mariners come back down to earth, and the A’s keep their pitching corps intact, they may not be as far from contending in the AL West as the 2007 standings might otherwise suggest.
Moving on to the National League:
---------- TBW ---------- ------- Runs -------- - Wins - NL Off Def Diff Rank Off Def Diff Rank Num Rank PHI 3246 3104 142 5 892 821 71 4 89 3t
NYN 2971 2842 129 7 804 750 54 6 88 5
ATL 3006 2845 161 3 810 733 77 2 84 7
WAS 2679 2997 -318 16 673 783 -110 15 73 11t
FLO 3044 3170 -126 11 790 891 -101 13 71 14t
CHN 2879 2746 133 6 752 690 62 5 85 6
MIL 3033 2876 157 4 801 776 25 7 83 8
SLN 2747 2922 -175 13 725 829 -104 14 78 10
HOU 2858 3113 -255 14 723 813 -90 12 73 11t
CIN 2983 3078 -95 9 783 853 -70 11 72 13
PIT 2754 3046 -292 15 724 846 -122 16 68 16
ARI 2761 2860 -99 10 712 732 -20 9 90 1t
COL 3111 2881 230 2 860 758 102 1 90 1t
SDN 2862 2583 279 1 741 666 75 3 89 3t
LAN 2788 2722 66 8 735 727 8 8 82 9
SFN 2673 2836 -163 12 683 720 -37 10 71 14t
The NL champion Rockies ranked second in TBW and first in run differential. The team they defeated in the one-game playoff for the wild card, the Padres, did almost as well, ranking first in TBW and third in run differential.
The Diamondbacks, however, were the biggest story, winning 90 games despite a -99 TBW and a -20 run differential. Since 1974, the only other teams to manage 90 wins with a negative TBW were the 1984 Mets (-24) and the 1997 Giants (-9).
In 2006, the Cardinals eked out a division title and a World Series championship, despite a -23 TBW and +19 run differential. We gave them something of a “pass” that year on the basis of lengthy injuries to numerous key players. In 2007, however, the team slipped precipitously to -175 TBW and -104 runs. The fact that they managed to win eight more games than that performance warranted should not mislead anyone into thinking that this is not a team in need of a significant overhaul.
Atlanta was reasonably efficient in converting a +161 TBW (ranked third) into +77 runs (ranked second), but not in turning that positive run differential into wins (ranked seventh). Milwaukee was the league’s most inefficient team, ranking fourth with a +157 TBW but just seventh with +25 runs, a testament, perhaps, to their particularly inept defense.
As we've pointed out, it's unusual for teams that are especially efficient or inefficient to sustain those levels the next year. Instead, they tend to revert to the normal relationships between TBW and runs and between runs and wins. That means we can identify teams that are likely to improve or fall back even if they don't make moves that change their talent level significantly.
For that reason, the A’s could be a sleeper team in the comparatively weak AL West, with underlying numbers in 2007 comparable to the Angels and overachieving Mariners. And based on their 2007 figures, Red Sox Nation can expect their team to be at least as tough again in 2008.
In the NL, 2008 could be the year the Braves return to the top of the East Division. It’s improbable for any team to put together a run like Colorado did last fall, but there is nothing in the numbers to suggest that the Rockies cannot carry last season’s improvement into 2008. The Diamondbacks, on the other hand, could be making a mistake if they choose to stand pat this winter. And a rebound by the Cardinals, even in the mediocre NL Central, appears unlikely without significant reinforcements.
]]>By Tom Tippett
December 11, 2006
In the mid-to-late 1990s, I started writing an annual essay about teams whose win-loss records were out of synch with their underlying stats. In the early years, these essays were largely unknown to the baseball community because they were available only as part of our annual Season Disk. That changed in 2002 when I wrote an article on this subject for ESPN.com, and we've been publishing them on ESPN.com and/or the Diamond Mind web site ever since.
The central idea is simple. Baseball analysts have developed a variety of methods for translating (a) hits and walks and other events into runs and (b) runs into wins. One can use these methods to identify teams that scored more or fewer runs than they "should have", teams that allowed more or fewer runs than they "should have", and teams that won or lost more games than they "should have" given the runs they actually scored and allowed.
In the years since our Team Efficiency article first appeared on ESPN.com, others have picked up the ball and run with it. The folks at Baseball Prospectus created a team standings page, updated daily during the season, that shows the actual standings and the standings as they would appear if every team had "normal" relationships between events and runs and wins. Bill James is now providing information of this type for the annual Bill James Baseball Handbooks. And The Hardball Times Baseball Annual has included a chapter on this subject.
In a nutshell, you win games by outscoring your opponents, so the connection between runs and wins is very strong, even though every season produces a few teams that win more or less than you'd expect given their run differential. To explore the relationship between runs and wins, we use the pythagorean method that was developed by Bill James.
You score runs by putting together hits, walks, steals, and other offensive events, and you prevent runs by holding the other team to a minimum of those things. In most cases, there's a direct relationship between runs and the underlying events that produce runs.
We use the term efficiency to represent the ability to turn events into runs and runs into wins. An efficient team is one that produces more wins than expected given its run margin, produces more runs than expected given its offensive events, and/or allows fewer runs than expected given the hits and walks produced by their opponents.
In the 2002 edition of this article, we showed that teams that are unusually efficient (or inefficient) have exhibited a very strong tendency to revert back to the norm the next year. That's good news for some teams and bad news for others. If you'd like to find out who falls into which category, read on.
The Bill James pythagorean method, a well-established formula based on the idea that a team's winning percentage is tightly coupled with runs scored and runs allowed. Bill's formula is quite simple ... take the square of runs scored and divide it by the sum of the squares of runs scored and runs allowed (RF = runs for, RA = runs allowed):
RF ** 2 Projected winning pct = ----------------- RF ** 2 + RA ** 2
In 2006, for instance, 18 of 30 teams finished with win-loss records within three games of their projected records, and 25 of 30 teams finished within five games. From 2003 to 2005, 75 of 90 teams finished within five games of their pythagorean projection.
We had a very big exception this year. The Indians won 12 fewer games than normal for a team with a run margin of +88. On a run-margin basis, they were more like an 90-win team that should have been in the thick of the division race to the end. Since 1962, when the 162-game schedule was first used in both leagues, only five teams have been at least 12 games worse than their pythagorean projection, so the 2006 Indians have assumed a dubious place in modern baseball history.
In a reversal of their 2005 season, Oakland won 93 games despite a +44 run margin that would normally produce an 86-win season. The year before, their real win-loss record fell short of their pythagorean mark by 6 games, enough to cost them the division title that year.
The Mets, Brewers and Red Sox each won 5 more games than their run margin supported. The Rangers and Braves each fell short by 6 games, while the Rockies came up short by 5 wins.
But 44 years of baseball history tells us that large deviations are unusual and tend not to be repeated the following year. In other words, the Indians could easily see a big improvement their win-loss record in 2006 even if they don't make major changes to the roster.
Just as there is a strong relationship between runs and wins, it's almost always true that the more hits and walks you produce, the more runs you'll score. Sometimes, of course, a productive team comes up short on the scoreboard because they didn't hit in the clutch, didn't run the bases well, or hit line drives right at people in key situations. But this relationship holds up most of the time.
To shed some light on this relationship, we need a way to take batting stats and turn them into a measure of overall offensive production. There are several good options here, including Runs Created (Bill James), Batting Runs (Pete Palmer), Equivalent Average (Clay Davenport), OPS (on-base average plus slugging average), and Base Runs (David Smyth).
For this exercise, we'll use the sum of total bases and walks, or TBW for short. TBW is not a perfect measure, but it does have a few things going for it. It captures the most important things a team does to produce runs -- singles, extra-base hits, and walks -- and it's easy to figure without a computer.
As with other statistics, a team's TBW total can be significantly influenced by its home park. For that reason, we focus on the difference between the TBW produced by a team's hitters and the TBW allowed by its pitchers. This effectively removes the park from the equation and helps us identify teams that outproduced their opponents.
The following table shows the offensive and defensive TBW figures for the 2006 American League, along with the difference between these two figures and each team's league rank based on those differences. It also shows runs for and against, the run differential, and the rankings based on run differential. Finally, because we're trying to trace a path from TBW to runs to wins, it lists the team's win total and league rank for the year.
---------- TBW ---------- ------- Runs -------- - Wins - AL Off Def Diff Rank Off Def Diff Rank Num Rank NY 3256 2807 449 1 930 767 163 1 97 1
Tor 3104 2839 265 3 809 754 55 6 87 7 Bos 3117 3026 91 9 820 825 -5 10 86 8
Bal 2850 3182 -332 12 768 899 -131 12 70 12
TB 2739 3134 -395 13 689 856 -167 13 61 14
Min 2870 2718 152 8 801 683 118 3 96 2
Det 2961 2728 233 4 822 675 147 2 95 3
Chi 3127 2925 202 5 868 794 74 5 90 5
Cle 3125 2849 276 2 870 782 88 4 78 10t
KC 2770 3319 -549 14 757 971 -214 14 62 13
Oak 2914 2897 17 10 771 727 44 8 93 4
LA 2869 2701 168 6 766 732 34 9 89 6
Tex 3028 2873 155 7 835 784 51 7 80 9
Sea 2810 2982 -172 11 756 792 -36 11 78 10t
Largely because the AL dominated the NL to such a great degree in inter-league play, it was an unusual year. Ten out of fourteen AL teams were above water in TBW differential and nine had positive run margins. Boston, Minnesota, Detroit, Chicago and Seattle dominated their inter-league series, each winning at least 14 of 18 contests against the weaker league.
The Yankees were strong across the board, leading the league in TBW differential, run margin, and wins. The bottom three teams were rotten in every way. In between, things didn't exactly go according to form.
We've already talked about the Indians, who were second in TBW differential, fourth in run margin, and tied for tenth in wins. That's not easy to do. Cleveland's TBW differential of +276 is in the top 12% of all teams in the past third of a century. Fully 90% of those teams won at least 90 games, and the 2006 Indians are only the third team in that group to lose more games than they won. The 1979 Dodgers went 79-83 with a +282 TBW differential in an aberrational year that was sandwiched between two seasons of at least 92 wins. And the 1984 Phillies finished at .500 despite a differential of +333 bases. Unlike the Dodgers, the Phils did not bounce back the next year, sliding to 76 wins in 1985.
The Blue Jays also underachieved, almost entirely on offense, scoring about 70 runs less than the Runs Created formula predicts based on their underlying stats. With the league's third-best TBW differential, they're positioned to make a postseason run in 2007 if they can overcome their offensive efficiency problems.
Minnesota's TBW differential of +152 was only 8th in the league and normally wouldn't be enough to put a team in playoff contention. But the Twins turned that into the league's third-best run margin and second-best record to grab the division title on the season's final day.
In 2005, Oakland was vastly better than Los Angeles statistically but fell seven games short in the standings. This year saw an almost complete reversal, with LA having a big lead in TBW differential, being roughly even in run margin, and finishing four games behind Oakland in the standings. Combine the two years and you've got parity across the board, so one division title each is about right, even if each team stole one from the other.
Moving on to the National League:
---------- TBW ---------- ------- Runs -------- - Wins - NL Off Def Diff Rank Off Def Diff Rank Num Rank NY 3021 2779 242 1 834 731 103 1 97 1
Phi 3168 3121 47 4 865 812 53 3 85 4
Atl 3066 3026 40 5 849 805 44 5 79 8
Flo 2890 2947 -57 10 758 772 -14 9 78 9
Was 2889 3087 -198 14 746 872 -126 16 71 14
SL 2913 2936 -23 7 781 762 19 6 83 5
Hou 2843 2828 15 6 735 719 16 7 82 6
Cin 2999 3057 -58 11 749 801 -52 12 80 7
Mil 2783 2918 -135 13 730 833 -103 13 75 13
Pit 2664 2995 -331 16 691 797 -106 14 67 15
Chi 2752 3053 -301 15 716 834 -118 15 66 16
SD 2886 2715 171 3 731 679 52 4 88 2t
LA 3035 2855 180 2 820 751 69 2 88 2t
Col 2969 3008 -39 8 813 812 1 8 76 10t
SF 2802 2846 -44 9 746 790 -44 11 76 10t
Ari 2897 2966 -69 12 773 788 -15 10 76 10t
Just like their AL counterparts, the Mets topped the league in TBW differential, run differential, and wins, all by a very comfortable margin, yet failed to make it to the World Series. The rest of the NL East went according to form, with strong relationships between batting events, runs, and wins.
The mediocrity of the Cardinals season is clear from these numbers. Despite being outproduced by 23 bases, St. Louis eked out a run margin of +19 and a winning record by a few games. Some have taken this as an indication that the Cardinals weren't worthy of their World Series title. I don't agree.
Their regular-season stats and record were far worse than they should have been because they lost Albert Pujols, Jim Edmonds, Scott Rolen, David Eckstein, Mark Mulder, and Jason Isringhausen to injuries for long stretches. By the time October rolled around, four of those guys were back, so the postseason Cardinals were more like the team that we projected for the best record in the NL. I still thought the Tigers were the better team, but it's just not fair to say the Cards were undeserving.
The Reds have been defying the odds in recent years, posting an actual record that was at least four games better than their pythagorean record in four of the past five seasons. (The exception was 2005, and the high-water mark was a +10 in 2004.) Still, their stats supported a third-place finish, and that's where they wound up after contending for the top spot almost all year.
In the West, a chasm opened between the top two teams and the rest of the pack. The Padres and Dodgers posted strong numbers across the board, while the other three teams were clustered together, all of them a little below average. Based on their TBW differentials, you'd expect the top two teams to finish in a tie and the bottom three to be within a game of each other, and that's exactly what happened.
Except for the Braves, who fell a few games short, and the Reds, who picked up a few unexpected wins, it was a case of what-you-see-is-what-you-get, with everyone in the NL posting a win-loss record that matched their underlying stats.
As we've pointed out, it's unusual for teams that are especially efficient or inefficient to sustain those levels the next year. Instead, they tend to revert to the normal relationships between TBW and runs and between runs and wins. That means we can identify teams that are likely to improve or fall back even if they don't make moves that change their talent level significantly.
For that reason, the Blue Jays and Indians have some reason for optimism going into 2006. Both put up impressive underlying numbers but didn't get the payoff in runs and/or wins this past season. If their offseason moves are talent-neutral or better, both teams can be expected to contend for at least a wild card next year.
Three teams would be making a mistake if they focus too much on their actual 2006 win-loss records. Boston was more like a .500 team than an 86-win team, Minnesota was quite good but not 96-win good, and Oakland was a little fortunate to win the division.
Judging by the money they're spending this winter, Boston understands that they need to put a much better team on the field in order to contend in 2007. Oakland's front office always seems to understand what they need and find a creative way to get it. Minnesota has yet to make any major moves, but they don't need to do much to remain a contender in what has become a very tough division.
]]>By Tom Tippett
December 12, 2005
This idea is catching on.
In the mid-to-late 1990s, I started writing an annual essay about teams whose win-loss records were out of synch with their underlying stats. In the early years, these essays were largely unknown to the baseball community because they weren't published anywhere other than as part of our annual Season Disk. That changed in 2002 when I wrote an article on this subject for ESPN.com, and we've been publishing them on ESPN.com and/or the Diamond Mind web site ever since.
The central idea is simple. Baseball analysts have developed a variety of methods for translating (a) hits and walks and other events into runs and (b) runs into wins. One can use these methods to identify teams that scored more or fewer runs than they "should have", teams that allowed more or fewer runs than they "should have", and teams that won or lost more games than they "should have" given the runs they actually scored and allowed.
In the three years since our Team Efficiency article first appeared on ESPN.com, others have picked up the ball and run with it. The folks at Baseball Prospectus created a team standings page, updated daily during the season, that shows the actual standings and the standings as they would appear if every team had "normal" relationships between events and runs and wins. Bill James wrote similar essays for the 2005 and 2006 Bill James Baseball Handbooks. And a recent book, The Hardball Times Baseball Annual, includes a chapter by Dan Fox on this subject.
With so many people now writing about this subject, I'm not sure it makes sense for me to continue this series of articles. I have a hard time getting excited about spending time on topics that others are already covering quite well. It's a lot more interesting to spend time on subjects that are not getting enough attention.
So I'll weigh in on the 2005 season, but this may be the last time. If other writers continue to serve the baseball community well in this area, there won't be much left for me to say, and I'll direct my time and energy elsewhere.
In a nutshell, you win games by outscoring your opponents, so the connection between runs and wins is very strong, even though every season produces a few teams that win more or less than you'd expect given their run differential. To explore the relationship between runs and wins, we'll use the pythagorean method that was developed by Bill James.
You score runs by putting together hits, walks, steals, and other offensive events, and you prevent runs by holding the other team to a minimum of those things. In most cases, there's a direct relationship between runs and the underlying events that produce runs.
We use the term efficiency to represent the ability to turn events into runs and runs into wins. An efficient team is one that produces more wins than expected given its run margin, produces more runs than expected given its offensive events, or allows fewer runs than expected given the hits and walks produced by their opponents.
In the 2002 edition of this article, we showed that teams that are unusually efficient (or inefficient) have exhibited a very strong tendency to revert back to the norm the next year. That's good news for some teams and bad news for others. If you'd like to find out who falls into which category, read on.
The Bill James pythagorean method, a well-established formula based on the idea that a team's winning percentage is tightly coupled with runs scored and runs allowed. Bill's formula is quite simple ... take the square of runs scored and divide it by the sum of the squares of runs scored and runs allowed (RF = runs for, RA = runs allowed):
RF ** 2 Projected winning pct = ----------------- RF ** 2 + RA ** 2
In 2005, for instance, 14 of 30 teams finished with win-loss records within three games of their projected records, and 23 of 30 teams finished within five games. In 2003 and 2004, 26 of 30 teams finished within five games of their pythagorean projection.
We had a very big exception this year. The Diamondbacks won 13 more games than normal for a team with a run margin of -160. On a run-margin basis, they were more like an 64-win team than the squad that surprised everyone by finishing second in the NL West with 77 victories. Since 1962, when the 162-game schedule was first used in both leagues, no team had ever been more than 12 games better than their pythagorean projection, so this is a modern record.
But 43 years of baseball history tells us that such large deviations are unusual and tend not to be repeated the following year. In other words, the Diamondbacks must dramatically improve their run margin in 2006 if they are to come close to matching this year's win total. The same is true of the White Sox, who finished 7 wins to the good.
The teams that most underperformed their pythagorean records were the Mets (-7), Athletics (-6), and Mariners (-6).
Just as there is a strong relationship between runs and wins, it's almost always true that the more hits and walks you produce, the more runs you'll score. Sometimes, of course, a productive team comes up short on the scoreboard because they didn't hit in the clutch, didn't run the bases well, or hit line drives right at people in key situations. But this relationship holds up most of the time.
To shed some light on this relationship, we need a way to take batting stats and turn them into a measure of overall offensive production. There are several good options here, including Runs Created (Bill James), Batting Runs (Pete Palmer), Equivalent Average (Clay Davenport), OPS (on-base average plus slugging average), and Base Runs (David Smyth).
For this exercise, we'll use the sum of total bases and walks, or TBW for short. TBW is not a perfect measure, but it does have a few things going for it. It captures the most important things a team does to produce runs -- singles, extra-base hits, and walks -- and it's easy to figure without a computer.
As with other statistics, a team's TBW total can be significantly influenced by its home park. For that reason, we focus on the difference between the TBW produced by a team's hitters and the TBW allowed by its pitchers. This effectively removes the park from the equation and helps us identify teams that outproduced their opponents.
The following table shows the offensive and defensive TBW figures for the 2005 American League, along with the difference between these two figures and each team's league rank based on those differences. It also shows runs for and against, the run differential, and the rankings based on run differential. Finally, because we're trying to trace a path from TBW to runs to wins, it lists the team's win total and league rank for the year.
---------- TBW ---------- ------- Runs -------- - Wins - AL Off Def Diff Rank Off Def Diff Rank Num Rank NY 3165 2807 +358 2 886 789 + 97 5 95 2t Bos 3209 2916 +293 3 910 805 +105 4 95 2t Tor 2759 2777 - 18 9 775 705 + 70 7 80 8 Bal 2856 2881 - 25 10 729 800 - 71 12 74 10 Tam 2772 3164 -392 13 750 936 -186 13 67 13 Chi 2784 2676 +108 6 741 645 + 96 6 99 1 Cle 3043 2546 +497 1 790 642 +148 1 93 5 Min 2661 2625 + 36 8 688 662 + 26 8 83 7 Det 2782 2872 - 90 11 723 787 - 64 11 71 11 KC 2604 3186 -582 14 701 935 -234 14 56 14 LAA 2745 2684 + 61 7 761 643 +118 2 95 2t Oak 2826 2584 +242 4 772 658 +114 3 88 6 Tex 3172 2961 +211 5 865 858 + 7 9 79 9 Sea 2621 2826 -205 12 699 751 - 52 10 69 12
The AL East went pretty much according to form. The Yankees and Red Sox ran away with the TBW lead and finished tied at the top of the division. Both teams, however, were fortunate to win 95 games, as a run differential of +100 usually produces only about 90 wins. The Sox were the more fortunate of the two. If they had allowed as many runs as they "should" have, their runs allowed total would have been higher.
Toronto was a mixed bag of efficiencies and inefficiencies. The offense produced more runs than expected, while the pitching allowed many fewer runs than expected. That level of efficiency could have produced a strong win-loss record, but they gave all of that back by falling nine games short of their pythagorean record. In the end, their TBW stats were right in synch with their 80 wins.
By now, you know the story of the AL Central. Chicago was a bit below average offensively, no matter how you look at it. The pitching was very good, but they were highly efficient, too. Three teams allowed fewer TBW, and a fourth was right with them, but the White Sox still managed to finish in a virtual tie for the league lead in fewest runs allowed. In addition, Chicago won 7 more games than their run margin would normally support thanks to a 35-19 record in one-run contests.
Cleveland was just the opposite. Their TBW differential of +497 was good enough to put them among the twenty best teams from the last 32 seasons. Think about that for a second. In more than three decades, a team has been this good statistically only about once every two seasons, and the Indians still managed to miss the playoffs. Why? Their offense was inefficient and they fell four games short of their pythagorean projection in large part because they were 22-36 in one-run games.
Cleveland outproduced Chicago by 259 TBW but outscored the Sox by only 49 runs. They allowed 130 fewer TBW, but topped Chicago by only 3 runs allowed. Even with these inefficiencies, they managed to post a run margin that was 51 better than Chicago's. Still, the Indians finished six games back.
The story in the AL West was almost identical to the one we told in 2004. Once again, Oakland had a big edge statistically, failed to turn that advantage into a better run differential, and fell short in the standings. The Angels are getting very good at this. Three times in four years, they've been among the game's most efficient teams.
Moving on to the National League:
---------- TBW ---------- ------- Runs -------- - Wins - NL Off Def Diff Rank Off Def Diff Rank Num Rank Atl 2920 2771 +149 5 769 674 + 95 2 90 2 Phi 2985 2807 +178 2 807 726 + 81 4 88 4 NY 2775 2604 +171 3 722 648 + 74 5 83 5t Flo 2766 2766 0 8 717 732 - 15 8 83 5t Was 2583 2736 -153 11 639 673 - 34 9 81 8t StL 2877 2610 +267 1 805 634 +171 1 100 1 Hou 2709 2554 +155 4 693 609 + 84 3 89 3 Mil 2833 2818 + 15 7 726 697 + 29 6 81 8t Chi 2876 2787 + 89 6 703 714 - 11 7 79 10 Cin 3094 3248 -154 12 820 889 - 69 11 73 13 Pit 2700 2931 -231 14 680 769 - 89 13 67 15t SD 2753 2795 - 42 9 684 726 - 42 10 82 7 Ari 2943 3119 -176 13 696 856 -160 16 77 11 SF 2592 2869 -277 15 649 745 - 96 14 75 12 LA 2689 2808 -119 10 685 755 - 70 12 71 14 Col 2783 3149 -366 16 740 862 -122 15 67 15t
St. Louis ran the table for the second straight year, ranking first in TBW differential, run differential, and wins, all by a very comfortable margin.
The most interesting division was the NL East, where the Mets were right with the division leaders in TBW differential and run margin but failed to keep pace in the standings. As I noted earlier in this article, New York had the worst pythagorean differential (-7) in the majors. What could have been a great three-team race turned out to be a fairly comfortable win for the Braves.
In the Central, Houston was a solid number two behind the Cardinals. The Cubs, preseason favorites in the eyes of many, had a TBW differential that would normally support a third-place finish, but a massive inefficiency on offense dropped them behind the Brewers. Chicago produced only one fewer TBW than did the Cardinals, yet St. Louis outscored the Cubs by 102 runs.
The NL West was full of interesting situations, but they had little impact on the pennant race in 2005. For example, Arizona was fascinating in much the same way as the Blue Jays were. At first glance, you'd be tempted to say that they were incredibly lucky, winning 77 games and finishing second despite the league's worst run margin. But it looks like the run margin was as much of an anomaly as the win-loss record.
In particular, Arizona's offense wasn't all that bad. The club was in the middle of the pack in both on-base and slugging, and its walk and homerun totals were third in the league. Their strikeout to walk ratio was fifth-best. Despite these accomplishments, Arizona scored 25 fewer runs than the league average. The Runs Created formula predicts a total of 787 runs, 91 more than they actually scored.
At the same time, the Dodgers were a little unlucky to finish fourth. Their TBW differential and run margin, while wholly unimpressive, were still good enough for a second-place finish in a very weak division. Instead, because three of their rivals posted large positive pythagorean differentials (Arizona +13, San Diego +6, San Francisco +5), they were only one rung from the basement.
As we've pointed out, it's unusual for teams that are especially efficient or inefficient to sustain those levels the next year. Instead, they tend to revert to the normal relationships between TBW and runs and between runs and wins. That means we can identify teams that are likely to improve or fall back even if they don't make moves that change their talent level significantly.
For that reason, the Blue Jays have some reason for optimism going into 2005. Not only did the Yankees and Red Sox overachieve in 2005, neither of these perennial powerhouses has improved so far this offseason. New York still has a lot of questions in its pitching staff, and Boston's roster is still unsettled due to injury risks, trade demands, and potential free agency losses. Meanwhile, Toronto should get more innings out of Roy Halliday and has added three good players in AJ Burnett, BJ Ryan, and Lyle Overbay.
In the AL Central, it will be interesting to see whether Chicago can build on its 2005 successes or whether Cleveland's statistical edge will carry the day in 2006. Similarly, Oakland must try to find a way to convert its superior TBW differential into more wins than the Angels after falling short twice in a row.
Given that the Mets were right there with the Braves and Phillies in TBW differential and run margin in 2005, their recent additions (mainly Carlos Delgado, Billy Wagner, and Paul LoDuca) make them the early favorites to unseat the Braves for the first time in eons.
In the other NL divisions, it's hard to see anyone making a serious run at the Cardinals. Houston may not have Roger Clemens this time around -- he may retire, and because they didn't offer him arbitration, he can't be signed until May 1 -- and they have a lot of ground to make up. It'll be interesting to see whether Milwaukee's young talent will begin to emerge in a big way.
Who knows what will happen in the NL West? All five teams were under water in TBW and runs in 2005, so they've all got some serious work to do.
A lot of things will change between now and opening day. This process of looking at TBW differentials and run margins doesn't tell us how the 2006 season will unfold, but it can identify some teams that might have more or less work to this winter than you may have thought.
I think it's safe to say that the Indians, Athletics, and Mets are among the good teams most likely to add to their win totals next season, even without major roster changes. The Rangers, Cubs and Dodgers are also in line for small efficiency-related bounces.
On the flip side, it will be fascinating to see whether the Angels and White Sox can sustain their recent successes. Perhaps they were a little lucky. Or perhaps they've figured out how to maximize the impact of the things that don't show up in these measures of expected runs, things such as baserunning, timely pitching (including a strong bullpen), and the judicious use of one-run strategies.
]]>Name UID Tm AVG G AB H 2B 3B HR R RBI HBP BB K SB ----------------------- ----- --- ----- --- --- --- -- -- -- --- --- --- --- --- --- Jose Abreu 29210 CHA .317 145 556 176 35 2 36 80 107 11 51 131 3 Christhian Adames 29356 COL .067 7 15 1 0 0 0 1 0 0 0 5 0 Lane Adams 29435 KCA .000 6 3 0 0 0 0 1 0 0 0 2 0 Jesus Aguilar 29271 CLE .121 19 33 4 0 0 0 2 3 0 4 13 0 Nick Ahmed 29325 ARI .200 25 70 14 2 0 1 9 4 0 3 10 0 Arismendy Alcantara 29336 CHN .205 70 278 57 11 2 10 31 29 2 17 93 8 Aaron Altherr 29306 PHI .000 2 5 0 0 0 0 0 0 0 0 2 0 Dean Anna 29227 NYA .136 12 22 3 1 0 1 3 3 0 2 6 0 Erisbel Arruebarrena 29277 LAN .195 22 41 8 1 0 0 4 4 0 3 17 0 Javier Baez 29354 CHN .169 52 213 36 6 0 9 25 20 1 15 95 5 Tucker Barnhart 29223 CIN .185 21 54 10 0 0 1 3 1 0 4 10 0 Vincent Belnome 29329 TBA .100 4 10 1 1 0 0 1 1 0 3 3 0 Mookie Betts 29438 BOS .291 52 189 55 12 1 5 34 18 2 21 31 7 Justin Bour 29293 MIA .284 39 74 21 3 0 1 10 11 0 9 19 0 Bryce Brentz 29432 BOS .308 9 26 8 2 0 0 5 2 0 0 9 0 Gary Brown 29411 SFN .429 7 7 3 0 0 0 1 1 0 0 0 0 Billy Burns 29353 OAK .167 13 6 1 0 0 0 4 0 0 0 0 3 Dan Butler 29363 BOS .211 7 19 4 3 0 0 1 2 0 1 5 0 Eric Campbell 29266 NYN .263 85 190 50 9 0 3 16 16 1 17 55 3 Curtis Casali 29343 TBA .167 30 72 12 3 0 0 10 3 2 8 23 0 Rusney Castillo 29430 BOS .333 10 36 12 1 0 2 6 6 1 3 6 3 Garin Cecchini 29285 BOS .258 11 31 8 3 0 1 6 4 2 3 11 0 Matt Clark 29407 MIL .185 16 27 5 0 0 3 4 7 0 2 8 0 Tyler Collins 29220 DET .250 18 24 6 0 0 1 3 4 0 1 4 0 Christian Colon 29327 KCA .333 21 45 15 5 1 0 8 6 0 3 4 2 CJ Cron 29256 ANA .256 79 242 62 12 1 11 28 37 1 10 61 0 Chris Dominguez 29401 SFN .059 8 17 1 0 0 1 1 2 0 1 4 0 Matt Duffy 29357 SFN .267 34 60 16 2 0 0 5 8 2 1 14 0 Adam Duvall 29320 SFN .192 28 73 14 2 0 3 8 5 1 3 20 0 Cole Figueroa 29272 TBA .233 23 43 10 2 1 0 6 6 0 4 4 0 Maikel Franco 29388 PHI .179 16 56 10 2 0 0 5 5 0 1 13 0 Greg Garcia 29248 SLN .143 14 14 2 1 0 0 2 1 3 1 6 0 Bradley Glenn 29321 TOR .067 6 15 1 0 0 0 0 0 0 1 5 0 Jacob Goebbert 29308 SDN .218 51 101 22 1 3 1 12 10 2 12 32 2 Terrance Gore 29434 KCA .000 11 1 0 0 0 0 5 0 1 0 0 5 Randal Grichuk 29249 SLN .245 47 110 27 6 1 3 11 8 0 5 31 0 Alexander Guerrero 29211 LAN .077 11 13 1 0 0 0 0 0 0 0 6 0 Alex Hassan 29284 BOS .125 3 8 1 0 0 0 1 0 0 1 5 0 Enrique Hernandez 29328 MIA .175 18 40 7 2 1 2 3 6 1 4 10 0 Enrique Hernandez 29328 HOU .284 24 81 23 4 2 1 10 8 0 8 11 0 Dilson Herrera 29382 NYN .220 18 59 13 0 1 3 6 11 0 7 17 0 Tyler Holt 29331 CLE .268 36 71 19 2 0 0 4 2 1 3 25 2 Ender Inciarte 29253 ARI .278 118 418 116 18 2 4 54 27 0 25 53 19 James Jones 29237 SEA .250 108 312 78 9 5 0 46 9 0 12 67 27 Caleb Joseph 29263 BAL .207 82 246 51 9 0 9 22 28 3 17 69 0 Jake Lamb 29361 ARI .230 37 126 29 4 1 4 15 11 0 6 37 1 Tommy La Stella 29281 ATL .251 93 319 80 16 1 1 22 31 1 36 40 2 Rymer Liriano 29365 SDN .220 38 109 24 2 0 1 13 6 2 9 39 4 Rafael Lopez 29405 CHN .182 7 11 2 0 0 0 0 1 0 2 4 0 James McCann 29396 DET .250 9 12 3 1 0 0 2 0 0 0 2 1 Steven Moya 29395 DET .375 11 8 3 0 0 0 2 0 0 0 2 0 Adrian Nieto 29219 CHA .236 48 106 25 5 0 2 8 7 1 8 38 0 Rougned Odor 29264 TEX .259 114 386 100 14 7 9 39 48 5 17 71 4 Shawn O'Malley 29419 ANA .188 11 16 3 0 0 0 3 1 0 0 8 2 Joe Panik 29314 SFN .305 73 269 82 10 2 1 31 18 0 16 33 0 Kyle Parker 29305 COL .192 18 26 5 1 0 0 1 1 0 0 14 0 Ben Paulsen 29347 COL .317 31 63 20 4 0 4 8 10 1 2 19 0 Joc Pederson 29386 LAN .143 18 28 4 0 0 0 1 0 0 9 11 0 Francisco Pena 29276 KCA .000 1 0 0 0 0 0 0 0 0 0 0 0 David Peralta 29286 ARI .286 88 329 94 12 9 8 40 36 1 16 60 6 Roberto Perez 29338 CLE .271 29 85 23 5 0 1 10 4 0 5 26 0 Jace Peterson 29243 SDN .113 27 53 6 0 0 0 3 0 1 2 18 2 Tommy Pham 29425 SLN .000 6 2 0 0 0 0 0 0 0 0 2 0 Jose Pirela 29436 NYA .333 7 24 8 1 2 0 6 3 0 1 4 0 Gregory Polanco 29299 PIT .235 89 277 65 9 0 7 50 33 0 30 59 14 Jorge Polanco 29322 MIN .333 5 6 2 1 1 0 2 3 0 2 2 0 Dalton Pompey 29426 TOR .231 17 39 9 1 2 1 5 4 0 4 12 1 J.T. Realmuto 29292 MIA .241 11 29 7 1 1 0 4 9 0 1 8 0 Carlos Rivero 29381 BOS .571 4 7 4 2 0 1 1 3 0 1 0 0 Dan Robertson 29250 TEX .271 70 177 48 9 1 0 23 21 0 17 28 6 Guilder Rodriguez 29423 TEX .167 7 12 2 0 0 0 2 1 0 1 5 0 Yorman Rodriguez 29412 CIN .222 11 27 6 0 0 0 3 2 1 1 12 0 Jason Rogers 29406 MIL .111 8 9 1 1 0 0 0 0 0 1 1 0 Miguel Rojas 29296 LAN .181 85 149 27 3 0 1 16 9 2 10 28 0 Jamie Romak 27729 LAN .048 15 21 1 1 0 0 2 3 0 2 8 0 Stefen Romero 29217 SEA .192 72 177 34 6 2 3 19 11 6 4 48 0 Ryan Rua 29385 TEX .295 28 105 31 7 0 2 11 14 2 2 18 1 Carlos Sanchez 29342 CHA .250 28 100 25 5 0 0 6 5 0 3 25 1 Danny Santana 29258 MIN .319 101 405 129 27 7 7 70 40 3 19 98 20 Domingo Santana 29437 HOU .000 6 17 0 0 0 0 1 0 0 1 14 0 Luis Sardinas 29240 TEX .261 43 115 30 6 0 0 12 8 2 5 21 5 Xavier Scruggs 29418 SLN .200 9 15 3 1 0 0 0 2 1 2 7 0 Jonathan Singleton 29287 HOU .168 95 310 52 13 0 13 42 44 1 50 134 2 Jake Smolinski 29334 TEX .349 24 86 30 5 0 3 12 12 3 3 24 0 Yangervis Solarte 29221 SDN .267 56 217 58 5 1 4 30 17 1 23 24 0 Yangervis Solarte 29221 NYA .253 75 253 64 14 0 6 26 31 3 30 35 0 Jorge Soler 29378 CHN .292 24 89 26 8 1 5 11 20 0 6 24 1 Steven Souza 29232 WAS .130 21 23 3 0 0 2 2 2 0 3 7 0 Cory Spangenberg 29398 SDN .290 20 62 18 2 1 2 7 9 0 2 14 4 George Springer 29235 HOU .231 78 295 68 8 1 20 45 51 9 39 114 5 Eugenio Suarez 29294 DET .242 85 244 59 9 1 4 33 23 5 22 67 3 Andrew Susac 29350 SFN .273 35 88 24 8 0 3 13 19 0 7 28 0 Matthew Szczur 29373 CHN .226 33 62 14 2 0 2 6 5 0 4 11 0 Oscar Taveras 29283 SLN .239 80 234 56 8 0 3 18 22 1 12 37 0 Chris Taylor 29349 SEA .287 47 136 39 8 0 0 16 9 2 11 39 5 Michael Taylor 29366 WAS .205 17 39 8 3 0 1 5 5 1 3 17 0 Tomas Telis 29377 TEX .250 18 68 17 2 0 0 7 8 1 1 10 0 Kennys Vargas 29355 MIN .274 53 215 59 10 1 9 26 38 3 12 63 0 Christian Vazquez 29339 BOS .240 55 175 42 9 0 1 15 20 0 19 33 0 Christian Walker 29433 BAL .167 6 18 3 1 0 1 1 1 0 1 9 0 Zelous Wheeler 29330 NYA .193 29 57 11 0 0 2 6 5 1 2 12 0 Andy Wilkins 29387 CHA .140 17 43 6 2 0 0 2 2 0 2 22 0 Jackson Williams 29380 COL .214 7 14 3 0 0 1 1 3 0 2 4 0 Rafael Ynoa 29393 COL .343 19 67 23 6 1 0 5 13 0 4 9 0
Name UID Tm G GS W L S ERA Inn H R ER BB K HR ----------------------- ----- --- --- -- -- -- -- ------ ----- --- --- --- --- --- -- A.J. Achter 29404 MIN 7 0 1 0 0 3.27 11.0 14 7 4 3 5 2 Austin Adams 29341 CLE 6 0 0 0 0 9.00 7.0 9 7 7 1 4 1 Dario Alvarez 29403 NYN 4 0 0 0 0 13.50 1.3 4 2 2 0 1 1 R.J. Alvarez 29410 SDN 10 0 0 0 0 1.13 8.0 3 1 1 5 9 0 Chase Anderson 29265 ARI 21 21 9 7 0 4.01 114.3 117 56 51 40 105 16 Pedro Baez 29260 LAN 20 0 0 0 0 2.63 24.0 16 7 7 5 18 3 Matt Barnes 29421 BOS 5 0 0 0 0 4.00 9.0 11 4 4 2 8 1 Aaron Barrett 29214 WAS 50 0 3 0 0 2.66 40.7 33 17 12 20 49 1 Chris Bassitt 29384 CHA 6 5 1 1 0 3.94 29.7 34 13 13 13 21 0 Cam Bedrosian 29291 ANA 17 0 0 1 0 6.52 19.3 23 17 14 12 20 2 Dallas Beeler 29323 CHN 2 2 0 2 0 3.27 11.0 10 5 4 7 6 0 Christian Bergman 29298 COL 10 10 3 5 0 5.93 54.7 75 37 36 10 31 9 Brett Bochy 29429 SFN 3 0 0 0 0 5.40 3.3 1 2 2 2 3 1 Mike Bolsinger 29234 ARI 10 9 1 6 0 5.50 52.3 66 36 32 17 48 7 Lisalberto Bonilla 29413 TEX 5 3 3 0 0 3.05 20.7 13 8 7 12 17 2 Aaron Brooks 29255 KCA 2 1 0 1 0 43.88 2.7 12 13 13 3 2 1 Brooks Brown 28065 COL 28 0 0 1 0 2.77 26.0 20 9 8 5 21 3 David Buchanan 29280 PHI 20 20 6 8 0 3.75 117.7 120 55 49 32 71 12 Jake Buchanan 29311 HOU 17 2 1 3 0 4.58 35.3 41 19 18 12 20 4 Ryan Buchter 29218 ATL 1 0 1 0 0 0.00 1.0 0 0 0 1 1 0 Eddie Butler 29295 COL 3 3 1 1 0 6.75 16.0 23 12 12 7 3 2 Leonel Campos 29402 SDN 6 0 0 0 0 5.14 7.0 9 5 4 4 9 0 Scott Carroll 29247 CHA 26 19 5 10 0 4.80 129.3 147 81 69 45 64 13 Andrew Chafin 29367 ARI 3 3 0 1 0 3.86 14.0 13 6 6 8 10 0 Alex Claudio 29369 TEX 15 0 0 0 0 2.92 12.3 14 4 4 4 14 0 Carlos Contreras 29313 CIN 17 0 0 1 0 6.52 19.3 19 16 14 17 19 2 Daniel Corcino 29376 CIN 5 3 0 2 0 4.34 18.7 13 9 9 10 15 2 Erik Cordier 29408 SFN 7 0 0 0 0 1.50 6.0 5 4 1 2 9 0 Daniel Coulombe 29431 LAN 5 0 0 0 0 4.15 4.3 5 3 2 2 4 1 Kyle Crockett 29274 CLE 44 0 4 1 0 1.80 30.0 26 6 6 8 28 2 Logan Darnell 29259 MIN 7 4 0 2 0 7.13 24.0 31 20 19 8 22 5 Jake deGrom 29269 NYN 22 22 9 6 0 2.69 140.3 117 44 42 43 144 7 Ryan Dennick 29397 CIN 8 0 0 0 0 11.57 4.7 7 7 6 4 3 2 Anthony DeSclafani 29267 MIA 13 5 2 2 0 6.27 33.0 40 23 23 5 26 4 Odrisamer Despaigne 29316 SDN 16 16 4 7 0 3.36 96.3 85 44 36 32 65 6 Jumbo Diaz 27503 CIN 36 0 0 1 0 3.38 34.7 29 13 13 14 37 3 Jairo Diaz 29424 ANA 5 0 0 0 0 3.18 5.7 4 2 2 3 8 0 Jon Edwards 29372 TEX 9 0 0 0 0 4.32 8.3 13 5 4 5 9 0 Roenis Elias 29225 SEA 29 29 10 12 0 3.79 163.7 151 77 69 64 143 16 Edwin Escobar 29379 BOS 2 0 0 0 0 4.50 2.0 1 1 1 0 2 0 Buck Farmer 29368 DET 4 2 0 1 0 11.57 9.3 12 12 12 5 11 2 Brandon Finnegan 29416 KCA 7 0 0 1 0 1.29 7.0 6 1 1 1 10 0 Yohan Flande 29318 COL 16 10 0 6 0 5.19 59.0 55 34 34 16 34 5 Mike Foltynewicz 29352 HOU 16 0 0 1 0 5.30 18.7 23 11 11 7 14 3 Eric Fornataro 29241 SLN 8 0 0 0 0 4.66 9.7 11 6 5 1 3 0 Carlos Frias 29360 LAN 15 2 1 1 0 6.12 32.3 33 22 22 7 29 4 Frank Garces 29374 SDN 15 0 0 0 0 2.00 9.0 8 2 2 1 10 1 Yimi Garcia 29394 LAN 8 0 0 0 0 1.80 10.0 6 2 2 1 9 2 Kenny Giles 29301 PHI 44 0 3 1 1 1.18 45.7 25 7 6 11 64 1 Erik Goeddel 29389 NYN 6 0 0 0 0 2.70 6.7 3 2 2 4 6 0 Marco Gonzales 29317 SLN 10 5 4 2 0 4.15 34.7 32 16 16 21 31 4 Miguel Alfredo Gonzalez 29399 PHI 6 0 0 1 0 6.75 5.3 9 4 4 3 5 1 Kendall Graveman 29415 TOR 5 0 0 0 0 3.86 4.7 4 2 2 0 4 0 Nicholas Greenwood 29304 SLN 19 1 2 1 0 4.75 36.0 36 19 19 6 17 5 Shane Greene 29242 NYA 15 14 5 4 0 3.78 78.7 81 38 33 29 81 8 Jarrett Grube 27896 ANA 1 0 0 0 0 13.50 .7 1 1 1 0 0 1 Bradin Hagens 29370 ARI 2 0 0 1 0 3.38 2.7 4 1 1 3 2 0 Jesse Hahn 29289 SDN 14 12 7 4 0 3.07 73.3 57 26 25 32 70 4 Blaine Hardy 29303 DET 38 0 2 1 0 2.54 39.0 34 12 11 20 31 1 Andrew Heaney 29307 MIA 7 5 0 3 0 5.83 29.3 32 19 19 7 20 6 Kyle Hendricks 29337 CHN 13 13 7 2 0 2.46 80.3 72 24 22 15 47 4 Christopher Heston 29428 SFN 3 1 0 0 0 5.06 5.3 6 3 3 3 4 0 Taylor Hill 29319 WAS 3 1 0 1 0 9.00 9.0 16 9 9 3 5 0 John Holdzkom 29409 PIT 9 0 1 0 1 2.00 9.0 4 2 2 2 14 1 Mario Hollands 29439 PHI 50 0 2 2 0 4.40 47.0 45 25 23 21 35 3 T.J. House 29273 CLE 19 18 5 3 0 3.35 102.0 113 41 38 22 80 10 Juan Jaime 29310 ATL 16 0 0 0 0 5.84 12.3 14 8 8 9 18 1 Eric Jokisch 29420 CHN 4 1 0 0 0 1.88 14.3 18 6 3 4 10 3 Tommy Kahnle 29224 COL 54 0 2 1 0 4.19 68.7 51 39 32 31 63 7 Phil Klein 29358 TEX 17 0 1 2 0 2.84 19.0 11 6 6 10 23 3 Corey Knebel 29279 DET 8 0 0 0 0 6.23 8.7 11 7 6 3 11 0 Dominic Leone 29229 SEA 57 0 8 2 0 2.17 66.3 52 18 16 25 70 4 Kyle Lobstein 29375 DET 7 6 1 2 0 4.35 39.3 35 20 19 14 27 3 Michael Mariot 29230 KCA 17 0 1 0 0 6.48 25.0 31 21 18 12 21 2 Justin Marks 29238 KCA 1 0 0 0 0 13.50 2.0 4 3 3 3 2 0 Evan Marshall 29262 ARI 57 0 4 4 0 2.74 49.3 50 17 15 17 54 3 Chris Martin 29246 COL 16 0 0 0 0 6.89 15.7 22 12 12 4 14 2 Nick Martinez 29228 TEX 29 24 5 12 0 4.55 140.3 150 79 71 55 77 18 Tyler Matzek 29300 COL 20 19 6 11 0 4.05 117.7 120 53 53 44 91 9 Trevor May 29364 MIN 10 9 3 6 0 7.88 45.7 59 41 40 22 44 7 Patrick McCoy 29315 DET 14 0 0 0 0 3.86 14.0 21 6 6 13 11 0 Roman Mendez 29333 TEX 30 0 0 1 0 2.18 33.0 20 8 8 17 22 2 Melvin Mercedes 29371 DET 1 0 0 0 0 0.00 2.0 0 0 0 0 2 0 Justin Miller 29236 DET 8 0 1 0 0 5.11 12.3 14 9 7 2 5 2 Bryan Mitchell 29362 NYA 3 1 0 1 0 2.45 11.0 10 3 3 3 7 0 Rafael Montero 29268 NYN 10 8 1 3 0 4.06 44.3 44 21 20 23 42 8 Michael Morin 29251 ANA 60 0 4 4 0 2.90 59.0 51 22 19 19 54 3 Hector Neris 29359 PHI 1 0 1 0 0 0.00 1.0 0 0 0 0 1 0 Daniel Norris 29414 TOR 5 1 0 0 0 5.40 6.7 5 4 4 5 4 1 Rudy Owens 29278 HOU 1 1 0 1 0 7.94 5.7 9 5 5 2 1 1 Red Patterson 29252 LAN 1 1 0 0 0 1.93 4.7 2 1 1 3 1 0 Spencer Patton 29417 TEX 9 0 1 0 0 .96 9.3 6 1 1 2 8 0 Yohan Pino 28026 MIN 11 11 2 5 0 5.07 60.3 66 37 34 14 50 8 Bryan Price 29390 CLE 3 0 0 0 0 20.25 2.7 8 6 6 1 1 3 Kevin Quackenbush 29245 SDN 56 0 3 3 6 2.48 54.3 42 15 15 18 56 2 Jose Ramirez 29290 NYA 8 0 0 2 0 5.40 10.0 11 6 6 7 10 2 Neil Ramirez 29244 CHN 50 0 3 3 3 1.44 43.7 29 11 7 17 53 2 Anthony Ranaudo 29351 BOS 7 7 4 3 0 4.81 39.3 39 21 21 16 15 10 Rob Rasmussen 29275 TOR 10 0 0 0 0 3.18 11.3 8 4 4 7 13 1 Robbie Ray 29261 DET 9 6 1 4 0 8.16 28.7 43 26 26 11 19 5 CJ Riefenhauser 29239 TBA 7 0 0 0 0 8.44 5.3 6 5 5 3 2 0 Donn Roach 29222 SDN 16 1 1 0 0 4.75 30.3 36 17 16 15 17 2 Wilking Rodriguez 29288 KCA 2 0 0 0 0 0.00 2.0 1 0 0 1 1 0 Jorge Rondon 29324 SLN 1 0 0 0 0 0.00 1.0 0 0 0 1 0 0 Seth Rosin 29213 TEX 3 0 1 0 0 6.75 4.0 6 3 3 1 3 0 Ben Rowen 29302 TEX 8 0 0 0 0 4.15 8.7 10 4 4 4 7 0 Drew Rucinski 29340 ANA 3 0 0 0 0 4.91 7.3 9 4 4 2 8 0 Kyle Ryan 29383 DET 6 1 2 0 0 2.61 10.3 10 3 3 2 4 0 Casey Sadler 29254 PIT 6 0 0 1 0 7.84 10.3 12 9 9 5 7 0 Aaron Sanchez 29348 TOR 24 0 2 2 3 1.09 33.0 14 5 4 9 27 1 Gus Schlosser 29215 ATL 15 0 0 1 0 7.64 17.7 23 16 15 6 8 2 Bo Schultz 29212 ARI 4 0 0 1 0 7.88 8.0 13 7 7 1 5 1 Chasen Shreve 29345 ATL 15 0 0 0 0 .73 12.3 10 1 1 3 15 0 Shae Simmons 29282 ATL 26 0 1 2 1 2.91 21.7 15 8 7 11 23 1 Chad Smith 29312 DET 10 0 0 0 0 5.40 11.7 15 7 7 3 9 1 Carson Smith 29392 SEA 9 0 1 0 0 0.00 8.3 2 0 0 3 10 0 Scott Snodgress 29400 CHA 4 0 0 0 0 15.43 2.3 8 7 4 3 1 1 Matt Stites 29309 ARI 37 0 0 0 0 5.73 33.0 33 23 21 16 26 6 Hunter Strickland 29391 SFN 9 0 1 0 1 0.00 7.0 5 0 0 0 9 0 Marcus Stroman 29257 TOR 26 20 11 6 1 3.65 130.7 125 56 53 28 111 7 Masahiro Tanaka 29226 NYA 20 20 13 5 0 2.77 136.3 123 47 42 21 141 15 Ian Thomas 29216 ATL 16 0 1 2 0 4.22 10.7 10 5 5 6 13 0 Taylor Thompson 29346 CHA 5 0 0 0 0 10.13 5.3 9 6 6 4 4 1 Blake Treinen 29233 WAS 15 7 2 3 0 2.49 50.7 57 17 14 13 30 1 Nick Tropeano 29427 HOU 4 4 1 3 0 4.57 21.7 19 12 11 9 13 0 Sam Tuivailala 29422 SLN 2 0 0 0 0 36.00 1.0 5 4 4 2 1 2 Drew VerHagen 29344 DET 1 1 0 1 0 5.40 5.0 5 3 3 3 4 0 Tsuyoshi Wada 29332 CHN 13 13 4 4 0 3.25 69.3 67 28 25 19 57 7 Wei-chung Wang 29231 MIL 14 0 0 0 0 10.90 17.3 30 23 21 8 13 6 Matthew West 29335 TEX 3 0 0 0 0 6.75 4.0 6 3 3 1 3 0 Chase Whitley 29270 NYA 24 12 4 3 0 5.23 75.7 94 44 44 18 60 10 Kirby Yates 29297 TBA 37 0 0 2 1 3.75 36.0 33 16 15 15 42 4]]>
Name UID Tm AVG G AB H 2B 3B HR R RBI HBP BB K SB ----------------------- ----- --- ----- --- --- --- -- -- -- --- --- --- --- --- --- David Adams 29043 NYA .193 43 140 27 5 1 2 10 13 2 9 43 0 James Adduci 29174 TEX .258 17 31 8 1 0 0 2 0 0 3 9 2 Ehire Adrianza 29191 SFN .222 9 18 4 1 0 1 3 3 0 1 5 0 Abraham Almonte 29168 SEA .264 25 72 19 4 0 2 10 9 0 6 21 1 Zoilo Almonte 29086 NYA .236 34 106 25 4 0 1 9 9 0 6 19 3 Oswaldo Arcia 29014 MIN .251 97 351 88 17 2 14 34 43 4 23 117 1 Nolan Arenado 29029 COL .267 133 486 130 29 4 10 49 52 1 23 72 2 Cody Asche 29134 PHI .235 50 162 38 8 1 5 18 22 1 15 43 1 Brandon Bantz 29073 SEA .000 1 2 0 0 0 0 0 0 0 0 1 0 Tim Beckham 29202 TBA .429 5 7 3 0 0 0 1 1 0 0 0 0 Engel Beltre 29095 TEX .250 22 40 10 1 0 0 7 2 1 0 5 1 Christian Bethancourt 29208 ATL .000 1 1 0 0 0 0 0 0 0 0 1 0 Xander Bogaerts 29155 BOS .250 18 44 11 2 0 1 7 5 0 5 13 1 Jackie Bradley 28989 BOS .189 37 95 18 5 0 3 18 10 2 10 31 2 Nicholas Buss 29197 LAN .105 8 19 2 0 0 0 0 0 0 1 1 0 Joseph Butler 29143 TEX .333 8 12 4 2 0 0 3 1 0 3 6 0 Nick Castellanos 29170 DET .278 11 18 5 0 0 0 1 0 0 0 1 0 Juan Centeno 29200 NYN .300 4 10 3 0 0 0 0 1 0 0 1 0 Michael Choice 29173 OAK .278 9 18 5 1 0 0 2 0 0 1 6 0 Cody Clark 27940 HOU .105 16 38 4 1 0 0 1 0 0 1 15 0 Chris Colabello 29049 MIN .194 55 160 31 3 0 7 14 17 1 20 58 0 Todd Cunningham 29133 ATL .250 8 8 2 0 0 0 2 0 0 0 3 0 Jermaine Curtis 29026 SLN .000 5 3 0 0 0 0 0 0 1 1 1 0 Travis D'Arnaud 29152 NYN .202 31 99 20 3 0 1 4 5 0 12 21 0 Khristopher Davis 28992 MIL .279 56 136 38 10 0 11 27 27 5 11 34 3 Matt Davidson 29147 ARI .237 31 76 18 6 0 3 8 12 1 10 24 0 Jaff Decker 29087 SDN .154 13 26 4 0 0 1 3 2 0 3 4 0 Matt Den Dekker 29166 NYN .207 27 58 12 1 0 1 7 6 1 4 23 4 Jonathan Diaz 29101 BOS .000 5 4 0 0 0 0 2 0 0 0 0 0 Corey Dickerson 29090 COL .263 69 194 51 13 5 5 32 17 0 16 41 2 Derek Dietrich 29038 MIA .214 57 215 46 10 2 9 32 23 7 11 56 1 Wilmer Flores 29141 NYN .211 27 95 20 5 0 1 8 13 0 5 23 0 Nick Franklin 29057 SEA .225 102 369 83 20 1 12 38 45 0 42 113 6 Nate Freiman 28993 OAK .274 80 190 52 8 1 4 10 24 2 14 31 0 Reymond Fuentes 29164 SDN .152 23 33 5 0 0 0 4 1 0 3 16 3 Leury Garcia 29002 CHA .204 20 49 10 1 0 0 2 1 0 4 18 6 Leury Garcia 29002 TEX .192 25 52 10 0 1 0 8 1 0 3 16 1 Evan Gattis 28996 ATL .243 105 354 86 21 0 21 44 65 4 21 81 0 Scooter Gennett 29068 MIL .324 69 213 69 11 2 6 29 21 1 10 42 2 Caleb Gindl 29082 MIL .242 57 132 32 7 2 5 17 14 0 20 25 2 Ryan Goins 29160 TOR .252 34 119 30 5 0 2 11 8 0 2 28 0 Miguel Gonzalez 29193 CHA .222 5 9 2 0 0 0 0 0 0 0 3 0 Tuffy Gosewisch 29138 ARI .178 14 45 8 2 0 0 1 3 0 0 8 0 Phil Gosselin 29153 ATL .333 4 6 2 0 0 0 2 0 0 1 2 0 Grant Green 29106 OAK .000 5 15 0 0 0 0 0 1 0 0 6 0 Grant Green 29106 ANA .280 40 125 35 8 1 1 16 16 1 10 38 0 Robbie Grossman 29023 HOU .268 63 257 69 14 0 4 29 21 2 23 70 6 Jedd Gyorko 28985 SDN .249 125 486 121 26 0 23 62 63 4 33 123 1 Sean Halton 29096 MIL .238 42 101 24 4 0 4 9 17 3 5 31 0 Billy Hamilton 29179 CIN .368 13 19 7 2 0 0 9 1 0 2 4 13 Cesar Hernandez 29059 PHI .289 34 121 35 5 0 0 17 10 1 9 26 0 Aaron Hicks 28988 MIN .192 81 281 54 11 3 8 37 27 2 24 84 9 Luis Jimenez 29010 ANA .260 34 104 27 6 0 0 15 5 3 2 28 0 Corban Joseph 29033 NYA .167 2 6 1 1 0 0 1 0 0 1 1 0 Kevin Kiermaier 29209 TBA .000 1 0 0 0 0 0 0 0 0 0 0 0 Roger Kieschnick 29135 SFN .202 38 84 17 0 1 0 6 5 0 11 29 0 Jeff Kobernus 29053 WAS .167 24 30 5 0 0 1 8 1 1 5 6 3 Marc Krauss 29088 HOU .209 52 134 28 9 0 4 11 13 1 10 45 2 Juan Lagares 29024 NYN .242 121 392 95 21 5 4 35 34 2 20 96 6 Junior Lake 29120 CHN .284 64 236 67 16 0 6 26 16 4 13 68 4 Andrew Lambo 29149 PIT .233 18 30 7 2 0 1 4 2 0 3 11 0 Edward Lucas 29060 MIA .256 94 351 90 14 1 4 43 28 2 26 78 1 Donald Lutz 29030 CIN .241 34 58 14 1 0 1 5 8 0 1 14 2 Jake Marisnick 29125 MIA .183 40 109 20 2 1 1 6 5 1 6 27 3 Alfredo Marte 28995 ARI .186 22 43 8 3 0 0 4 4 1 4 12 0 Christopher McGuiness 29071 TEX .176 10 34 6 1 0 0 0 1 0 0 13 0 Thomas Medica 29194 SDN .290 19 69 20 2 0 3 9 10 0 10 23 0 Bradley Miller 29098 SEA .265 76 306 81 11 6 8 41 36 1 24 52 5 Johnny Monell 29186 SFN .125 8 8 1 0 0 0 2 1 1 0 3 0 J.R. Murphy 29172 NYA .154 16 26 4 1 0 0 3 1 0 1 9 0 Wil Myers 29085 TBA .293 88 335 98 23 0 13 50 53 1 33 91 5 Nick Noonan 28994 SFN .219 62 105 23 2 0 0 12 5 0 6 24 0 Chris Owings 29178 ARI .291 20 55 16 5 0 0 5 5 0 6 10 2 Marcell Ozuna 29031 MIA .265 70 275 73 17 4 3 31 32 2 13 57 5 Audry Perez 29198 SLN .000 2 1 0 0 0 0 0 0 0 0 1 0 Juan Perez 29074 SFN .258 34 89 23 5 0 1 8 8 0 6 21 2 Brock Peterson 27707 SLN .077 23 26 2 0 0 0 0 2 0 2 11 0 Shane Peterson 29015 OAK .143 2 7 1 0 0 0 1 1 0 1 3 0 Josh Phegley 29105 CHA .206 65 204 42 7 0 4 14 22 0 5 41 2 Kevin Pillar 29150 TOR .206 36 102 21 4 0 3 11 13 2 4 29 0 Josmil Pinto 29169 MIN .342 21 76 26 5 0 4 10 12 1 6 22 0 Josh Prince 29004 MIL .125 8 8 1 1 0 0 3 0 0 1 1 0 Yasiel Puig 29066 LAN .319 104 382 122 21 2 19 66 42 11 36 97 11 Jose Ramirez 29175 CLE .333 15 12 4 0 1 0 5 0 0 2 2 0 Anthony Rendon 29018 WAS .265 98 351 93 23 1 7 40 35 5 31 69 1 Christopher Robinson 27724 SDN .167 8 12 2 0 0 1 1 3 0 0 3 0 Derrick Robinson 29003 CIN .255 102 192 49 7 3 0 21 8 1 18 44 4 Cameron Rupp 29192 PHI .308 4 13 4 1 0 0 1 2 0 1 4 0 Tony Sanchez 29092 PIT .233 22 60 14 4 0 2 9 5 2 3 14 0 Jonathan Schoop 29206 BAL .286 5 14 4 0 0 1 5 1 0 1 2 0 Marcus Semien 29185 CHA .261 21 69 18 4 0 2 7 7 0 1 22 2 Kyle Skipworth 29007 MIA .000 4 3 0 0 0 0 0 0 0 1 1 0 Neftali Soto 29045 CIN .000 13 12 0 0 0 0 0 0 1 0 6 0 Max Stassi 29157 HOU .286 3 7 2 0 0 0 0 1 1 0 2 0 Jesus Sucre 29051 SEA .192 8 26 5 0 0 0 1 3 0 2 1 0 Steve Susdorf 29127 PHI .143 3 7 1 1 0 0 1 0 0 0 1 0 Kensuke Tanaka 29109 SFN .267 15 30 8 0 0 0 4 2 0 4 3 2 Joseph Terdoslavich 29104 ATL .215 55 79 17 4 0 0 11 4 0 12 24 1 Wilfredo Tovar 29204 NYN .200 7 15 3 0 0 0 1 2 1 1 3 1 Henry Urrutia 29121 BAL .276 24 58 16 0 1 0 5 2 0 0 11 0 Jonathan Villar 29123 HOU .243 58 210 51 9 2 1 26 8 0 24 71 18 Zach Walters 29188 WAS .375 8 8 3 0 1 0 2 1 0 1 0 0 Logan Watkins 29139 CHN .211 27 38 8 1 0 0 2 0 0 3 14 0 Kolten Wong 29151 SLN .153 32 59 9 1 0 0 6 0 0 3 12 3 Christian Yelich 29124 MIA .288 62 240 69 12 1 4 34 16 1 31 66 10 Michael Zunino 29079 SEA .214 52 173 37 5 0 5 22 14 3 16 49 1
Name UID Tm G GS W L S ERA Inn H R ER BB K HR ----------------------- ----- --- --- -- -- -- -- ------ ----- --- --- --- --- --- -- Andrew Albers 29140 MIN 10 10 2 5 0 4.05 60.0 64 34 27 7 25 6 Jose Alvarez 29075 DET 14 6 1 5 0 5.82 38.7 42 26 25 16 31 7 Steve Ames 29130 MIA 4 0 0 1 0 4.50 4.0 6 2 2 2 4 0 Mike Belfiore 29207 BAL 1 0 0 0 0 13.50 1.3 3 2 2 1 0 2 Chad Bettis 29136 COL 16 8 1 3 0 5.64 44.7 55 34 28 20 30 6 Vic Black 29126 PIT 3 0 0 0 0 4.50 4.0 6 2 2 2 3 0 Vic Black 29126 NYN 15 0 3 0 1 3.46 13.0 11 5 5 4 12 1 Michael Blazek 29089 MIL 7 0 0 1 0 3.86 7.0 6 4 3 3 4 1 Michael Blazek 29089 SLN 11 0 0 0 0 6.97 10.3 10 8 8 10 10 2 Buddy Boshers 29148 ANA 25 0 0 0 0 4.70 15.3 13 8 8 8 13 0 Ryan Brasier 29035 ANA 7 0 0 0 0 2.00 9.0 7 2 2 4 7 1 Charles Brewer 29078 ARI 4 0 0 0 0 3.00 6.0 8 2 2 2 5 0 Drake Britton 29122 BOS 18 0 1 1 0 3.86 21.0 21 9 9 7 17 1 Hiram Burgos 29019 MIL 6 6 1 2 0 6.44 29.3 38 23 21 11 18 5 Keith Butler 29063 SLN 16 0 0 0 0 4.05 20.0 13 9 9 11 16 0 Cesar Cabral 29171 NYA 8 0 0 0 0 2.45 3.7 3 1 1 1 6 0 Arquimedes Caminero 29154 MIA 13 0 0 0 0 2.77 13.0 10 4 4 3 12 2 Simon Castro 29103 CHA 4 0 0 1 0 2.70 6.7 5 2 2 3 6 1 Kevin Chapman 29146 HOU 25 0 1 1 1 1.77 20.3 13 6 4 13 15 1 Nick Christiani 29161 CIN 3 0 0 0 0 2.25 4.0 2 1 1 2 1 1 Jose Cisnero 29162 HOU 28 0 2 2 0 4.12 43.7 49 23 20 22 41 5 Preston Claiborne 29037 NYA 44 0 0 2 0 4.11 50.3 51 23 23 14 42 7 Zachary Clark 29032 BAL 1 0 0 0 0 16.20 1.7 3 3 3 2 1 0 Paul Clemens 29006 HOU 35 5 4 7 0 5.40 73.3 82 48 44 26 49 16 Gerrit Cole 29077 PIT 19 19 10 7 0 3.22 117.3 109 43 42 28 100 7 Alex Colome 29061 TBA 3 3 1 1 0 2.25 16.0 14 8 4 9 12 2 Jarred Cosart 29116 HOU 10 10 1 1 0 1.95 60.0 46 15 13 35 33 3 Brandon Cumpton 29081 PIT 6 5 2 1 0 2.05 30.7 26 8 7 5 22 1 Erik Davis 29064 WAS 10 0 1 0 0 3.12 8.7 10 3 3 1 12 0 Eury De La Rosa 29117 ARI 19 0 0 1 0 7.36 14.7 13 13 12 5 16 5 Jose De La Torre 29040 BOS 7 0 0 0 0 6.35 11.3 10 8 8 10 15 2 Jorge De Leon 29144 HOU 11 0 0 1 0 5.40 10.0 12 7 6 7 6 1 Jose Dominguez 29102 LAN 9 0 0 0 0 2.16 8.3 11 3 2 3 4 0 Jake Dunning 29083 SFN 29 0 0 2 0 2.84 25.3 20 8 8 11 16 2 Chris Dwyer 29205 KCA 2 0 0 0 0 0.00 3.0 2 0 0 1 2 0 Robbie Erlin 29163 SDN 11 9 3 3 0 4.12 54.7 53 26 25 15 40 6 Jose Fernandez 29001 MIA 28 28 12 6 0 2.19 172.7 111 47 42 58 187 10 Josh Fields 28997 HOU 41 0 1 3 5 4.97 38.0 31 21 21 18 40 8 Brian Flynn 29182 MIA 4 4 0 2 0 8.50 18.0 27 17 17 13 15 4 Justin Freeman 29017 CIN 1 0 0 0 0 18.00 1.0 2 2 2 0 0 1 Kyuji Fujikawa 28986 CHN 12 0 1 1 2 5.25 12.0 11 7 7 2 14 1 Luis Garcia 29108 PHI 24 0 1 1 0 3.73 31.3 27 15 13 23 23 3 Onelkis Garcia 29195 LAN 3 0 0 0 0 13.50 1.3 1 2 2 4 1 1 John Gast 29042 SLN 3 3 2 0 0 5.11 12.3 11 7 7 5 8 1 Kevin Gausman 29050 BAL 20 5 3 5 0 5.66 47.7 51 30 30 13 49 8 Gonzalez Germen 29115 NYN 29 0 1 2 1 3.93 34.3 32 15 15 16 33 1 Kyle Gibson 29099 MIN 10 10 2 4 0 6.53 51.0 69 38 37 20 29 7 Sonny Gray 29113 OAK 12 10 5 3 0 2.67 64.0 51 22 19 20 67 4 Preston Guilmet 29107 CLE 4 0 0 0 0 10.13 5.3 8 6 6 3 1 0 David Hale 29196 ATL 2 2 1 0 0 .82 11.0 11 1 1 1 14 0 Donovan Hand 29054 MIL 31 7 1 5 0 3.69 68.3 71 29 28 21 37 10 Johnny Hellweg 29097 MIL 8 7 1 4 0 6.75 30.7 40 30 23 26 9 3 Heath Hembree 29177 SFN 9 0 0 0 0 0.00 7.7 4 0 0 2 12 0 David Holmberg 29165 ARI 1 1 0 0 0 7.36 3.7 6 3 3 3 0 0 Colt Hynes 29119 SDN 22 0 0 0 0 9.00 17.0 25 17 17 9 13 3 Phillip Irwin 29012 PIT 1 1 0 0 0 7.71 4.7 6 5 4 4 4 0 Erik Johnson 29184 CHA 5 5 3 2 0 3.25 27.7 32 16 10 11 18 5 Kris Johnson 29156 PIT 4 1 0 2 0 6.10 10.3 12 7 7 4 9 0 Taylor Jordan 29100 WAS 9 9 1 3 0 3.66 51.7 59 27 21 11 29 3 Donnie Joseph 29114 KCA 6 0 0 0 0 0.00 5.7 4 0 0 4 7 0 Nathan Karns 29056 WAS 3 3 0 1 0 7.50 12.0 17 11 10 6 11 5 Mike Kickham 29055 SFN 12 3 0 3 0 10.16 28.3 46 34 32 10 29 8 Ian Krol 29070 WAS 32 0 2 1 0 3.95 27.3 28 12 12 8 22 5 Bobby LaFromboise 29009 SEA 10 0 0 1 0 5.91 10.7 12 8 7 4 11 0 Matthew Langwell 29065 CLE 5 0 1 0 0 5.06 5.3 5 3 3 2 6 1 Matthew Langwell 29065 ARI 8 0 0 0 0 5.19 8.7 8 5 5 5 6 1 Chen Lee 29118 CLE 8 0 0 0 0 4.15 4.3 4 3 2 3 4 0 Charlie Leesman 29145 CHA 8 1 0 0 0 7.04 15.3 16 14 12 16 13 2 Chang Yong Lim 29189 CHN 6 0 0 0 0 5.40 5.0 6 3 3 7 5 0 Chia-Jen Lo 29131 HOU 19 0 0 3 2 4.19 19.3 14 9 9 13 16 2 Tyler Lyons 29048 SLN 12 8 2 4 0 4.75 53.0 49 29 28 16 43 5 Matthew Magill 29027 LAN 6 6 0 2 0 6.51 27.7 27 25 20 28 26 6 Michael Maness 29034 SLN 66 0 5 2 1 2.32 62.0 65 17 16 13 35 4 Brett Marshall 29041 NYA 3 0 0 0 0 4.50 12.0 13 6 6 7 7 3 Carlos Martinez 29036 SLN 21 1 2 1 1 5.08 28.3 31 16 16 9 24 1 David Martinez 29158 HOU 4 0 1 0 0 7.15 11.3 16 11 9 3 6 1 Ethan Martin 29137 PHI 15 8 2 5 0 6.08 40.0 42 27 27 26 47 9 Brandon Maurer 28999 SEA 22 14 5 8 0 6.30 90.0 114 66 63 27 70 16 T.J. McFarland 29000 BAL 38 1 4 1 0 4.22 74.7 83 37 35 28 58 7 Yoervis Medina 29016 SEA 63 0 4 6 1 2.91 68.0 49 22 22 40 71 5 Jimmy Nelson 29187 MIL 4 1 0 0 0 .90 10.0 2 1 1 5 8 0 Sean Nolin 29052 TOR 1 1 0 1 0 40.50 1.3 7 6 6 1 0 1 Vidal Nuno 29028 NYA 5 3 1 2 0 2.25 20.0 16 5 5 6 9 2 Brett Oberholtzer 29021 HOU 13 10 4 5 0 2.76 71.7 66 26 22 13 45 7 Edgar Olmos 29069 MIA 5 0 0 1 0 7.20 5.0 7 9 4 3 2 2 Joe Ortiz 28990 TEX 32 0 2 2 0 4.23 44.7 46 26 21 10 27 5 Curtis Partch 29076 CIN 14 0 0 1 0 6.17 23.3 17 16 16 17 16 8 James Paxton 29190 SEA 4 4 3 0 0 1.50 24.0 15 5 4 7 21 2 Jake Petricka 29159 CHA 16 0 1 1 0 3.26 19.3 20 7 7 10 10 0 Jon Pettibone 29022 PHI 18 18 5 4 0 4.04 100.3 109 50 45 38 66 9 Stolmy Pimentel 29181 PIT 5 0 0 0 0 1.93 9.3 6 4 2 2 9 0 Ryan Pressly 28998 MIN 49 0 3 3 0 3.87 76.7 71 37 33 27 49 5 J.C. Ramirez 29094 PHI 18 0 0 1 0 7.50 24.0 30 22 20 15 16 6 Cory Rasmus 29047 ATL 3 0 0 0 0 8.10 6.7 8 6 6 3 6 4 Cory Rasmus 29047 ANA 16 0 1 1 0 4.20 15.0 16 9 7 10 14 2 Evan Reed 29044 DET 16 0 0 1 0 4.24 23.3 28 16 11 8 17 2 Ryan Reid 29067 PIT 7 0 0 0 1 1.64 11.0 9 2 2 3 7 1 Scott Rice 28991 NYN 73 0 4 5 0 3.71 51.0 42 22 21 27 41 1 Andre Rienzo 29132 CHA 10 10 2 3 0 4.82 56.0 55 34 30 28 38 11 Tanner Roark 29142 WAS 14 5 7 1 0 1.51 53.7 38 11 9 11 40 1 Mauricio Robles 29176 PHI 3 0 0 0 0 1.93 4.7 7 3 1 3 6 0 Chaz Roe 28047 ARI 21 0 1 0 0 4.03 22.3 18 10 10 13 24 3 Enny Romero 29203 TBA 1 1 0 0 0 0.00 4.7 1 0 0 4 0 0 Bruce Rondon 29025 DET 30 0 1 2 1 3.45 28.7 28 11 11 11 30 2 Hector Rondon 28984 CHN 45 0 2 1 0 4.77 54.7 52 29 29 25 44 6 Zachary Rosscup 29180 CHN 10 0 0 0 0 1.35 6.7 3 1 1 7 7 1 Michael Roth 29011 ANA 15 1 1 1 0 7.20 20.0 24 16 16 6 17 0 Hyun-Jin Ryu 28987 LAN 30 30 14 8 0 3.00 192.0 182 67 64 49 154 15 Danny Salazar 29111 CLE 10 10 2 3 0 3.12 52.0 44 18 18 15 65 7 Matt Shoemaker 29201 ANA 1 1 0 0 0 0.00 5.0 2 0 0 2 5 0 Kevin Siegrist 29072 SLN 45 0 3 1 0 .45 39.7 17 2 2 18 50 1 Burch Smith 29039 SDN 10 7 1 3 0 6.44 36.3 39 26 26 21 46 9 Zeke Spruill 29091 ARI 6 2 0 2 0 5.56 11.3 17 11 7 5 9 3 Nicholas Tepesch 29005 TEX 19 17 4 6 0 4.84 93.0 100 53 50 27 76 12 Caleb Thielbar 29046 MIN 49 0 3 2 0 1.76 46.0 24 11 9 14 39 4 Michael Tonkin 29112 MIN 9 0 0 0 0 .79 11.3 9 6 1 3 10 0 Yordano Ventura 29199 KCA 3 3 0 1 0 3.52 15.3 13 6 6 6 11 3 Michael Wacha 29058 SLN 15 9 4 1 0 2.78 64.7 52 20 20 19 65 5 Taijuan Walker 29167 SEA 3 3 1 0 0 3.60 15.0 11 7 6 4 12 0 Daniel Webb 29183 CHA 9 0 0 0 0 3.18 11.3 9 4 4 4 10 0 Allen Webster 29020 BOS 8 7 1 2 0 8.60 30.3 37 30 29 18 23 7 Duke Welker 29093 PIT 2 0 0 0 0 0.00 1.3 0 0 0 0 1 0 Zack Wheeler 29084 NYN 17 17 7 5 0 3.42 100.0 90 42 38 46 84 10 Alex Wilson 29008 BOS 26 0 1 1 0 4.88 27.7 34 16 15 14 22 0 Chris Withrow 29080 LAN 26 0 3 0 1 2.60 34.7 20 10 10 13 43 5 Alex Wood 29062 ATL 31 11 3 3 0 3.13 77.7 76 29 27 27 77 3 Rob Wooten 29128 MIL 27 0 3 1 0 3.90 27.7 27 12 12 8 18 1 Brandon Workman 29110 BOS 20 3 6 3 0 4.97 41.7 44 23 23 15 47 5 Steven Wright 29013 BOS 4 1 2 0 0 5.40 13.3 12 8 8 9 10 0 Josh Zeid 29129 HOU 25 0 0 1 1 3.90 27.7 26 12 12 12 24 3]]>
Name UID Tm AVG G AB H 2B 3B HR R RBI HBP BB K SB ----------------------- ----- --- ----- --- --- --- -- -- -- --- --- --- --- --- --- Matt Adams 28850 SLN .244 27 86 21 6 0 2 8 13 0 5 24 0 Norichika Aoki 28791 MIL .288 151 520 150 37 4 10 81 50 13 43 55 30 Xavier Avery 28840 BAL .223 32 94 21 6 1 1 14 6 0 11 23 6 Brandon Barnes 28930 HOU .204 43 98 20 3 0 1 8 7 1 5 29 1 Quintin Berry 28855 DET .258 94 291 75 10 6 2 44 29 7 25 80 21 Jeff Bianchi 28896 MIL .188 33 69 13 2 0 3 8 9 0 4 13 0 Rob Brantly 28935 MIA .290 31 100 29 8 0 3 14 8 0 13 16 1 Kole Calhoun 28853 ANA .174 21 23 4 1 0 0 2 1 0 2 6 1 Adrian Cardenas 28103 CHN .183 45 60 11 6 0 0 5 2 0 7 13 0 Alex Castellanos 28862 LAN .174 16 23 4 0 1 1 3 3 1 0 8 0 Yoenis Cespedes 28789 OAK .292 129 487 142 25 5 23 70 82 7 43 102 16 Michael Costanzo 27552 CIN .056 17 18 1 0 0 0 0 2 0 1 10 0 Charlie Culberson 28842 SFN .136 6 22 3 0 0 0 0 1 0 0 7 0 Jordan Danks 28875 CHA .224 50 67 15 1 0 1 12 4 0 6 16 3 Juan Diaz 28857 CLE .267 5 15 4 0 0 0 4 0 1 1 5 0 Brian Dozier 28834 MIN .234 84 316 74 11 1 6 33 33 1 16 58 9 Adam Eaton 28962 ARI .259 22 85 22 3 2 2 19 5 3 14 15 2 Jake Elmore 28933 ARI .191 30 68 13 4 0 0 1 7 0 5 6 0 Luis Exposito 28831 BAL .056 9 18 1 0 0 0 2 0 0 3 5 0 Irving Falu 28833 KCA .341 24 85 29 6 1 0 14 7 0 4 9 0 Ryan Flaherty 28802 BAL .216 77 153 33 2 1 6 15 19 3 6 43 1 Freddy Galvis 28790 PHI .226 58 190 43 15 1 3 14 24 0 7 29 0 Avisail Garcia 28948 DET .319 23 47 15 0 0 0 7 3 1 3 10 0 Mauro Gomez 28841 BOS .275 37 102 28 5 2 2 14 17 0 8 26 0 Yan Gomes 28847 TOR .204 43 98 20 4 0 4 9 13 3 6 32 0 Marwin Gonzalez 28801 HOU .234 80 205 48 13 0 2 21 12 0 13 29 3 Anthony Gose 28903 TOR .223 56 166 37 7 3 1 25 11 2 17 59 15 Tyler Graham 28975 ARI .000 10 2 0 0 0 0 1 0 0 0 2 0 Yasmani Grandal 28865 SDN .297 60 192 57 7 1 8 28 37 1 31 39 0 Didi Gregorius 28964 CIN .300 8 20 6 0 0 0 1 2 0 0 5 0 Matt Hague 28798 PIT .229 30 70 16 2 0 0 5 7 1 3 14 1 Bryce Harper 28824 WAS .270 139 533 144 26 9 22 98 59 2 56 120 18 Adeiny Hechavarria 28924 TOR .254 41 126 32 8 0 2 10 15 1 4 32 0 Gorkys Hernandez 28851 PIT .083 25 24 2 0 0 0 2 2 1 1 5 2 Gorkys Hernandez 28851 MIA .212 45 132 28 2 3 3 16 11 2 12 37 5 Chris Herrmann 28973 MIN .056 7 18 1 0 0 0 0 1 0 1 5 0 Elian Herrera 28845 LAN .251 67 187 47 10 1 1 26 17 2 23 50 4 L.J. Hoes 28979 BAL .000 2 1 0 0 0 0 0 0 0 0 0 0 Bryan Holaday 28870 DET .250 6 12 3 1 0 0 3 0 0 0 2 0 Brock Holt 28952 PIT .292 24 65 19 2 1 0 6 3 0 4 14 0 Brett Jackson 28926 CHN .175 44 120 21 6 1 4 14 9 0 22 59 0 Ryan Jackson 28932 SLN .118 13 17 2 0 0 0 2 0 0 1 3 0 Luis Jimenez 27210 SEA .059 7 17 1 0 0 0 0 0 0 1 4 0 Munenori Kawasaki 28794 SEA .192 61 104 20 1 0 0 13 7 1 8 18 2 Erik Komatsu 28797 SLN .211 15 19 4 0 0 0 3 0 0 2 2 0 Erik Komatsu 28797 MIN .219 15 32 7 0 0 0 2 1 0 4 3 0 Blake Lalli 28849 CHN .133 6 15 2 0 0 0 1 2 0 1 3 0 Sandy Leon 28843 WAS .267 12 30 8 2 0 0 2 2 2 4 11 0 Steve Lerud 28947 PHI .200 3 10 2 0 0 0 1 0 0 0 2 0 Che-Hsuan Lin 28813 BOS .250 9 12 3 0 0 0 1 0 0 0 5 0 David Lough 28949 KCA .237 20 59 14 2 1 0 9 2 1 4 9 1 Zach Lutz 28820 NYN .091 7 11 1 0 0 0 1 0 0 0 5 0 Manny Machado 28931 BAL .262 51 191 50 8 3 7 24 26 0 9 38 2 Joe Mahoney 28895 BAL .000 2 4 0 0 0 0 0 0 0 0 0 0 Starling Marte 28913 PIT .257 47 167 43 3 6 5 18 17 3 8 50 12 Kevin Mattison 28839 MIA .000 3 5 0 0 0 0 0 0 0 0 2 0 Matt McBride 28925 COL .205 31 78 16 2 0 2 8 11 1 1 17 0 Jordy Mercer 28860 PIT .210 42 62 13 5 1 1 7 5 1 4 14 0 Melky Mesa 28981 NYA .500 3 2 1 0 0 0 0 1 0 0 0 0 Will Middlebrooks 28830 BOS .288 75 267 77 14 0 15 34 54 3 13 70 4 Tyler Moore 28826 WAS .263 75 156 41 9 0 10 20 29 1 14 46 3 Thomas Neal 28956 CLE .217 9 23 5 1 0 0 2 2 1 0 6 0 Kris Negron 28876 CIN .250 4 4 1 0 0 0 2 0 0 1 2 0 Kirk Nieuwenhuis 28808 NYN .252 91 282 71 12 1 7 40 28 2 25 98 4 Derek Norris 28885 OAK .201 60 209 42 8 1 7 19 34 1 21 66 5 Mike Olt 28919 TEX .152 16 33 5 1 0 0 2 5 0 5 13 1 Rafael Ortega 28982 COL .500 2 4 2 0 0 0 0 0 1 1 2 1 Tyler Pastornicky 28788 ATL .243 76 169 41 6 1 2 21 13 1 10 32 2 Francisco Peguero 28944 SFN .188 17 16 3 0 0 0 6 0 0 0 7 3 Eury Perez 28951 WAS .200 13 5 1 0 0 0 3 0 0 0 0 3 Hernan Perez 28877 DET .500 2 2 1 0 0 0 1 0 0 0 0 0 Denis Phipps 28957 CIN .300 8 10 3 1 0 1 4 2 0 1 4 0 A.J. Pollock 28814 ARI .247 31 81 20 4 1 2 8 8 0 9 11 1 Jurickson Profar 28953 TEX .176 9 17 3 2 0 1 2 2 0 0 4 0 Clint Robinson 28873 KCA .000 4 4 0 0 0 0 0 0 0 0 2 0 Eddy Rodriguez 28918 SDN .200 2 5 1 0 0 1 1 1 0 2 3 0 Henry Rodriguez 28955 CIN .214 12 14 3 1 0 0 0 2 0 2 2 0 Darin Ruf 28971 PHI .333 12 33 11 2 1 3 4 10 0 2 12 0 Josh Rutledge 28898 COL .274 73 277 76 20 5 8 37 37 4 9 54 7 Jean Segura 28912 ANA .000 1 3 0 0 0 0 0 0 0 0 2 0 Jean Segura 28912 MIL .264 44 148 39 4 3 0 19 14 0 13 21 7 Moises Sierra 28916 TOR .224 49 147 33 4 0 6 14 15 2 8 44 1 Andrelton Simmons 28866 ATL .289 49 166 48 8 2 3 17 19 1 12 21 1 Donovan Solano 28852 MIA .295 93 285 84 11 3 2 29 28 2 21 58 7 Jhonatan Solano 28859 WAS .314 12 35 11 3 0 2 6 6 0 2 5 1 Ali Solis 28974 SDN .000 5 4 0 0 0 0 0 0 0 0 2 0 Carlos Triunfel 28967 SEA .227 10 22 5 2 0 0 2 3 0 1 4 0 Jordany Valdespin 28818 NYN .241 94 191 46 9 1 8 28 26 2 10 44 10 Scott Van Slyke 28838 LAN .167 27 54 9 2 0 2 4 7 0 2 14 1 Josh Vitters 28927 CHN .121 36 99 12 2 0 2 7 5 2 7 33 2 Stephen Vogt 28805 TBA .000 18 25 0 0 0 0 0 0 0 2 2 0 Ryan Wheeler 28909 ARI .239 50 109 26 6 1 1 11 10 0 9 22 1
Name UID Tm G GS W L S ERA Inn H R ER BB K HR ----------------------- ----- --- --- -- -- -- -- ------ ----- --- --- --- --- --- -- Cody Allen 28908 CLE 27 0 0 1 0 3.72 29.0 29 12 12 15 27 2 Chris Archer 28884 TBA 6 4 1 3 0 4.60 29.3 23 17 15 13 36 3 Phillippe Aumont 28942 PHI 18 0 0 1 2 3.68 14.7 10 6 6 9 14 0 Luis Avilan 28899 ATL 31 0 1 0 0 2.00 36.0 27 9 8 10 33 1 Scott Barnes 28861 CLE 16 0 0 0 0 4.26 19.0 17 9 9 7 16 1 Trevor Bauer 28890 ARI 4 4 1 2 0 6.06 16.3 14 13 11 13 17 2 Jeff Beliveau 28911 CHN 22 0 1 0 0 4.58 17.7 21 9 9 12 17 5 Brad Boxberger 28879 SDN 24 0 0 0 0 2.60 27.7 22 12 8 18 33 3 Barret Browning 28060 SLN 22 0 1 3 0 5.12 19.3 18 11 11 7 11 2 Tyson Brummett 28983 PHI 1 0 0 0 0 0.00 .7 2 0 0 0 2 0 Dylan Bundy 28977 BAL 2 0 0 0 0 0.00 1.7 1 0 0 1 0 0 Cory Burns 28923 SDN 17 0 0 1 0 5.50 18.0 26 11 11 10 18 1 Alberto Cabrera 28917 CHN 25 0 1 1 0 5.40 21.7 16 15 13 18 27 1 Edwar Cabrera 28889 COL 2 2 0 2 0 11.12 5.7 9 9 7 7 5 3 Carter Capps 28920 SEA 18 0 0 0 0 3.96 25.0 25 11 11 11 28 0 Dave Carpenter 28811 ANA 28 0 1 2 0 4.76 39.7 42 21 21 17 28 6 Robert III Carson 28848 NYN 17 0 0 0 0 4.73 13.3 13 7 7 4 5 2 Lendy Castillo 28803 CHN 13 0 0 1 0 7.88 16.0 24 16 14 12 13 2 Jaye Chapman 28961 CHN 14 0 0 1 0 3.75 12.0 8 5 5 10 12 0 Wei-Yin Chen 28796 BAL 32 32 12 11 0 4.02 192.7 186 97 86 57 154 29 Tony Cingrani 28968 CIN 3 0 0 0 0 1.80 5.0 4 1 1 2 9 1 Tyler Cloyd 28946 PHI 6 6 2 2 0 4.91 33.0 33 18 18 7 30 8 Patrick Corbin 28827 ARI 22 17 6 8 1 4.54 107.0 117 56 54 25 86 14 Evan Crawford 28812 TOR 10 0 0 0 0 6.75 8.0 10 6 6 4 5 3 Casey Crosby 28863 DET 3 3 1 1 0 9.49 12.3 15 13 13 11 9 2 Rhiner Cruz 28804 HOU 52 0 1 1 0 6.05 55.0 65 38 37 29 46 8 Yu Darvish 28792 TEX 29 29 16 9 0 3.90 191.3 156 89 83 89 221 14 Cole De Vries 28856 MIN 17 16 5 5 0 4.11 87.7 88 48 40 18 58 16 Jake Diekman 28844 PHI 32 0 1 1 0 3.95 27.3 25 17 12 20 35 1 Sean Doolittle 28869 OAK 44 0 2 1 1 3.04 47.3 40 18 16 11 60 3 Darin Downs 28893 DET 18 0 2 1 0 3.48 20.7 18 8 8 9 20 1 Sam Dyson 28894 TOR 2 0 0 0 0 40.50 .7 4 3 3 2 1 0 Josh Edgin 28897 NYN 34 0 1 2 0 4.56 25.7 19 14 13 10 30 5 Jeurys Familia 28960 NYN 8 1 0 0 0 5.84 12.3 10 8 8 9 10 0 Chuckie Fick 28858 SLN 2 0 0 0 0 5.40 1.7 3 1 1 1 0 0 Chuckie Fick 28858 HOU 18 0 0 1 0 4.30 23.0 24 13 11 17 17 4 Stephen Fife 28905 LAN 5 5 0 2 0 2.70 26.7 25 8 8 12 20 2 Pedro Figueroa 28816 OAK 19 0 0 0 0 3.32 21.7 16 9 8 15 14 2 Wilmer Font 28976 TEX 3 0 0 0 0 9.00 2.0 0 2 2 4 1 0 Sam Freeman 28864 SLN 24 0 0 2 0 5.40 20.0 17 13 12 10 18 2 Christian Friedrich 28837 COL 16 16 5 8 0 6.17 84.7 102 61 58 30 74 14 Christian Garcia 27594 WAS 13 0 0 0 0 2.13 12.7 8 3 3 2 15 2 Steven Geltz 28936 ANA 2 0 0 0 0 4.50 2.0 2 1 1 3 1 0 Miguel Gonzalez 27603 BAL 18 15 9 4 0 3.25 105.3 92 38 38 35 77 13 A.J. Griffin 28886 OAK 15 15 7 1 0 3.06 82.3 74 29 28 19 64 10 Justin Grimm 28881 TEX 5 2 1 1 0 9.00 14.0 22 14 14 3 13 1 Will Harris 28089 COL 20 0 1 1 0 8.15 17.7 27 18 16 6 19 3 Matt Harvey 28914 NYN 10 10 3 5 0 2.73 59.3 42 19 18 26 70 5 Deunte Heath 28950 CHA 3 0 0 0 0 4.50 2.0 1 1 1 1 1 1 Jeremy Hefner 28819 NYN 26 13 4 7 0 5.09 93.7 110 54 53 18 62 9 Jim Henderson 28915 MIL 36 0 1 3 3 3.52 30.7 26 12 12 13 45 1 Pedro Hernandez 28907 CHA 1 1 0 1 0 18.00 4.0 12 8 8 1 2 3 J.J. Hoover 28821 CIN 28 0 1 0 1 2.05 30.7 17 7 7 13 31 2 Drew Hutchison 28815 TOR 11 11 5 3 0 4.60 58.7 59 31 30 20 49 8 Hisashi Iwakuma 28823 SEA 30 16 9 5 2 3.16 125.3 117 49 44 43 101 17 Chad Jenkins 28928 TOR 13 3 1 3 0 4.50 32.0 32 16 16 11 16 5 Dan Jennings 28828 MIA 22 0 1 0 0 1.89 19.0 18 5 4 11 8 2 Steve Johnson 28902 BAL 12 4 4 0 0 2.11 38.3 23 9 9 18 46 4 Nate Jones 28800 CHA 65 0 8 0 0 2.39 71.7 67 19 19 32 65 4 Casey Kelly 28945 SDN 6 6 2 3 0 6.21 29.0 39 23 20 10 26 5 Joe Kelly 28880 SLN 24 16 5 7 0 3.53 107.0 112 50 42 36 75 10 Dallas Keuchel 28882 HOU 16 16 3 8 0 5.27 85.3 93 56 50 39 38 14 Tom Koehler 28966 MIA 8 1 0 1 0 5.40 13.3 15 8 8 2 13 4 Tom Layne 28934 SDN 26 0 2 0 2 3.24 16.7 9 6 6 3 25 0 Aaron Loup 28900 TOR 33 0 0 2 0 2.64 30.7 26 10 9 2 21 0 Lucas Luetge 28793 SEA 63 0 2 2 2 3.98 40.7 37 20 18 24 38 3 Jean Machi 28958 SFN 8 0 0 0 0 6.75 6.7 7 5 5 1 4 2 Nick Maronde 28954 ANA 12 0 0 0 0 1.50 6.0 6 1 1 3 7 0 Collin McHugh 28943 NYN 8 4 0 4 0 7.59 21.3 27 22 18 8 17 5 Kyle McPherson 28938 PIT 10 3 0 2 0 2.73 26.3 24 8 8 7 21 3 Miles Mikolas 28832 SDN 25 0 2 1 0 3.62 32.3 32 15 13 15 23 4 Shelby Miller 28963 SLN 6 1 1 0 0 1.32 13.7 9 2 2 4 16 0 D.J. Mitchell 28829 NYA 4 0 0 0 0 3.86 4.7 7 2 2 3 2 1 Bryan Morris 28972 PIT 5 0 0 0 0 1.80 5.0 2 2 1 2 6 0 Jake Odorizzi 28978 KCA 2 2 0 1 0 4.91 7.3 8 4 4 4 4 1 Brian Omogrosso 28086 CHA 17 0 0 0 0 2.57 21.0 20 6 6 9 18 3 Jose Ortega 28874 DET 2 0 0 0 0 3.38 2.7 3 1 1 1 4 1 Dan Otero 28807 SFN 12 0 0 0 0 5.84 12.3 19 11 8 2 8 0 Blake Parker 28846 CHN 7 0 0 0 0 6.00 6.0 10 7 4 5 6 3 Wily Peralta 28822 MIL 6 5 2 1 0 2.48 29.0 24 8 8 11 23 0 Martin Perez 28888 TEX 12 6 1 4 0 5.45 38.0 47 26 23 15 25 3 David Phelps 28806 NYA 33 11 4 4 0 3.34 99.7 81 38 37 38 96 14 Stu Pomeranz 28835 BAL 3 0 0 0 0 3.00 6.0 7 2 2 1 3 1 Stephen Pryor 28867 SEA 26 0 3 1 0 3.91 23.0 22 13 10 13 27 5 Luke Putkonen 28825 DET 12 0 0 2 1 3.94 16.0 19 7 7 8 10 0 Jose Quintana 28836 CHA 25 22 6 6 0 3.76 136.3 142 62 57 42 81 14 Brooks Raley 28929 CHN 5 5 1 2 0 8.14 24.3 33 23 22 11 16 7 Erasmo Ramirez 28795 SEA 16 8 1 3 0 3.36 59.0 47 26 22 12 48 6 Elvin Ramirez 28868 NYN 20 0 0 1 0 5.48 21.3 24 13 13 20 22 1 A.J. Ramos 28959 MIA 11 0 0 0 0 3.86 9.3 8 4 4 4 13 2 Todd Redmond 27716 CIN 1 1 0 1 0 10.80 3.3 7 4 4 5 2 1 Tyler Robertson 28108 MIN 40 0 2 2 0 5.40 25.0 21 16 15 14 26 4 Paco Rodriguez 28969 LAN 11 0 0 1 0 1.35 6.7 3 1 1 4 6 0 B.J. Rosenberg 28878 PHI 22 1 1 2 0 6.12 25.0 18 17 17 14 24 4 Trevor Rosenthal 28906 SLN 19 0 0 2 0 2.78 22.7 14 7 7 7 25 2 Robbie Ross 28799 TEX 58 0 6 0 0 2.22 65.0 55 21 16 23 47 3 Chris Rusin 28939 CHN 7 7 2 3 0 6.37 29.7 38 22 21 11 21 4 Rob Scahill 28970 COL 6 0 0 0 0 1.04 8.7 7 1 1 3 4 0 Tanner Scheppers 28871 TEX 39 0 1 1 1 4.45 32.3 47 18 16 9 30 6 Leyson Septimo 28891 CHA 21 0 0 2 0 5.02 14.3 8 8 8 6 14 3 Tyler Skaggs 28940 ARI 6 6 1 3 0 5.83 29.3 30 20 19 13 21 6 Will Smith 28854 KCA 16 16 6 9 0 5.32 89.7 111 54 53 33 59 12 Drew Smyly 28809 DET 23 18 4 3 0 3.99 99.3 93 49 44 33 94 12 Miguel Socolovich 28901 CHN 6 0 0 0 0 4.50 6.0 4 3 3 3 6 1 Miguel Socolovich 28901 BAL 6 0 0 0 0 6.97 10.3 11 8 8 6 6 2 Mickey Storey 28922 HOU 26 0 0 1 0 3.86 30.3 27 14 13 10 34 2 Daniel Straily 28921 OAK 7 7 2 1 0 3.89 39.3 36 19 17 16 32 11 Andrew Taylor 28980 ANA 3 0 0 0 0 11.57 2.3 3 3 3 4 0 0 Tyler Thornburg 28883 MIL 8 3 0 0 0 4.50 22.0 24 11 11 7 20 8 Shawn Tolleson 28872 LAN 40 0 3 1 0 4.30 37.7 30 19 18 20 39 4 Ryan Verdugo 28904 KCA 1 1 0 1 0 32.40 1.7 8 6 6 2 2 1 Pedro Villarreal 28965 CIN 1 0 0 0 0 0.00 1.0 0 0 0 0 1 0 Nick Vincent 28887 SDN 27 0 2 0 0 1.71 26.3 19 5 5 7 28 2 Josh Wall 28910 LAN 7 0 1 0 0 4.76 5.7 3 3 3 1 4 1 Adam Warren 28892 NYA 1 1 0 0 0 23.14 2.3 8 6 6 2 1 2 Thad Weber 28817 DET 2 0 0 1 0 9.00 4.0 10 4 4 2 1 0 Andrew Werner 28941 SDN 8 8 2 3 0 5.58 40.3 45 26 25 14 35 5 Joe Wieland 28810 SDN 5 5 0 4 0 4.55 27.7 26 16 14 9 24 5 Justin Wilson 28937 PIT 8 0 0 0 0 1.93 4.7 10 1 1 3 7 0]]>
Name UID Tm AVG G AB H 2B 3B HR R RBI HBP BB K SB ----------------------- ----- --- ----- --- --- --- -- -- -- --- --- --- --- --- --- Dustin Ackley 28658 SEA .273 90 333 91 16 7 6 39 36 0 40 79 6 Ryan Adams 28618 BAL .281 29 89 25 4 0 0 9 7 1 6 25 0 Jose Altuve 28682 HOU .276 57 221 61 10 1 2 26 12 2 5 29 7 Alexi Amarista 28594 ANA .154 23 52 8 3 1 0 2 5 0 2 8 0 Matt Angle 28677 BAL .177 31 79 14 4 0 1 12 7 1 12 13 11 Brandon Belt 28564 SFN .225 63 187 42 6 1 9 21 18 2 20 57 3 Joe Benson 28752 MIN .239 21 71 17 6 1 0 3 2 0 3 21 2 Charlie Blackmon 28644 COL .255 27 98 25 1 0 1 9 8 0 3 8 5 Andrew Brown 28654 SLN .182 11 22 4 1 0 0 1 3 0 0 8 0 Corey Brown 28757 WAS .000 3 3 0 0 0 0 0 0 0 0 2 0 Tony Campana 28615 CHN .259 95 143 37 3 0 1 24 6 1 8 30 24 Russell Canzler 28777 TBA .333 3 3 1 0 0 0 0 1 0 1 1 0 Matt Carpenter 28639 SLN .067 7 15 1 1 0 0 0 0 0 4 4 0 Ezequiel Carrera 28617 CLE .243 68 202 49 8 3 0 27 14 1 16 35 10 Adron Chambers 28762 SLN .375 18 8 3 0 1 0 2 4 0 0 1 0 Robinson Chirinos 28679 TBA .218 20 55 12 2 0 1 4 7 0 5 13 0 Lonnie Chisenhall 28664 CLE .255 66 212 54 13 0 7 27 22 1 8 49 1 Steve Clevenger 28785 CHN .250 2 4 1 1 0 0 1 0 1 0 0 0 Jose Constanza 27549 ATL .303 42 109 33 1 1 2 21 10 0 6 14 7 David Cooper 28596 TOR .211 27 71 15 7 0 2 9 12 1 7 14 0 Collin Cowgill 28688 ARI .239 36 92 22 3 0 1 8 9 0 8 28 4 Zack Cozart 28671 CIN .324 11 37 12 0 0 2 6 3 0 0 6 0 Brandon Crawford 28628 SFN .204 66 196 40 5 2 3 22 21 0 23 31 1 Tony Cruz 28623 SLN .262 38 65 17 5 0 0 8 6 1 6 13 0 Chase D'Arnaud 28662 PIT .217 48 143 31 6 2 0 17 6 1 4 36 12 James Darnell 28702 SDN .222 18 45 10 2 0 1 2 7 0 5 7 1 Blake Davis 28660 BAL .254 25 59 15 3 1 1 6 6 0 6 13 1 Ivan De Jesus 28572 LAN .188 17 32 6 0 0 0 2 1 0 2 11 0 Brian Dinkelman 28638 MIN .301 23 73 22 1 0 0 5 4 1 4 14 2 Andy Dirks 28607 DET .251 78 219 55 13 0 7 34 28 3 11 36 5 Matt Dominguez 28746 FLO .244 17 45 11 4 0 0 2 2 1 2 8 0 Brad Emaus 28569 NYN .162 14 37 6 0 0 0 2 1 1 4 9 0 Eduardo Escobar 28742 CHA .286 9 7 2 0 0 0 0 0 0 0 1 0 Eric Farris 28689 MIL .000 1 1 0 0 0 0 0 0 0 0 0 0 Tim Federowicz 28767 LAN .154 7 13 2 0 0 0 0 1 1 2 4 0 Thomas Field 28770 COL .271 16 48 13 0 0 0 4 3 0 3 14 0 Pedro Florimon Jr. 28764 BAL .125 4 8 1 1 0 0 1 2 0 1 6 0 Logan Forsythe 28601 SDN .213 62 150 32 9 1 0 12 12 3 12 33 3 Todd Frazier 28621 CIN .232 41 112 26 5 0 6 17 15 2 7 27 1 Eric Fryer 28663 PIT .269 10 26 7 0 0 0 5 0 0 3 7 1 Cole Garner 27598 COL .222 4 9 2 0 0 0 1 3 0 1 6 0 Johnny Giavotella 28700 KCA .247 46 178 44 9 4 2 20 21 1 6 32 5 Paul Goldschmidt 28694 ARI .250 48 156 39 9 1 8 28 26 0 20 53 4 Hector Gomez 28779 COL .333 2 6 2 0 0 0 1 0 0 1 2 0 Dee Gordon 28641 LAN .304 56 224 68 9 2 0 34 11 0 7 27 24 Taylor Green 28721 MIL .270 20 37 10 3 0 0 2 1 0 0 6 0 Brandon Guyer 28604 TBA .195 15 41 8 1 0 2 7 3 0 1 9 0 Josh Harrison 28633 PIT .272 65 195 53 13 2 1 21 16 0 3 24 4 Jerad Head 28719 CLE .125 10 24 3 1 0 0 2 1 1 0 5 1 Eric Hosmer 28602 KCA .293 128 523 153 27 3 19 66 78 1 34 82 11 Kyle Hudson 28737 BAL .143 14 28 4 0 0 0 3 2 0 0 6 2 Cedric Hunter 28097 SDN .250 6 4 1 0 0 0 1 0 0 1 0 0 Jose Iglesias 28605 BOS .333 10 6 2 0 0 0 3 0 0 0 2 0 Jason Kipnis 28683 CLE .272 36 136 37 9 1 7 24 19 2 11 34 5 Pete Kozma 28616 SLN .176 16 17 3 1 0 0 2 1 0 4 4 0 Brandon Laird 28684 NYA .190 11 21 4 0 0 0 3 1 0 3 4 0 Ryan Lavarnway 28710 BOS .231 17 39 9 2 0 2 5 8 0 4 10 0 Brett Lawrie 28696 TOR .293 43 150 44 8 4 9 26 25 3 16 31 7 D.J. LeMahieu 28631 CHN .250 37 60 15 2 0 0 3 4 0 1 12 0 Alex Liddi 28759 SEA .225 15 40 9 3 0 3 7 6 1 3 17 1 Steve Lombardozzi 28755 WAS .194 13 31 6 1 0 0 3 1 0 1 4 0 Martin Maldonado 28734 MIL .000 3 1 0 0 0 0 0 0 0 0 1 0 Chris Marrero 28100 WAS .248 31 109 27 5 0 0 6 10 1 4 27 0 J.D. Martinez 28690 HOU .274 53 208 57 13 0 6 29 35 2 13 48 0 Luis Martinez 28675 SDN .203 22 59 12 1 1 1 7 10 1 8 14 1 Leonys Martin 28729 TEX .375 8 8 3 1 0 0 2 0 0 0 1 0 Michael Martinez 28573 PHI .196 88 209 41 5 2 3 25 24 0 18 35 3 Darin Mastroianni 28716 TOR .000 1 2 0 0 0 0 0 0 0 0 1 0 Devin Mesoraco 28733 CIN .180 18 50 9 3 0 2 5 6 0 3 10 0 Jesus Montero 28726 NYA .328 18 61 20 4 0 4 9 12 1 7 17 0 Jeremy Moore 28731 ANA .125 8 8 1 0 0 0 3 0 0 0 2 0 Mike Moustakas 28648 KCA .263 89 338 89 18 1 5 26 30 1 22 51 2 Efren Navarro 28732 ANA .200 8 10 2 1 0 0 1 0 0 1 1 0 Tsuyoshi Nishioka 28565 MIN .226 68 221 50 5 0 0 14 19 1 15 43 2 Jordan Pacheco 28754 COL .286 21 84 24 1 0 2 5 14 1 3 9 0 Jimmy Paredes 28693 HOU .286 46 168 48 8 2 2 16 18 0 9 47 5 Chris Parmelee 28753 MIN .355 21 76 27 6 0 4 8 14 0 12 13 0 Andrew Parrino 28717 SDN .182 24 44 8 1 0 0 3 4 1 9 17 1 Carlos Peguero 28588 SEA .196 46 143 28 3 2 6 14 19 3 8 54 0 Salvador Perez 28706 KCA .331 39 148 49 8 2 3 20 21 1 7 20 0 Cord Phelps 28646 CLE .155 35 71 11 2 1 1 10 6 0 8 17 1 Brett Pill 28749 SFN .300 15 50 15 3 2 2 7 9 0 2 8 0 Manuel Pina 28695 KCA .214 4 14 3 2 0 0 2 0 0 1 2 0 Anthony Recker 28714 OAK .176 5 17 3 1 0 0 3 0 0 4 7 0 Antoan Richardson 28744 ATL .500 9 4 2 0 0 0 2 0 0 0 0 1 Anthony Rizzo 28649 SDN .141 49 128 18 8 1 1 9 9 4 21 46 2 Trayvon Robinson 28698 SEA .210 44 143 30 12 0 2 12 14 0 8 61 1 Josh Rodriguez 28059 PIT .083 7 12 1 0 0 0 1 1 1 1 8 0 Austin Romine 28771 NYA .158 9 19 3 0 0 0 2 0 0 1 5 0 Wilin Rosario 28748 COL .204 16 54 11 3 1 3 6 8 0 2 20 0 Hector Sanchez 28676 SFN .258 13 31 8 2 0 0 0 1 0 3 6 0 Jerry Sands 28587 LAN .253 61 198 50 15 0 4 20 26 1 25 51 3 Dave Sappelt 28701 CIN .243 38 107 26 8 0 0 14 5 0 7 17 1 Joshua Satin 28736 NYN .200 15 25 5 1 0 0 3 2 1 1 11 0 Logan Schafer 28735 MIL .333 8 3 1 0 0 0 1 0 0 1 1 0 Kyle Seager 28670 SEA .258 53 182 47 13 0 3 22 13 2 13 36 3 Justin Sellers 28707 LAN .203 36 123 25 9 0 1 20 13 2 12 21 1 J.B. Shuck 28699 HOU .272 37 81 22 2 1 0 9 3 0 11 7 2 Nate Spears 28751 BOS .000 3 4 0 0 0 0 0 0 0 0 1 0 Michael Taylor 28738 OAK .200 11 30 6 0 0 1 4 1 0 5 11 0 Blake Tekotte 28624 SDN .176 19 34 6 1 1 0 1 1 0 4 21 2 Eric Thames 28612 TOR .262 95 362 95 24 5 12 58 37 5 23 88 2 Rene Tosoni 28595 MIN .203 60 172 35 7 1 5 20 22 3 14 42 0 Mike Trout 28673 ANA .220 40 123 27 6 0 5 20 16 2 9 30 4 Jemile Weeks 28643 OAK .303 97 406 123 26 8 2 50 36 4 21 62 22 Michael Wilson 27782 SEA .148 8 27 4 1 0 0 0 3 0 1 7 0 Matthew Young 27787 ATL .208 20 48 10 1 0 0 4 1 0 4 6 0
Name UID Tm G GS W L S ERA Inn H R ER BB K HR ----------------------- ----- --- --- -- -- -- -- ------ ----- --- --- --- --- --- -- Juan Abreu 28715 HOU 7 0 0 0 0 2.70 6.7 6 2 2 3 12 1 Nathan Adcock 28575 KCA 24 3 1 1 1 4.62 60.3 63 34 31 26 36 5 Al Alburquerque 28582 DET 41 0 6 1 0 1.87 43.3 21 9 9 29 67 0 Henderson Alvarez 28703 TOR 10 10 1 3 0 3.53 63.7 64 26 25 8 40 8 Dylan Axelrod 28758 CHA 4 3 1 0 0 2.89 18.7 18 6 6 9 19 1 Anthony Bass 28653 SDN 27 3 2 0 0 1.68 48.3 41 9 9 21 24 3 Pedro Beato 28568 NYN 60 0 2 1 0 4.30 67.0 59 41 32 27 39 5 Blake Beavan 28668 SEA 15 15 5 6 0 4.27 97.0 106 46 46 15 42 13 Chad Beck 28772 TOR 3 0 0 0 0 0.00 2.3 1 0 0 0 3 0 Duane Below 28685 DET 14 2 0 2 0 4.34 29.0 28 16 14 11 14 2 Dellin Betances 28784 NYA 2 1 0 0 0 6.75 2.7 1 2 2 6 2 0 Bruce Billings 28627 COL 1 0 0 0 0 4.50 2.0 5 1 1 0 0 0 Bruce Billings 28627 OAK 3 0 0 0 0 12.60 5.0 8 9 7 6 7 1 Andrew Brackman 28783 NYA 3 0 0 0 0 0.00 2.3 1 0 0 3 0 0 Brad Brach 28724 SDN 9 0 0 2 0 5.14 7.0 9 5 4 7 11 0 Zach Britton 28563 BAL 28 28 11 11 0 4.61 154.3 162 93 79 62 97 12 Brian Broderick 28571 WAS 11 0 0 1 0 6.57 12.3 16 9 9 3 4 0 Rex Brothers 28640 COL 48 0 1 2 1 2.88 40.7 33 14 13 20 59 4 Andrew Carignan 28743 OAK 6 0 0 0 0 4.26 6.3 8 4 3 2 5 1 Chris Carpenter 28656 CHN 10 0 0 0 0 2.79 9.7 12 3 3 7 8 1 David Carpenter 28665 HOU 34 0 1 3 1 2.93 27.7 28 9 9 13 29 3 Joel Carreno 28712 TOR 11 0 1 0 0 1.15 15.7 11 2 2 4 14 1 Xavier Cedeno 28778 HOU 3 0 0 0 0 27.00 1.7 7 5 5 0 0 2 Tyler Chatwood 28566 ANA 27 25 6 11 0 4.75 142.0 166 81 75 71 74 14 Maikel Cleto 28636 SLN 3 0 0 0 0 12.46 4.3 7 6 6 4 6 2 Alex Cobb 28598 TBA 9 9 3 2 0 3.42 52.7 49 21 20 21 37 3 Louis Coleman 28589 KCA 48 0 1 4 1 2.87 59.7 44 20 19 26 64 9 Josh Collmenter 28585 ARI 31 24 10 10 0 3.38 154.3 137 61 58 28 100 17 Tim Collins 28574 KCA 68 0 4 4 0 3.63 67.0 52 28 27 48 60 5 Ryan Cook 28686 ARI 12 0 0 1 0 7.04 7.7 11 6 6 8 7 0 Michael Crotta 28577 PIT 15 0 0 1 0 9.28 10.7 20 11 11 5 7 2 Aaron Crow 28561 KCA 57 0 4 4 0 2.76 62.0 55 20 19 31 65 8 Justin De Fratus 28780 PHI 5 0 1 0 0 2.25 4.0 1 2 1 3 3 0 Dane De La Rosa 28681 TBA 7 0 0 0 0 9.82 7.3 10 8 8 3 8 1 Rubby De La Rosa 28625 LAN 13 10 4 5 0 3.71 60.7 54 26 25 31 60 6 Steven Delabar 28766 SEA 6 0 1 1 0 2.57 7.0 5 2 2 4 7 1 Randall Delgado 28659 ATL 7 7 1 1 0 2.83 35.0 29 12 11 14 18 5 Fautino De Los Santos 28129 OAK 34 0 3 2 0 4.32 33.3 27 19 16 17 43 4 Scott Diamond 28678 MIN 7 7 1 5 0 5.08 39.0 51 25 22 17 19 3 Brandon Dickson 28666 SLN 4 1 0 0 0 3.24 8.3 9 3 3 3 7 2 Rafael Dolis 28786 CHN 1 0 0 0 0 0.00 1.3 0 0 0 1 1 0 Danny Duffy 28614 KCA 20 20 4 8 0 5.64 105.3 119 66 66 51 87 15 Steven Edlefsen 28711 SFN 13 0 0 0 0 9.53 11.3 17 12 12 10 6 2 Nate Eovaldi 28697 LAN 10 6 1 2 0 3.63 34.7 28 14 14 20 23 2 Cody Eppley 28590 TEX 10 0 1 1 0 8.00 9.0 11 8 8 5 6 3 Danny Farquhar 28773 TOR 3 0 0 0 0 13.50 2.0 4 4 3 2 1 0 Michael Fiers 28775 MIL 2 0 0 0 0 0.00 2.0 2 0 0 3 2 0 Charlie Furbush 28620 DET 17 2 1 3 0 3.62 32.3 36 18 13 14 26 5 Charlie Furbush 28620 SEA 11 10 3 7 0 6.62 53.0 61 41 39 16 41 11 Steve Garrison 28044 NYA 1 0 0 0 0 0.00 .7 0 0 0 0 0 0 John Gaub 28769 CHN 4 0 0 0 0 6.75 2.7 2 2 2 2 3 0 Cory Gearrin 28591 ATL 18 0 1 1 0 7.85 18.3 17 16 16 12 25 0 Graham Godfrey 28647 OAK 5 4 1 2 0 3.96 25.0 32 14 11 5 13 3 Brandon Gomes 28600 TBA 40 0 2 1 0 2.92 37.0 34 15 12 16 32 3 Javy Guerra 28609 LAN 47 0 2 2 21 2.31 46.7 37 12 12 18 38 2 Nick Hagadone 28725 CLE 9 0 1 0 0 4.09 11.0 4 6 5 6 11 0 Mark Hamburger 28722 TEX 5 0 1 0 0 4.50 8.0 5 4 4 3 6 0 Erik Hamren 28692 SDN 14 0 1 0 0 4.38 12.3 10 7 6 9 10 2 Brad Hand 28642 FLO 12 12 1 8 0 4.20 60.0 53 32 28 35 38 10 Chris Hatcher 28090 FLO 11 0 0 0 0 6.97 10.3 14 8 8 4 8 2 Liam Hendriks 28750 MIN 4 4 0 2 0 6.17 23.3 29 16 16 6 16 3 Kelvin Herrera 28782 KCA 2 0 0 1 0 13.50 2.0 2 3 3 0 0 1 Jeremy Horst 28630 CIN 12 0 0 0 0 2.93 15.3 18 6 5 6 9 2 Tommy Hottovy 28637 BOS 8 0 0 0 0 6.75 4.0 4 3 3 3 2 0 Jared Hughes 28760 PIT 12 0 0 1 0 4.09 11.0 9 5 5 4 10 1 Alan Johnson 28584 COL 1 1 0 0 0 9.00 4.0 6 5 4 3 3 0 Josh Judy 28619 CLE 12 0 0 0 0 7.07 14.0 18 11 11 4 10 4 Cole Kimball 28608 WAS 12 0 1 0 0 1.93 14.0 8 3 3 11 11 0 Corey Kluber 28728 CLE 3 0 0 0 0 8.31 4.3 6 4 4 3 5 0 George Kontos 28763 NYA 7 0 0 0 0 3.00 6.0 4 2 2 3 6 1 Josh Lindblom 28634 LAN 27 0 1 0 0 2.73 29.7 21 9 9 10 28 0 Shane Lindsay 28740 CHA 4 0 0 0 0 12.00 6.0 11 8 8 5 6 1 Jeff Locke 28765 PIT 4 4 0 3 0 6.48 16.7 21 12 12 10 5 3 Josh Lueke 28578 SEA 25 0 1 1 0 6.06 32.7 34 22 22 13 29 2 Jordan Lyles 28632 HOU 20 15 2 8 0 5.36 94.0 107 61 56 26 67 14 Lance Lynn 28635 SLN 18 2 1 1 1 3.12 34.7 25 12 12 11 40 3 Trystan Magnuson 28610 OAK 9 0 0 0 0 6.14 14.7 15 11 10 5 11 3 Luis Marte 28727 DET 4 0 1 0 0 2.45 3.7 6 1 1 1 3 0 Ryan Mattheus 28652 WAS 35 0 2 2 0 2.81 32.0 26 11 10 15 12 1 Zach McAllister 28672 CLE 4 4 0 1 0 6.11 17.7 26 16 12 7 14 1 Wade Miley 28708 ARI 8 7 4 2 0 4.50 40.0 48 20 20 18 25 6 Tom Milone 28739 WAS 5 5 1 0 0 3.81 26.0 28 11 11 4 15 2 Matt Moore 28774 TBA 3 1 1 0 0 2.89 9.3 9 3 3 3 15 1 Daniel Moskos 28599 PIT 31 0 1 1 0 2.96 24.3 29 11 8 9 11 0 Juan Nicasio 28629 COL 13 13 4 4 0 4.14 71.7 73 35 33 18 58 8 Hector Noesi 28611 NYA 30 2 2 2 0 4.47 56.3 63 29 28 22 45 6 Lester Oliveros 28667 DET 9 0 0 0 0 5.63 8.0 8 5 5 4 4 0 Lester Oliveros 28667 MIN 10 0 0 0 0 4.05 13.3 13 6 6 7 9 0 Jarrod Parker 28787 ARI 1 1 0 0 0 0.00 5.7 4 0 0 1 1 0 Joseph Paterson 28579 ARI 62 0 0 3 1 2.91 34.0 28 11 11 15 28 1 Brad Peacock 28756 WAS 3 2 2 0 0 .75 12.0 7 1 1 6 4 0 Lance Pendleton 28581 HOU 4 0 0 0 0 17.36 4.7 10 9 9 1 5 4 Lance Pendleton 28581 NYA 11 0 0 0 0 3.21 14.0 10 5 5 10 8 2 Luis Perez 28583 TOR 37 4 3 3 0 5.12 65.0 74 40 37 27 54 9 Zach Phillips 28723 BAL 10 0 0 0 0 1.13 8.0 6 1 1 2 8 1 Michael Pineda 28562 SEA 28 28 9 10 0 3.74 171.0 133 76 71 55 173 18 Drew Pomeranz 28768 COL 4 4 2 1 0 5.40 18.3 19 11 11 5 13 0 Zach Putnam 28776 CLE 8 0 1 1 0 6.14 7.3 10 5 5 0 9 1 Addison Reed 28741 CHA 6 0 0 0 0 3.68 7.3 10 3 3 1 12 1 Garrett Richards 28705 ANA 7 3 0 2 0 5.79 14.0 16 11 9 7 9 4 Aneury Rodriguez 28567 HOU 43 8 1 6 0 5.27 85.3 83 57 50 32 64 13 Chance Ruffin 28687 DET 2 0 0 0 0 4.91 3.7 5 2 2 0 3 2 Chance Ruffin 28687 SEA 13 0 1 0 0 3.86 14.0 13 6 6 9 15 2 Eduardo Sanchez 28580 SLN 26 0 3 1 5 1.80 30.0 14 6 6 16 35 1 Amauri Sanit 28606 NYA 4 0 0 0 0 12.86 7.0 12 10 10 3 4 0 Hector Santiago 28669 CHA 2 0 0 0 0 0.00 5.3 1 0 0 1 2 0 Joe Savery 28781 PHI 4 0 0 0 0 0.00 2.7 1 0 0 0 2 0 Christopher Schwinden 28761 NYN 4 4 0 2 0 4.71 21.0 23 13 11 6 17 1 Michael Schwimer 28709 PHI 12 0 1 1 0 5.02 14.3 15 8 8 7 16 2 Evan Scribner 28593 SDN 10 0 0 0 0 7.07 14.0 18 11 11 4 10 1 Atahualpa Severino 28747 WAS 6 0 1 0 0 3.86 4.7 5 2 2 1 7 1 Bryan Shaw 28650 ARI 33 0 1 0 0 2.54 28.3 30 9 8 8 24 2 Henry Sosa 28127 HOU 10 10 3 5 0 5.23 53.3 54 31 31 23 38 7 Joshua Spence 28661 SDN 40 0 0 2 0 2.73 29.7 14 9 9 19 31 2 Zach Stewart 28657 TOR 3 3 0 1 0 4.86 16.7 26 9 9 5 10 2 Zach Stewart 28657 CHA 10 8 2 5 0 6.22 50.7 64 35 35 13 35 9 Josh Stinson 28730 NYN 14 0 0 2 1 6.92 13.0 14 10 10 7 8 1 Mike Stutes 28592 PHI 57 0 6 2 0 3.63 62.0 49 25 25 28 58 7 Eric Surkamp 28718 SFN 6 6 2 2 0 5.74 26.7 32 18 17 17 13 1 Yoshinori Tateyama 28622 TEX 39 0 2 0 1 4.50 44.0 37 23 22 11 43 8 Everett Teaford 28613 KCA 26 3 2 1 1 3.27 44.0 36 17 16 14 28 8 Julio Teheran 28603 ATL 5 3 1 1 0 5.03 19.7 21 11 11 8 10 4 Aaron Thompson 27763 PIT 4 1 0 0 0 7.04 7.7 13 6 6 6 1 2 Mason Tobin 28576 TEX 4 0 0 0 0 6.75 5.3 5 5 4 5 0 1 Alex Torres 28680 TBA 4 0 1 1 0 3.38 8.0 8 4 3 7 9 0 Jacob Turner 28691 DET 3 3 0 1 0 8.53 12.7 17 13 12 4 8 3 Jose Valdez 28586 HOU 12 0 0 0 0 9.00 14.0 17 14 14 7 15 2 Anthony Vasquez 28713 SEA 7 7 1 6 0 8.90 29.3 46 35 29 10 13 13 Brayan Villarreal 28560 DET 16 0 1 1 0 6.75 16.0 21 12 12 10 14 3 Elih Villanueva 28655 FLO 1 1 0 1 0 24.00 3.0 5 8 8 5 2 1 Arodys Vizcaino 28704 ATL 17 0 1 1 0 4.67 17.3 16 9 9 9 17 1 Neil Wagner 28720 OAK 6 0 0 0 0 7.20 5.0 6 7 4 3 4 1 Kyle Waldrop 28745 MIN 7 0 1 0 0 5.73 11.0 10 7 7 6 5 1 Tony Watson 28645 PIT 43 0 2 2 0 3.95 41.0 34 18 18 20 37 6 Kyle Weiland 28674 BOS 7 5 0 3 0 7.66 24.7 29 22 21 12 13 5 Kevin Whelan 28651 NYA 2 0 0 0 0 5.40 1.7 0 1 1 5 1 0 Alex White 28597 CLE 3 3 1 0 0 3.60 15.0 14 7 6 9 13 3 Alex White 28597 COL 7 7 2 4 0 8.42 36.3 48 35 34 16 24 12 Tom Wilhelmsen 28570 SEA 25 0 2 0 0 3.31 32.7 25 13 12 13 30 2 Adam Wilk 28626 DET 5 0 0 0 0 5.40 13.3 14 10 8 3 10 3]]>
Name UID Tm AVG G AB H 2B 3B HR R RBI HBP BB K SB ----------------------- ----- --- ----- --- --- --- -- -- -- --- --- --- --- --- --- Yonder Alonso 28455 CIN .207 22 29 6 2 0 0 2 3 0 0 10 0 Pedro Alvarez 28406 PIT .256 95 347 89 21 1 16 42 64 0 37 119 0 Bryan Anderson 28040 SLN .281 15 32 9 2 0 0 1 4 1 1 7 0 Lars Anderson 28104 BOS .200 18 35 7 1 0 0 4 4 0 7 8 0 J.P. Arencibia 28439 TOR .143 11 35 5 1 0 2 3 4 0 2 11 0 Darwin Barney 28443 CHN .241 30 79 19 4 0 0 12 2 0 6 12 0 Mike Baxter 28468 SDN .125 9 8 1 0 0 0 0 1 0 0 2 0 Josh Bell 28416 BAL .214 53 159 34 5 0 3 15 12 0 2 53 0 Brennan Boesch 28502 DET .256 133 464 119 26 3 14 49 67 5 40 99 7 Brian Bogusevic 28462 HOU .179 19 28 5 3 0 0 5 3 0 3 12 1 J.C. Boscan 28500 ATL .000 1 0 0 0 0 0 1 0 0 1 0 0 Peter Bourjos 28437 ANA .204 51 181 37 6 4 6 19 15 2 6 40 10 Domonic Brown 28429 PHI .210 35 62 13 3 0 2 8 13 0 5 24 2 Jordan Brown 27876 CLE .230 26 87 20 7 0 0 9 2 1 4 10 0 Drew Butera 28504 MIN .197 49 142 28 6 1 2 12 13 4 4 25 0 Lorenzo Cain 28038 MIL .306 43 147 45 11 1 1 17 13 1 9 28 7 Chris Carter 28440 OAK .186 24 70 13 1 0 3 8 7 0 7 21 1 Jason Castro 28411 HOU .205 67 195 40 8 1 2 26 8 0 22 41 0 Starlin Castro 28375 CHN .300 125 463 139 31 5 3 53 41 6 29 71 10 Welington Castillo 28263 CHN .300 7 20 6 4 0 1 3 5 0 1 7 0 Pedro Ciriaco 28479 PIT .500 8 6 3 1 1 0 3 1 0 0 3 0 Hank Conger 28482 ANA .172 13 29 5 1 1 0 2 5 0 5 9 0 Scott Cousins 28460 FLO .297 27 37 11 2 2 0 2 2 0 1 13 0 Allen Craig 28506 SLN .246 44 114 28 7 0 4 12 18 0 9 26 0 Colin Curtis 28410 NYA .186 31 59 11 3 0 1 7 8 1 4 15 0 Brad Davis 27992 FLO .211 33 109 23 7 1 3 8 16 1 9 37 2 Ike Davis 28507 NYN .264 147 523 138 33 1 19 73 71 1 72 138 3 Daniel Descalso 28488 SLN .265 11 34 9 2 0 0 6 4 1 2 6 1 Argenis Diaz 28508 PIT .242 22 33 8 1 0 0 0 2 0 3 10 0 Joshua Donaldson 28509 OAK .156 14 32 5 1 0 1 1 4 0 2 12 0 Jason Donald 28067 CLE .253 88 296 75 19 3 4 39 24 3 22 70 5 Lucas Duda 28456 NYN .202 29 84 17 6 0 4 11 13 1 6 22 0 Jarrod Dyson 28474 KCA .211 18 57 12 4 2 1 11 5 0 6 16 9 Danny Espinosa 28459 WAS .214 28 103 22 4 1 6 16 15 0 9 30 0 Jesus Feliciano 28400 NYN .231 54 108 25 4 1 0 12 3 1 6 12 1 Darren Ford 28512 SFN .000 7 0 0 0 0 0 1 0 0 0 0 2 Jeff Frazier 28432 DET .217 9 23 5 1 0 0 3 1 0 1 6 0 Freddie Freeman 28454 ATL .167 20 24 4 1 0 1 3 1 0 0 8 0 Cole Gillespie 28106 ARI .231 45 104 24 8 0 2 11 12 1 7 29 1 Greg Halman 28495 SEA .138 9 29 4 1 0 0 1 3 0 1 11 1 Mark Hamilton 28489 SLN .143 9 14 2 0 0 0 0 0 0 1 5 0 Chris Hatcher 28090 FLO .000 5 6 0 0 0 0 0 0 0 2 5 0 Chris Heisey 28261 CIN .254 97 201 51 10 1 8 33 21 6 16 57 1 Jason Heyward 28514 ATL .277 142 520 144 29 5 18 83 72 10 91 128 11 Brandon Hicks 28264 ATL .000 16 5 0 0 0 0 7 0 0 1 2 0 Steven Hill 28445 SLN .333 1 3 1 0 0 1 1 1 0 0 1 0 Chad Huffman 28403 NYA .167 9 18 3 0 0 0 1 2 1 2 5 0 Luke Hughes 28515 MIN .286 2 7 2 0 0 1 1 1 0 0 3 0 Rhyne Hughes 28516 BAL .213 14 47 10 2 0 0 3 4 0 4 19 0 Austin Jackson 28041 DET .293 151 618 181 34 10 4 103 41 4 47 170 27 Jon Jay 28518 SLN .300 105 287 86 19 2 4 47 27 3 24 50 2 Desmond Jennings 28453 TBA .190 17 21 4 1 1 0 5 2 1 2 4 2 Ryan Kalish 28434 BOS .252 53 163 41 11 1 4 26 24 1 12 38 10 Eric Kratz 28420 PIT .118 9 34 4 0 0 0 2 1 0 2 9 0 John Lindsey 27230 LAN .083 11 12 1 0 0 0 0 0 1 0 3 0 Jonathan Lucroy 28386 MIL .253 75 277 70 9 0 4 24 26 1 18 44 4 Matt Mangini 28496 SEA .211 11 38 8 0 0 0 2 1 0 2 13 0 Ozzie Martinez 28492 FLO .326 14 43 14 4 1 0 8 2 0 4 6 1 Lucas May 28464 KCA .189 12 37 7 1 0 0 3 6 1 0 10 0 Michael McKenry 28109 COL .000 6 8 0 0 0 0 0 0 0 1 5 0 Russell Mitchell 27962 LAN .143 15 42 6 0 0 2 3 4 0 0 8 0 Brent Morel 28472 CHA .231 21 65 15 3 0 3 9 7 0 4 17 2 Mitch Moreland 28431 TEX .255 47 145 37 4 0 9 20 25 1 25 36 3 Logan Morrison 28427 FLO .283 62 244 69 20 7 2 43 18 2 41 51 0 Daniel Nava 28402 BOS .242 60 161 39 14 1 1 23 26 8 19 46 1 Yamaico Navarro 28447 BOS .143 20 42 6 0 0 0 4 5 0 2 17 0 Chris Nelson 27895 COL .280 17 25 7 1 0 0 7 0 0 1 4 1 Mike Nickeas 28465 NYN .200 5 10 2 0 0 0 0 0 0 0 5 0 Eduardo Nunez 28446 NYA .280 30 50 14 1 0 1 12 7 0 3 2 5 Bryan Petersen 28378 FLO .083 23 24 2 0 0 0 1 2 0 1 6 0 Trevor Plouffe 28385 MIN .146 22 41 6 1 0 2 7 6 0 0 14 0 Alex Presley 28480 PIT .261 19 23 6 1 0 0 2 0 0 1 8 1 Wilson Ramos 28525 MIN .296 7 27 8 3 0 0 2 1 1 0 3 0 Wilson Ramos 28525 WAS .269 15 52 14 4 0 1 3 4 0 2 9 0 John Raynor 28526 PIT .200 11 10 2 0 0 0 1 0 0 1 3 0 Ben Revere 28469 MIN .179 13 28 5 0 0 0 1 2 0 2 5 0 Will Rhymes 28425 DET .304 54 191 58 12 3 1 30 19 0 14 16 0 Andrew Romine 28499 ANA .091 5 11 1 0 0 0 0 0 0 0 4 0 Kevin Russo 28379 NYA .184 31 49 9 2 0 0 5 4 1 3 9 1 Carlos Santana 28401 CLE .260 46 150 39 13 0 6 23 22 1 37 29 3 Konrad Schmidt 28484 ARI .125 4 8 1 0 0 0 0 0 0 1 0 0 Scott Sizemore 28099 DET .224 48 143 32 7 0 3 19 14 0 15 40 0 Justin Smoak 28530 SEA .239 30 113 27 4 0 5 11 14 0 8 34 0 Justin Smoak 28530 TEX .209 70 235 49 10 0 8 29 34 0 38 57 1 Brad Snyder 27376 CHN .185 12 27 5 1 0 0 1 5 0 1 12 0 Brandon Snyder 28046 BAL .300 10 20 6 2 0 0 1 3 0 0 3 0 Eric Sogard 28486 OAK .429 4 7 3 0 0 0 0 0 0 2 1 0 Mike Stanton 28396 FLO .259 100 359 93 21 1 22 45 59 2 34 123 5 Maxim St.Pierre 26311 DET .222 6 9 2 1 0 0 1 0 0 0 2 0 Jose Tabata 28031 PIT .299 102 405 121 21 4 4 61 35 2 28 57 19 Ruben Tejada 28533 NYN .213 78 216 46 12 0 1 28 15 8 22 38 2 Steven Tolleson 28535 OAK .286 25 49 14 3 0 1 5 4 0 4 9 0 Mark Trumbo 28481 ANA .067 8 15 1 0 0 0 2 2 0 1 8 0 Chris Valaika 28255 CIN .263 19 38 10 1 0 1 3 2 0 1 9 0 Danny Valencia 28394 MIN .311 85 299 93 18 1 7 30 40 0 20 46 2 Dayan Viciedo 28409 CHA .308 38 104 32 7 0 5 17 13 0 2 25 1 Brett Wallace 28433 HOU .222 51 144 32 6 1 2 14 13 7 8 50 0 Casper Wells 28382 DET .323 36 93 30 6 1 4 14 17 0 6 19 0 Danny Worth 28383 DET .255 39 106 27 5 0 2 10 8 0 6 13 1 Lance Zawadzki 28537 SDN .200 20 35 7 2 0 0 4 1 0 5 7 1
Name UID Tm G GS W L S ERA Inn H R ER BB K HR -------------------- ----- --- --- -- -- -- -- ------ ----- --- --- --- --- --- -- Fernando Abad 28430 HOU 22 0 0 1 0 2.84 19.0 14 6 6 5 12 3 Hector Ambriz 28084 CLE 34 0 0 2 0 5.59 48.3 68 31 30 17 37 10 Jake Arrieta 28399 BAL 18 18 6 6 0 4.66 100.3 106 57 52 48 52 9 Luis Atilano 28501 WAS 16 16 6 7 0 5.15 85.7 96 56 49 32 40 11 Brandon Beachy 28494 ATL 3 3 0 2 0 3.00 15.0 16 9 5 7 15 0 Omar Beltre 27095 TEX 2 2 0 1 0 9.00 7.0 9 7 7 7 9 3 Zach Braddock 28387 MIL 46 0 1 2 0 2.94 33.7 29 11 11 19 41 1 Jay Buente 28390 FLO 8 0 0 0 0 6.55 11.0 16 8 8 11 9 0 Alex Burnett 28503 MIN 41 0 2 2 0 5.29 47.7 52 28 28 23 37 6 Andrew Cashner 28393 CHN 53 0 2 6 0 4.80 54.3 55 31 29 30 50 8 Bobby Cassevah 28505 ANA 16 0 1 2 0 3.15 20.0 23 11 7 8 8 0 Jose Ceda 28123 FLO 8 0 0 0 0 5.19 8.7 8 5 5 11 9 1 Aroldis Chapman 28452 CIN 15 0 2 2 0 2.03 13.3 9 4 3 5 19 0 Steve Cishek 28493 FLO 3 0 0 0 0 0.00 4.3 1 0 0 1 3 0 Robert Coello 28467 BOS 6 0 0 0 0 4.76 5.7 4 3 3 5 5 0 Casey Coleman 28436 CHN 12 8 4 2 0 4.11 57.0 56 27 26 25 27 3 Danny Cortes 28490 SEA 4 0 0 1 0 3.38 5.3 3 3 2 3 6 0 Bob Cramer 27902 OAK 4 4 2 1 0 3.04 23.7 20 8 8 6 13 5 Samuel Deduno 28450 COL 4 0 0 0 0 3.38 2.7 3 1 1 1 3 1 Robert Delaney 28118 MIN 1 0 0 0 0 9.00 1.0 2 1 1 1 0 1 Enerio Del Rosario 28388 HOU 2 0 0 0 0 20.25 1.3 4 3 3 0 1 0 Enerio Del Rosario 28388 CIN 9 0 1 1 0 2.08 8.7 13 4 2 4 3 0 Sam Demel 28407 ARI 37 0 2 1 2 5.35 37.0 42 27 22 12 33 5 Thomas Diamond 27565 CHN 16 3 1 3 0 6.83 29.0 33 23 22 18 36 5 Felix Doubront 28408 BOS 12 3 2 2 2 4.32 25.0 27 16 12 10 23 3 Kyle Drabek 28485 TOR 3 3 0 3 0 4.76 17.0 18 9 9 5 12 2 John Ely 28510 LAN 18 18 4 10 0 5.49 100.0 105 63 61 40 76 12 Jesse English 28511 WAS 7 0 0 0 0 3.86 7.0 10 3 3 2 4 0 Barry Enright 28131 ARI 17 17 6 7 0 3.91 99.0 97 43 43 29 49 20 Edgmer Escalona 28475 COL 5 0 0 0 0 1.50 6.0 4 1 1 4 2 0 Matt Fox 28463 BOS 3 0 0 0 0 10.80 1.7 4 2 2 1 0 0 Matt Fox 28463 MIN 1 1 0 0 0 3.18 5.7 4 2 2 1 0 0 Dillon Gee 28473 NYN 5 5 2 2 0 2.18 33.0 25 10 8 15 17 2 Jeanmar Gomez 28421 CLE 11 11 4 5 0 4.68 57.7 73 36 30 22 34 7 Lucas Harrell 27611 CHA 8 3 1 0 0 4.88 24.0 34 18 13 17 15 2 Jeremy Hellickson 28052 TBA 10 4 4 0 0 3.47 36.3 32 14 14 8 33 5 David Herndon 28513 PHI 47 0 1 3 0 4.30 52.3 67 27 25 17 29 2 Frank Herrmann 28395 CLE 40 0 0 1 1 4.03 44.7 48 22 20 9 24 6 Greg Holland 28435 KCA 15 0 0 1 0 6.75 18.7 23 15 14 8 23 3 James Houser 27628 FLO 1 0 0 0 0 20.25 1.3 3 3 3 1 1 1 Ryota Igarashi 28517 NYN 34 0 1 1 0 7.12 30.3 29 24 24 18 25 4 Gregory Infante 28471 CHA 5 0 0 0 0 0.00 4.7 2 0 0 4 5 0 Justin James 28458 OAK 5 0 0 0 0 4.50 4.0 7 2 2 4 5 0 Kenley Jansen 28423 LAN 25 0 1 0 4 .67 27.0 12 2 2 15 41 0 Jeremy Jeffress 28457 MIL 10 0 1 0 0 2.70 10.0 8 4 3 6 8 0 Craig Kimbrel 28376 ATL 21 0 4 0 1 .44 20.7 9 2 1 16 40 0 Brandon Kintzler 28478 MIL 7 0 0 1 0 7.36 7.3 10 6 6 4 9 2 Michael Kirkman 28448 TEX 14 0 0 0 0 1.65 16.3 9 3 3 10 16 0 Michael Kohn 28426 ANA 24 0 2 0 1 2.11 21.3 17 5 5 16 20 0 Zach Kroenke 28477 ARI 3 1 1 0 0 6.75 6.7 9 5 5 4 2 2 Mike Leake 28519 CIN 24 22 8 4 0 4.23 138.3 158 77 65 49 91 19 Samuel LeCure 27652 CIN 15 6 2 5 0 4.50 48.0 50 24 24 25 37 6 Rommie Lewis 28520 TOR 14 0 0 0 0 6.75 18.7 20 14 14 8 15 4 Brad Lincoln 28398 PIT 11 9 1 4 0 6.66 52.7 66 42 39 15 25 9 Jon Link 28039 LAN 9 0 0 0 0 4.15 8.7 12 7 4 4 4 0 Cory Luebke 28461 SDN 4 3 1 1 0 4.08 17.7 17 8 8 6 18 3 Evan MacLane 27238 SLN 2 0 0 1 0 9.00 1.0 1 1 1 1 0 1 Scott Maine 28451 CHN 13 0 0 0 0 2.08 13.0 9 4 3 5 11 1 Jhan Marinez 28419 FLO 4 0 1 1 0 6.75 2.7 3 3 2 3 3 1 Jeff Marquez 28418 CHA 1 0 0 0 0 18.00 1.0 2 2 2 0 0 1 Frank Mata 28389 BAL 15 0 0 0 0 7.79 17.3 24 16 15 8 9 2 Marcos Mateo 28442 CHN 21 0 0 1 0 5.82 21.7 20 15 14 9 26 6 Yunesky Maya 28470 WAS 5 5 0 3 0 5.88 26.0 30 18 17 11 12 3 Mike McClendon 28444 MIL 17 0 2 0 0 3.00 21.0 15 7 7 7 21 2 Jacob McGee 27679 TBA 8 0 0 0 0 1.80 5.0 2 1 1 3 6 0 Jenrry Mejia 28521 NYN 33 3 0 4 0 4.62 39.0 46 21 20 20 22 3 Aldaberto Mendez 28466 FLO 5 5 1 3 0 5.11 24.7 28 14 14 12 11 7 Mike Minor 28441 ATL 9 8 3 2 0 5.98 40.7 53 28 27 11 43 6 Carlos Monasterios 28522 LAN 32 13 3 5 0 4.38 88.3 99 48 43 29 52 15 Jordan Norberto 28523 ARI 33 0 0 2 0 5.85 20.0 16 13 13 22 15 3 Ivan Nova 28380 NYA 10 7 1 2 0 4.50 42.0 44 22 21 17 26 4 Alexi Ogando 28404 TEX 44 0 4 1 0 1.30 41.7 31 6 6 16 39 2 Andy Oliver 28414 DET 5 5 0 4 0 7.36 22.0 26 22 18 13 18 3 Logan Ondrusek 28524 CIN 60 0 5 0 0 3.68 58.7 49 25 24 20 39 7 Adam Ottavino 28392 SLN 5 3 0 2 0 8.46 22.3 37 21 21 9 12 5 Vinny Pestano 28498 CLE 5 0 0 0 1 3.60 5.0 4 2 2 5 8 0 Matt Reynolds 28538 COL 21 0 1 0 0 2.00 18.0 10 4 4 5 17 2 Francisco Rodriguez 28527 ANA 43 0 1 3 0 4.37 47.3 46 23 23 26 36 5 Mark Rogers 28483 MIL 4 2 0 0 0 1.80 10.0 2 2 2 3 11 0 Sandy Rosario 28491 FLO 2 0 0 0 0 54.00 1.0 9 6 6 1 0 2 Tyson Ross 28528 OAK 26 2 1 4 1 5.49 39.3 39 24 24 20 32 4 James Russell 28529 CHN 57 0 1 1 0 4.96 49.0 55 37 27 11 42 11 Fernando Salas 28391 SLN 27 0 0 0 0 3.52 30.7 28 13 12 15 29 4 Chris Sale 28438 CHA 21 0 2 1 4 1.93 23.3 15 5 5 10 32 2 Alejandro Sanabia 28413 FLO 15 12 5 3 0 3.73 72.3 74 32 30 16 47 6 Sergio Santos 27352 CHA 56 0 2 2 1 2.96 51.7 53 18 17 26 56 2 Jay Sborz 28412 DET 1 0 0 0 0 67.50 .7 3 5 5 0 1 0 Brian Schlitter 28415 CHN 7 0 0 1 0 12.38 8.0 18 11 11 5 7 2 Brett Sinkbeil 28487 FLO 3 0 0 0 0 13.50 2.0 2 3 3 5 1 0 Anthony Slama 28422 MIN 5 0 0 1 0 7.71 4.7 6 4 4 5 5 1 Jordan Smith 28405 CIN 37 0 3 2 1 3.86 42.0 45 18 18 11 26 7 Daniel Stange 28531 ARI 4 0 0 0 0 13.50 4.0 4 6 6 6 2 1 Drew Storen 28384 WAS 54 0 4 4 5 3.58 55.3 48 24 22 22 52 3 Stephen Strasburg 28397 WAS 12 12 5 3 0 2.91 68.0 56 25 22 17 92 5 Hisanori Takahashi 28532 NYN 53 12 10 6 8 3.61 122.0 116 51 49 43 114 13 Kanekoa Texeira 28534 SEA 16 0 0 1 0 5.30 18.7 22 12 11 10 14 0 Kanekoa Texeira 28534 KCA 27 0 1 0 0 4.64 42.7 51 24 22 15 19 3 Josh Tomlin 28428 CLE 12 12 6 4 0 4.56 73.0 72 38 37 19 43 10 Cesar Valdez 28377 ARI 9 2 1 2 0 7.65 20.0 29 19 17 10 13 2 Raul Valdes 28536 NYN 38 1 3 3 1 4.91 58.7 59 33 32 27 56 7 Anthony Varvaro 28497 SEA 4 0 0 1 0 11.25 4.0 6 5 5 6 5 2 Jonathan Venters 27948 ATL 79 0 4 4 1 1.95 83.0 61 30 18 39 93 1 Henry Villar 28476 HOU 8 0 0 0 0 4.50 6.0 5 3 3 3 3 0 Jordan Walden 28449 ANA 16 0 0 1 1 2.35 15.3 13 4 4 7 23 1 Robbie Weinhardt 28417 DET 28 0 2 2 0 6.14 29.3 40 23 20 8 21 2 Blake Wood 28381 KCA 51 0 1 3 0 5.07 49.7 54 29 28 22 31 6 Travis Wood 27784 CIN 17 17 5 4 0 3.51 102.7 85 45 40 26 86 9 Vance Worley 28424 PHI 5 2 1 1 0 1.38 13.0 8 2 2 4 12 1]]>
y Charles Wolfson and Tom Tippett of Diamond Mind, a Simnasium, Inc. company
(www.simnasium.com)
March 31, 2007
How will the free agent spending splurge this past winter play out in 2007? Will Alfonso Soriano, Carlos Lee, Gary Matthews Jr., Gil Meche, Juan Pierre and others justify their big contracts, or will they prove to be multiyear financial millstones for their teams? When and where (if at all) will Roger Clemens pitch in 2007? Will the Cinderella team of 2006, the Tigers, go the way of the White Sox, who took the slipper in 2005, and the Red Sox, who ended the Curse in 2004, and fall short of repeating last year’s success?
As final roster decisions are made and Opening Day approaches, the best laid plans of major league teams are subjected to the scrutiny of commentators, analysts, fantasy addicts and everyday fans, who offer up a varying mix of sabermetrics, wishful thinking and fatalism with their predictions for the coming season.
This is the tenth year for which we at Diamond Mind have used our projection methodology and our simulation software to project the final standings for the coming season. Over that span, our approach has produced some prescient (and, for some teams, sobering) forecasts. For example, in 2006 our system correctly identified five of the six division winners, and we were only an NL West tie-breaker away from a clean sweep. For a survey of the relative success of prognosticators across the nation, see 2006 Predictions – Keeping Score.Before revealing our final standings for the 2007 season, here’s an overview of how we produced them.
We began by projecting the 2007 performance of over 1800 players contending for major league jobs. To do this, we took their major and minor league stats for the past 3 seasons, adjusted for factors such as the level of competition (majors, Japan, AAA, AA, etc) and offense in a league, park effects, and whether the DH rule was in use. Then, giving greater weight to more recent seasons, performances at higher levels, and seasons with more playing time, and adjusting for age, we projected their performance into the league and park where they will be competing in the coming year.
We didn’t merely project the aggregate “headline” stats for each player, but their left/right splits as well. We also assigned ratings for skills such as bunting, baserunning, defensive range and throwing.After all players have been rated, we set up a manager profile for each team, consisting of a starting rotation, bullpen assignments, projected lineups against right- and left-handed starters, and a positional depth chart. Once these profiles were in place for every team, we played out the season using our Diamond Mind Baseball simulation game. The computer manager, guided by our manager profile, makes decisions about starting pitchers, lineups, substitutions, and pitch-by-pitch tactics. Because luck can play a major role in any single season for players and teams (both in real-life and our simulations), we ran the season 200 times and averaged the results.
To provide you with a bit more insight into the process, factors that we do and don’t take into account in our projections include the following:
Keep in mind that many of the most noteworthy events of a baseball season – the breakout performances and fantastic flops by individual players, the teams for which everything goes right or everything goes wrong, the crippling injuries – are things that might occur in individual seasons that we’ve simulated, but are unlikely to appear in our averaged results.
There is a large element of luck involved in baseball, and any given season, real or simulated, will produce a larger spread of runs and wins than are reflected in our projected standings. That’s because averaging the results of 100 or 200 simulated seasons will tend to smooth out the catching-lightning-in-a-bottle features of any real baseball season.
Here are the projected final standings for 2007, based on the 200 seasons we simulated on March 23. Anything that has happened since then in the way of roster decisions, trades and injuries is not reflected here. So, for example, Jonathan Papelbon’s return to the closer role did make it into Boston’s manager profile, but Jorge Julio, obtained from the Diamondbacks in a March 26 trade, did not close for the Marlins in our season simulations.
Legend
W, L, Pct, GB – average wins, losses, winning percentage, games behind division leader
RF, RA – average runs for and against
Div%, WC% - percentage of seasons winning division and wild card (fractions for ties)
AL East W L Pct GB RF RA Div% WC% New York 96 66 .593 - 937 780 71.8 10.5 Toronto 88 74 .543 8 850 791 18.0 18.7 Boston 86 76 .531 10 907 841 9.5 14.6 Baltimore 76 86 .469 20 799 859 0.8 2.0 Tampa Bay 70 92 .432 26 816 926 AL Central W L Pct GB RF RA Div% WC% Cleveland 91 71 .562 - 865 738 41.1 17.4 Detroit 89 73 .549 2 842 763 31.1 14.9 Minnesota 87 75 .537 4 795 740 24.1 10.0 Chicago 78 84 .481 13 827 870 3.8 1.9 Kansas City 66 96 .407 25 765 915 AL West W L Pct GB RF RA Div% WC% Los Angeles 91 71 .562 - 810 711 75.0 3.1 Oakland 84 78 .519 7 773 748 20.3 5.0 Seattle 77 85 .475 14 748 795 2.8 2.2 Texas 75 87 .463 16 794 851 2.0 NL East W L Pct GB RF RA Div% WC% Philadelphia 85 77 .525 - 852 813 36.9 15.9 Atlanta 84 78 .519 1 804 770 32.2 14.0 New York 82 80 .506 3 813 794 24.3 6.8 Washington 75 87 .463 10 757 823 4.2 2.3 Florida 73 89 .451 12 765 845 2.5 1.0 NL Central W L Pct GB RF RA Div% WC% St Louis 85 77 .525 - 769 728 40.1 6.7 Chicago 83 79 .512 2 818 799 27.9 7.9 Houston 81 81 .500 4 803 800 17.9 7.6 Cincinnati 77 85 .475 8 756 798 5.7 4.2 Milwaukee 76 86 .469 9 736 790 7.7 1.1 Pittsburgh 72 90 .444 13 708 800 0.8 1.3 NL West W L Pct GB RF RA Div% WC% San Diego 88 74 .543 - 806 729 64.0 7.2 Los Angeles 81 81 .500 7 769 785 15.3 10.9 Arizona 79 83 .488 9 811 826 10.3 4.2 San Francisco 78 84 .481 10 776 797 6.0 4.4 Colorado 77 85 .475 11 820 866 4.5 4.8
The trend toward increasing parity that we noted in our Projected Standings for the 2006 Season looks set to continue for 2007. In our 2006 simulations, just three of the 30 major league teams failed to reach the postseason in at least one simulated season. For 2007 that number has dropped to two, with the Tampa Bay Devil Rays and Kansas City Royals the only teams to be shut out.
In 2004 10 teams (five in the AL and five in the NL) managed 90 or more wins. In 2005 that number dropped to seven (five in the AL and two in the NL). Our 2006 projected standings had just four teams reaching 90 wins (projected/actual wins): the New York Yankees (93/96), Minnesota Twins (90/96), Oakland A’s (96/93) and St. Louis Cardinals (95/83), although six actually achieved it, the other three being the Tigers (79/95), White Sox (86/90) and New York Mets (87/97). For 2007 the number of teams projected to win at least 90 has dropped even further to just three: the Cleveland Indians and Los Angeles Angels (91 each) and the Yankees (96).
Last year we projected the runs scored for all National League teams to fall within the fairly narrow range of 705 to 818 runs. The actual spread was a bit wider, from a low of 691 for the Pittsburgh Pirates to a high of 865 for the Philadelphia Phillies (not dissimilar to 2007, with the Phillies projected to score a league high 852 runs and the Pirates bringing up the rear with just 708). More noteworthy for 2007, however, is the fact that no team in the National League is projected to win more than 88 games (the San Diego Padres, with the next best total just 85 by the Phillies and Cardinals) or fewer than the 72 (the Pirates).
This doesn't mean there won't be a 90-game winner in the NL this year. The real season will be played only once, and it's quite possible that two or three teams will find a way to reach that threshold. However, our simulation results suggest that no NL team has put together a roster strong enough to make 90+ wins a high probability.
As far as races to qualify for the postseason, the 2006 season generally followed our projections in the American League, with both the wild card, and the only real divisional race, coming out of the Central. For 2007 the only close divisional race in our AL projections again is the Central, and the Tigers again eke out the wild card by the barest of margins.
The 2007 National League divisional races look to be closer, with most teams at least on the fringe of postseason contention (division or wild card) deep into the season. However, the lack of dominant teams in the NL means that any team that manages to put together a big season may waltz home in their division, as the Mets did in 2006, and should two teams manage the feat in a single division, even the wild card race could turn into a runaway.AL East
We see the Yankees again taking the AL East comfortably, by the biggest margin of any division winner. Heading into 2006, it seemed that the East (courtesy of the Yankees and Red Sox), had taken up permanent ownership of the wild card. Surprise! Despite the greater depth of competition in the AL Central, the wild card came out of the Central in 2006. While the Tigers have the highest win total in our projected wild card standings, the Central took the wild card in 44% of the seasons we simulated, while the East came out on top 47% of the time, and the Blue Jays and Red Sox figure to be in the wild card hunt right down to the wire.
New York Yankees (1st, 96-66, division title 72%, wild card 11%)
It’s April 2, Opening Day, and Yankee Stadium is packed. The Yankees starting lineup is being introduced, the names echoing over the PA. “And warming up in the bullpen, the starting pitcher, Carl Pavano.”
Is there an evil eye trained on Yankees starters? In 2005 they used 14 different ones; in 2006 12. They begin 2007 with Chien-Ming Wang out for at least the month of April (an injury that occurred after our simulations were run and so was not taken into account); potential replacements Jeff Karstens and Humberto Sanchez both with sore elbows; Andy Pettitte struggling with back spasms; and Kei Igawa searching for the strike zone.
On the one hand, the Yankees have made 12 straight trips to the postseason, and they’ve overcome many serious injuries to do it. On the other, the closest they’ve come to the World Series since it was snatched from their grasp by the Red Sox in 2004, is signing the guy who caught the ball for the final out that year, Doug Mientkiewicz, for 2007.
Despite lengthy injuries in 2006 to Gary Sheffield, Hideki Matsui and Robinson Cano, the Yankees topped all of baseball with a massive 930 runs scored. We project them to come out well on top again in 2007 with 937.
With their vaunted payroll advantage, and 12 straight postseason appearances, for this team it’s all about World Series wins. There was a lot of buzz about prime prospect Phillip Hughes when camp began, and the prospect of Roger Clemens waiting in the wings. It’s par for the course when talking about the Yankees that, in a preseason preview, the main question ends up being whether their rotation come playoff time will set up strongly enough to return them to the Promised Land.
Toronto Blue Jays (2nd, 88-74, division title 18%, wild card 19%)
It’s funny how sometimes a decent season can leave a team and its fans with a bad aftertaste, while a disappointing season nevertheless can leave behind a positive afterglow. The Blue Jays certainly fall in the latter category. After a huge free agent plunge prior to 2006, they fell out of the wild card race early, then endured a period of midseason turmoil in the dugout and clubhouse with the Shea Hillenbrand and Ted Lilly incidents. By season’s end, however, thanks in part to the struggles of the Red Sox, they found themselves in second place in the East, their first finish higher than third since 1993 (the second of their back-to-back championship seasons).
The Blue Jays certainly have the look of an up-and-coming team. They actually scored 70 fewer runs in 2006 than the runs created formula predicted, suggesting that, with a bit more efficiency, they could increase their scoring in 2007 just by repeating their overall offensive output in 2006 (and that’s without any added production from new DH Frank Thomas or hotshot prospect Adam Lind).
For my money, whether the Blue Jays take the next step, from respectable also-ran to a postseason berth, depends on a few key players: Troy Glaus and Thomas on offense, and A.J. Burnett and B.J. Ryan on the mound. Although closing the gap to the Yankees in the AL East would be a tall order, if these four players can remain healthy and productive, and barring any major problems cropping up elsewhere, Toronto would have as good a shot at the wild card spot as any.
Boston Red Sox (3rd, 86-76, division title 9.5%, wild card 15%)
We can hear the cries echoing from Red Sox Nation. “86 wins? Are you out of your freakin’ minds?!”
Prior to the 2006 season we projected 86 wins for the Red Sox., which is exactly how many games they won. If the Blue Jays season was a feel good disappointment, the Red Sox season certainly was the opposite, although, considering Boston actually was outscored by five runs (820 runs scored to 825 runs allowed), 86 wins was a pretty decent result.
So Boston made some big changes, signing J.D. Drew to replace Trot Nixon in right and Julio Lugo to replace Alex Gonzalez at short; committing to rookie Dustin Pedroia to replace Mark Loretta at second; and, of course, adding Daisuke Matsuzaka to the starting rotation. Then there were the changes they didn’t make: Manny Ramirez remains in left, and Jonathan Papelbon reprises the closer’s role.
Take all of these changes, mix well, simulate the 2007 season 200 times over, and we once again have projected Boston for 86 wins and another third place finish. So how is it possible that the Sox were only moderately better than average in our simulations?
Scoring isn’t the problem. The 2007 Red Sox lineup may not be in the same class as the one in New York, or remind anyone of the 2003-04 version, but it trailed only the Yankees in offense in our simulations.
The problem was pitching. Despite the addition of Daisuke Matsuzaka (projected to be one of the league’s better starters), the team finished 9th in the league in run prevention in the simulations.
How is that possible? Let us count the ways:
So, it's quite possible that the Sox will end up playing a whole bunch of 7-5 games, and while they're likely to win more than they'll lose, that's not a proven formula for big-time success. For the Sox to be an elite contender, several members of the pitching staff need to step up.
Can they? Just about everyone on the staff has posted one or more big-league seasons in which they were much better than how we project them for 2007. It's asking too much to expect all of them to return to peak form in unison, but it's not much of a stretch to imagine, say, Schilling and Beckett. If that happens, and there are no big negative surprises, this club could be a legitimate threat to win it all.
Baltimore Orioles (4th, 76-86, division title 0.8%, wild card 2%)
Chad Bradford, 3 years, $10.5 million; Jamie Walker, 3 years, $12 million; Danys Baez, 3 years, $19 million.
We could stop right there, but that wouldn’t really be fair to Orioles fans, although it might be merciful.
Aubrey Huff, 3 years, $20 million; Jay Payton, 2 years, $9.5 million; Kevin Millar, 1 year, $2.75 million.
Can you hear the gap between the Orioles and the Yankees, Blue Jays and Red Sox closing yet?
No review of the Orioles would be complete without mentioning two noteworthy streaks from the 2006 season. Miguel Tejada hit fewer homers (24) in 2006 than in any season since 1999, but he managed that many despite going 126 consecutive at bats from August 22 to September 23 without one. That was no match, however, for up-and-comer Nick Markakis, who went homer-less for three solid months, 205 at bats from April 15 to July 15, yet still ended the season with 16.
Tampa Bay Devil Rays (5th, 70-92, no postseason appearances)
It’s hardly original to question how Tampa Bay can be expected to compete in the AL East with Boardwalk and Park Place (a.k.a. the Yankees and Boston, not to mention Toronto and Baltimore, who are hardly crying poor). Really, though, it seems to me that the problem is more than just money. Casting my mind back a few seasons to my one visit to Tropicana Field, and setting that image against the history and drama that are Fenway Park and Yankee Stadium, and the juxtaposition feels almost surreal.
Nevertheless, at least one well known authority (Jim Callis at Baseball America) has predicted that the Devil Rays will win the World Series … in 2010. The future is now for the Devil Rays in the outfield with their five tool trio of Carl Crawford, Rocco Baldelli and Delmon Young (assuming they can keep this group intact long enough for the rest of the team to come together, although there already was talk this past winter that they were looking to trade Baldelli for prospects and replace him with Elijah Dukes). The 2007 infield of Ty Wigginton, Akinori Iwamura and Ben Zobrist has been replaced by Callis in 2010 with BJ Upton, Evan Longoria and Reid Brignac. And Scott Kazmir has teamed with prospects Jeff Niemann and Jacob McGee and projected 2007 first overall draft pick David Price to give the team four front-line starters.
Here is where all those phrases about the end of the beginning, and light at the end of the tunnel, come to mind.
AL Central
Incredibly, it was just 2003 in which this division was so weak that the Kansas City Royals rode a hot April to contention in September, and the Tigers, despite having just endured a 119 loss season, could think that the addition of Pudge Rodriguez and a few other bona fide players might actually put them into contention.
No more. The AL Central is now the deepest, strongest division in baseball. It produced the AL wild card in 2006, and looks set to do so again in 2007, despite the fact that four of the five teams in the division are legitimate contenders who will be banging on each other incessantly throughout the season.
Cleveland Indians (1st, 91-71, division title 41%, wild card 17%)
On March 22 in Lakeland, Florida, the Indians turned 6 double plays against the Tigers, all of the 6-4-3 and 4-6-3 variety, providing a strong indication that the addition of Josh Barfield at second and the rejuvenation of Jhonny Peralta at short could pay dividends this season. The thing is, they lost the game, 5-4.
As we said in our 2006 season projections, “The 2005 Indians [93-69] did everything right except win the close games. They outscored their opponents by 206 runs and outproduced them by 497 total bases and walks, far exceeding the next-best team in the majors, and leaving the rest of the AL Central in their dust. Unfortunately, a 22-36 record in one-run games left them behind the White Sox and out of the action in October.”
So, what happened in 2006? They did everything required to meet, or even exceed, our 88 win projection for them, everything, that is, except actually win games. Their 78 wins was a whopping 12 fewer than the Pythagorean projection based on their +88 run differential.
Following each season we’ve taken to writing an article entitled, Measuring Team Efficiency. This year’s article details the historic inefficiency the Indians displayed in 2006. TBW refers to total bases + walks, and looking at a team’s won-lost record compared to its TBW for/against differential is another way of gauging a team’s over- or under-achievement. In the AL in 2006 the Indians “were second in TBW differential, fourth in run margin, and tied for tenth in wins. That’s not easy to do. Cleveland’s TBW differential of +276 is in the top 12% of all teams in the past third of a century. Fully 90% of those teams won at least 90 games, and the 2006 Indians are only the third team in that group to lose more games than they won.”
An incredible run of ill-fortune? Or is there something about this team that defies conventional analysis? The warning signs were there in our 2006 season simulations, because their projected +91 scoring margin normally would have been good for 91 wins, but they averaged only 88.
The bony finger of blame was pointed squarely at the bullpen in 2006, although the Indians actually reduced their negative won-lost differential in one run games last year from -14 in 2005 to -8 (18-26). Nevertheless, seven games lost in which the team was leading after eight innings, 27 losses of record in relief, 21 blown saves in 45 opportunities, and 45.9% of inherited runners scoring, is very, very bad. It just doesn’t seem that free agents Joe Borowski, Roberto Hernandez and Aaron Fultz are a potent enough remedy for this malady, nor do our season simulations suggest otherwise. (Joe Borowski’s projected record as the Indians closer is 4-8 29/38 4.46.)
Perhaps, however, with this team’s starting rotation, near enough is good enough out of the pen. The Indians project to allow the second fewest runs in the AL, improving from 782 in 2006 to 738, led by C.C. Sabathia (15-8 3.50), Jeremy Sowers (14-8 3.60) and Jake Westbrook (14-9 3.78), while the Tigers and Twins are projected to regress from the miserly 675 and 683 runs they allowed in 2006 to 763 and 740, respectively, in 2007.
2007 may indeed prove to be the Indians’ year. If, however, they produce yet another season of underachievement in the won-lost column, skeptics may be called upon to reconsider whether an ability (or inability) to win, independent of conventional measures of performance, does in fact exist. As Bert Gordon (George C. Scott) said to “Fast Eddie” Felson (Paul Newman) in The Hustler (paraphrasing from memory), “This isn’t football. They don’t pay you for yardage. At the end of the game you count up your money, and that’s how you know who’s best.”
Detroit Tigers (2nd, 89-73, division title 31%, wild card 15%)
Although for 2006 we projected the Tigers to win just 79 games, we did observe that they could be one of the season’s pleasant surprises, and even contend if three or four things went their way.
At least that many things went their way in a season full of positives. Here’s the thing though: apart from the addition of Gary Sheffield (not to be underestimated), this team hasn’t changed much, and with all the great performances last season, there may be more downside than upside potential.
We do see a modest increase in scoring for the Tigers in 2007 to 842 runs, up from 822 in 2006. Interestingly, they got there despite just 9 HR and 35 RBI from LF Craig Monroe (28/92 in 2006), with Marcus Thames, the subject of trade rumors throughout the winter and spring, taking playing time from Monroe and belting a team leading 37 HR.
It’s on the other side of the scoring ledger that we project the team’s vaunted pitching to slip, allowing 763 runs compared to last season’s major league low 675. And the fact is that 2006 ROY Justin Verlander has struggled this spring, Todd Jones and Kenny Rogers (who will open the season on the 15 day DL with a “tired arm”) are a year older, Jeremy Bonderman still hasn’t come up with a consistent change up, and Jamie Walker took the money and moved to Baltimore (those canny Orioles!)
Still, the Tigers have a lot of pitching. Key veterans with past injury histories, like Carlos Guillen, Magglio Ordonez and Pudge Rodriguez, remaining healthy and productive throughout 2006 were a key to the team’s success. Similar good fortune may be even more crucial in 2007.
Minnesota Twins (3rd, 87-75, division title 24%, wild card 10%)
The Twins came to spring training with four spots up for grabs in their starting rotation. As in 2006, when they opted to open the season with veterans Tony Batista and Juan Castro manning the left side of the infield, the Twins signed retread starters Sidney Ponson and Ramon Ortiz for 2007 to fill two of those vacancies.
The team’s remarkable turnaround last season began when Nick Punto and Jason Bartlett replaced Batista and Castro. We didn’t wait that long for youth to be served in our 2007 simulations, going with a rotation behind Johan Santana of (projected 2007 records): Boof Bonser (10-11 5.05), Carlos Silva (11-12 4.83), Matt Garza (11-10 4.63) and Scott Baker (10-11 4.86).
Ponson and Ortiz actually pitched reasonably well this spring, and will open the season in the rotation with Santana, Bonser, and Silva (who pitched poorly), but the likelihood that either will do any better (assuming they keep their jobs) than Garza and Baker did in our simulations seems pretty small. Rather, whether the Twins (who, after all, have won the division four of the last five seasons) can once again survive the AL Central affray to capture a postseason berth, will rest squarely on the shoulders of their Big Four of Santana, Joe Nathan, Justin Morneau and Joe Mauer (and the “stress reaction” in Mauer’s leg is worrisome). There’s no room for error in the Central, and little prospect that the Twins could replace what these guys give them if they don’t each put in a top season again in 2007.
Chicago White Sox (4th, 78-84, division title 3.8%, wild card 1.9%)
It’s no mystery what has happened to the White Sox since 2005. The 2005 team was all about pitching, scoring a modest 741 runs but allowing just 645. They didn’t stand pat after their Series win, adding Jim Thome to the lineup and Javier Vazquez (at the cost of CF prospect Chris Young) to the rotation. And the offense took a huge leap forward, scoring 865 runs in 2006. But the gain in scoring was more than offset by the struggles of the pitching staff, which allowed 794, with big drop-offs by the four holdover starters.
We see the rotation as a group performing in 2007 more like it did in 2006 than 2005. In fact, we project another jump in runs allowed by the White Sox, to 870 (exceeded in the AL only by Kansas City and Tampa Bay), with runs scored easing to 827. It looks like it will be all about the pitching for the White Sox again in 2007, but unlike 2005, that may not be a very good thing.
Kansas City Royals (5th, 66-96, no postseason appearances)
While the Royals may not be that much better this year than last, they are at least more interesting.
Positives for the Royals include:
The biggest negative, of course, is that they are on the bottom of the Central, looking up at the Indians, Tigers, Twins and White Sox.
AL West
If a team in the AL West wants to play in the postseason in 2007, they’d better win the division, because our projections offer them slim hope of a wild card berth.
Los Angeles Angels (1st, 91-71, division title 75%, wild card 3.1%)
Since 2002 it’s pretty much been a two horse race in the AL West between the Angels and A’s. In 2006, the teams couldn’t have been much closer, with the Angels scoring just five runs less than the A’s, while allowing just five more.
We see the two teams moving in opposite directions in 2007. Our projections have the Angels reducing their runs allowed from 732 to a league best 711 (with the A’s increasing from 727 to 748), and increasing their runs scored from 766 to a division best 810 (with the A’s increasing only slightly from 771 to 778), boosted by the addition of CF Gary Matthews Jr and a full season from 2B Howie Kendrick.
(So why, with a +65 improvement in their run differential, has the Angels’ projected win total gone up by just two from 89 in 2006? Because last year they outperformed their Pythagorean projection by five wins. That still wasn’t good enough to overtake the A’s, whose run differential was only +10 better than the Angels, because the A’s outperformed their Pythagorean projection by even more.)
If there are any clouds on the postseason horizon for the Angels, they are the injuries to Jered Weaver and Chone Figgins (neither of which was factored into our simulations), which are the sort of injuries that could linger beyond April.
Oakland A’s (2nd, 84-78, division title 20%, wild card 5%)
We projected the A’s to finish first in the AL West every season from 2001 to 2006, and they won it in three of those years (the Angels won it in two others), and took the wild card in another (with 102 wins in 2001, the year Seattle won a mind boggling 116 regular season games). We projected the A’s to win over 90 games in every one of those seasons except 2005, and they did.
The A’s have done it by leveraging risk, and there’s plenty of that on their 2007 roster. The thing is, the downturn we’ve projected for 2007 occurs despite having given them the benefit of that leverage in averaging our simulated seasons. We’ve got Bobby Crosby, Mark Ellis, Shannon Stewart, Eric Chavez, Milton Bradley, and even Rich Harden, all remaining reasonably healthy and productive for the entire year. And our projection includes a 27 HR, 98 RBI season from Mike Piazza.
Even if the A’s repeated their level of production from 2006, they likely would be going backwards in the win column, given that they outperformed their Pythagorean projection by eight wins last season. The modest -14 slip in run differential we’ve projected only compounds the problem.
For years (at least in some quarters) the demise of the Oakland A’s has been greatly exaggerated. Until perhaps now.
Seattle Mariners (3rd, 77-85, division title 2.8%, wild card 2.2%)
It’s not like this team didn’t have some decent players to build on at the end of 2006. It’s the changes they’ve made since that leaves you scratching your head in wonderment. Is Jose Vidro supposed to be the second coming of Edgar Martinez? Because his projected 4/30/.271 batting line sure doesn’t look like it. On the other hand, Jeff Weaver’s projected 8-13 5.36 looks exactly like, well, Jeff Weaver.
They swapped Chris Snelling to the Nationals in the Vidro deal, then signed former National Jose Guillen to play right. Why wouldn’t you just put Snelling out there? The Nationals were desperate to unload Vidro, so refusing to give them Snelling wouldn’t have been a deal breaker. Was it that they thought Snelling would never stay healthy? Well, Guillen hasn’t exactly been Mr. Dependability the past three seasons either.
And wouldn’t it be nice if Rafael Soriano (for whom the Braves happily gave up Horatio Ramirez) were still around to set up (or, if necessary, replace the injured) J.J. Putz?
It’s possible that GM Bill Bavasi has been consulting Allison DuBois, and knows things no one else does, though it certainly seems more like he may be taking his advice from Patricia Arquette.
Texas Rangers (4th, 75-87, division title 2%, no wild cards)
Let’s take a journey together down Rangers memory lane:
Before the 2006 season we said: “Will a retooled starting rotation lead to great things in 2006? Maybe, but it doesn't look that way. . . . Kenny Rogers was their best pitcher in 2005, and he's in Detroit now. Chris Young was their second-best starter, and he was traded to San Diego . . .”
Before the 2005 season we said: “Only one of their starting pitchers, Ryan Drese, posted an ERA below the league average last year, and Drese's 4.20 figure carries some baggage.”
Before the 2004 season we said: “The ERAs of the 16 men who started at least one game for the 2003 Rangers were 4.85, 5.09, 5.49, 6.10, 6.23, 6.45, 6.85, 7.01, 7.11, 7.16, 7.30, 7.58, 8.35, 8.53, 11.40, and 12.00. John Thomson, the best of this bunch, is now in Atlanta. The guys who were north of 7.00 amassed a total of 61 starts, so this isn't just a handful of cup-of-coffee September starts that make the overall picture look worse than it really was. You could hardly do worse if you dumped them all and started over with replacement-level pitchers.”
Before the 2003 season we said: “In 2002, much was said and written about the Rangers' pitching woes -- Chan Ho Park was anything but an ace, several key relievers got hurt, and a number of blown leads turned what might have been a good start into a deep hole.”
The more things change in Texas, the more they stay the same.
NL East
Nothing will set tongues wagging like a player who has the temerity to predict victory for his team, the way Jimmy Rollins has for the Phillies. It could be, though, that a bit of swagger is just what that team needs.
We project a dogfight in the NL East this year between the Phillies, Mets and Braves. Like last year, however, when the Mets blew the doors off and ran away with the division, any one of these teams, if things go its way, could approximate that feat.
Philadelphia Phillies (1st, 85-77, division title 37%, wild card 16%)
Jimmy Rollins merely said what he claims all the Phillies players think: that they’re the team to beat in the NL East. Our projections back him up, even though for 2007 we have the Phillies winning exactly the same number of games, and registering almost identical runs scored and runs allowed, as they did in 2006.
Rollins, Ryan Howard and Chase Utley are terrific players. While the addition of C Rod Barajas and 3B Wes Helms might be questioned on another team, both are improvements over their predecessors, Mike Lieberthal and David Bell, and, together with the underappreciated Pat Burrell, Shane Victorino and Aaron Rowand, provide a solid supporting cast for those three.
If the Phils have a significant worry entering the season, it’s the health of their pitching staff. Freddy Garcia won’t be ready for Opening Day and (although he reportedly will only miss a week) may be showing the effects of all the mileage on his right arm. John Lieber, who had been the odd man out of the rotation, also is hurting now and unavailable to take Garcia’s place. And woe be the Phillies if Tom Gordon’s shoulder and elbow don’t make it through the season intact.
Atlanta Braves (2nd, 84-78, division title 32%, wild card 14%)
The streak (14 consecutive division titles from 1991 to 2005, excluding the aborted 1994 season) had to end sometime, and it did in 2006. After John Smoltz, the rotation reminded no one of Greg Maddux and Tom Glavine; the bullpen registered a league high 29 blown saves; and 2B Brian Giles had a poor season offensively and defensively.
GM John Schuerholtz is one of the few baseball executives whose personnel moves are given the benefit of the doubt by experts, who may be just humble enough to think, if not admit out loud, that he actually might know something that they don’t. Beginning with the acquisition of closer Bob Wickman during 2006 (from the Indians, ironically, who had the AL’s worst bullpen last year), Schuerholtz has rebuilt the Braves’relief corps, adding Rafael Soriano (for Horatio Ramirez) and Mike Gonzalez (plus SS prospect Brent Lillibridge, for Adam LaRoche). (Is it just a coincidence that Schuerholtz turned to the Mariners and Pirates, two of baseball’s biggest patsies in recent years, to make those deals?)
Giles was non-tendered (would any other team get less criticism for letting a player of his caliber go for nothing?) and will be replaced by the three times converted SS to 3B to OF to 2B Kelly Johnson, and LaRoche will be replaced by some combination of Scott Thorman and Craig Wilson. And while other teams were signing guys like Jason Marquis (Cubs, 3 years, $21 million) and Miguel Batista (Mariners, 3 years, $25 million), the Braves picked up a healthy Mark Redman (a durable league average left-handed starter) as a non-roster invitee, on a deal (now that he’s made the team) that will pay him a mere $750,000 plus incentives.
There are, of course, Braves players that everyone, not just Schuerholtz, knows about, including veterans Andruw and Chipper Jones and Edgar Renteria (another canny Schuerholtz pickup after his one eminently forgettable season in Boston), and young up-and-coming stars Jeff Francoeur and Brian McCann (just signed to an unprecedented six years, $26 million deal).
The Braves look to still be a few pieces short of returning to their prior dominance, but would anyone really be too surprised if they came out and smoked the NL East in 2007? After all, Schuerholtz may indeed know stuff that we don’t.
New York Mets (3rd, 82-80, division title 24%, wild card 6.8%)
The Mets won 97 games last year. No other team in the league won even 90.
Basically the entire lineup, which scored 834 runs (3rd best in the league), is back. The only noteworthy change is Moises Alou in LF, who can still rake and is an upgrade.
For all the scrutiny that the starting rotation is getting, is it really any worse than last year’s? The 2006 Mets had seven pitchers with ERA’s between 5.48 and 9.87 start a total of 36 games. Glavine, Hernandez and Maine are back; Pedro Martinez may be for the second half of 2007, and was basically just a .500 pitcher on a .600 team before he went down in 2006. Does anyone seriously think that losing Steve Trachsel will cost the Mets 15 games in the win column?
On the other hand, the Mets outplayed their Pythagorean projection in 2006 by five games, so you could say they begin 2007 from a 92-win baseline. Can Glavine and Hernandez really be counted on to continue their Old Man River acts indefinitely? Projected starters Mike Pelfrey and Oliver Perez are two of that group of seven pitchers from 2006 with those bloated ERA’s. Plus the bullpen has taken several hits, with the departures of Chad Bradford, Roberto Hernandez and Darren Oliver, and Duaner Sanchez out with injury.
Speculation aside, there is an objective and imposing obstacle in the Mets’ path in 2007, which is a killer inter-league schedule. Besides their usual subway series with the Yankees, they’ve also been scheduled to face the Tigers, Twins and A’s.
If there is one absolutely indispensable player on the Mets, it has to be Billy Wagner. In 2006 he saved 40 games with a 2.24 ERA. In our 2007 season simulations he averaged 30 saves from 38 opportunities with a 3.12 ERA. If the Mets are going to return to the postseason in 2007, they will need to find a way to bridge the gap to Wagner with the lead more often than that, and for him to slam the door decisively when they do.
Washington Nationals (4th, 75-87, division title 4.2%, wild card 2.3%)
If anything caught my eye when we first looked at the results of our season simulations, it was the Nationals finishing ahead of the Marlins in the East. This is a team that many are saying will be lucky to avoid 100 losses.
Nor, looking at their player stats averaged from our simulated seasons, is it readily apparent how they managed it. John Patterson did remain reasonably healthy and pitched pretty effectively, as did Shawn Hill, and Chad Cordero was around to close the entire year.
What is apparent, however, is that the team that will take the field already is several wins worse than the one that averaged 75 simulated wins. Nick Johnson and Alex Escobar, both of whom will open the season on the disabled list, played much more in our simulated seasons than we now know they will. It’s also probably more likely than not that Cordero will be dealt during the year. (We do not attempt to forecast deadline deals from also-rans to contenders in our season simulations.)
If we were to rerun our season simulations today, the Nationals would almost certainly drop to the bottom of the East. They would still, however, most likely dodge the infamy of a 100-loss season.
Florida Marlins (5th, 73-89, division title 2.5%, wild card 1%)
As much of a feel good story as the Marlins were in 2006, they actually ended up winning just 78 games, so our 2007 projection isn’t really that big a regression. And they averaged 73 wins in our simulated seasons with recently released Mike Koplove, not recently acquired Jorge Julio, closing.
Young players, even very good ones, are more likely to take two steps forward, then one step back, than they are to take two steps forward, then two more steps forward. So it’s not surprising that number crunching projections for the Marlins encounter this consolidation stumbling block. In fact, however, on the offensive side, we project the 2007 team to outscore last year’s slightly, 765 to 758. It’s the pitching that lets the team down, allowing 845 runs (next worst to Colorado’s 866) compared to just 772 in 2006.
Josh Johnson’s injury accounts for part of the difference, as does the departure of Joe Borowski. (How well Julio will do as Borowski’s replacement remains to be seen.) For this team to match, let alone surpass, last year’s surprising, if modest, success, it’s going to need a couple of pitchers to step up unexpectedly. A solid season from the surprise winner of the CF job, Alejandro De Aza, wouldn’t hurt either.
NL Central
The Cardinals ran away with the NL Central in 2000, 2002, 2004 and 2005. They came back to the pack in the other seasons (including 2006, in which they pulled off the division title with just 83 wins) to produce close races with Houston and (in 2001 and 2003) Chicago. We project another close three-way race between the Cardinals, Astros and Cubs in 2007, with the Cards again prevailing.
St Louis Cardinals (1st, 85-77, division title 40%, wild card 6.7%)
There’s not much change to the starting lineup, other than the addition of 2B Adam Kennedy, who strikes me as a natural fit for this team. Our simulations assumed a healthy Jim Edmonds and Juan Encarnacion, although both will probably begin the season on the DL, but the Cardinals have decent backup depth in the outfield. Mainly, however, the Cardinals have Albert Pujols.
It’s the pitching staff where there have been major changes. Jeff Suppan, Jason Marquis and Jeff Weaver, three-fifths of last year’s starting rotation, are gone, replaced by converted relievers Braden Looper and Adam Wainwright, and Kip Wells (at least until the anticipated midseason return of Mark Mulder). No problem, according to our projections, with both Looper and Wainwright, as well Anthony Reyes, performing solidly. Throw in ace Chris Carpenter and the Cardinals staff projects to allow just 728 runs in 2007, down from 762 in 2006 and best in the league.
If there are potential problems they’re in the bullpen, where the jury is still out on whether Jason Isringhausen can bounce back from season ending hip surgery in 2006. Our projections are based on the assumption that he will, to the relatively modest tune of 27 saves (from 37 opportunities) and a 4.18 ERA. Josh Kinney, gone for the year with elbow surgery, also was axed from our Cardinals squad before the simulated seasons were run.
Pujols, pitching, and defense, is the formula that looks to carry the Cardinals to yet another division title in 2007.
Chicago Cubs (2nd, 83-79, division title 28%, wild card 7.9%)
Imagine a game show, with Cubs fans the contestant: “Ok, Cubs fans, you can have an 83-79 season and second place finish to the Cardinals right now, or take what’s behind the curtain!”
Our projection for the Cubs represents a huge 17 win improvement from last season’s 66-96 debacle, though not quite enough to reach the postseason. Of course, they did commit close to $300 million this past winter to achieve it, including $136 million (8 years) to Alfonso Soriano, $75 million (5 years) to Aramis Ramirez, $40 million (4 years) to Ted Lilly, $21 million (3 years) to Jason Marquis, and $13 million (3 years) to Mark DeRosa.
One comforting thought for Cubs fans is that our projections did not assume any material contribution from Kerry Wood or Mark Prior. For those who continue to light candles for these two, however, anything positive that either of them actually does would be an unanticipated bonus.
Among the many things that went wrong for the Cubs last year was a meltdown by the bullpen, particularly closer Ryan Dempster. Sometimes, in setting our manager profiles for each team prior to running our simulated seasons, we conclude that there is a better player available on the roster to fill a role than the guy the team plans to use, and assume that the team will come to the same conclusion and make the change. We concluded that Dempster’s days as Cubs closer were numbered and, with Kerry Wood ruled out, slotted in Bobby Howry, who averaged 30 saves with a 3.68 ERA.
Houston Astros (3rd, 81-81, division title 18%, wild card 7.6%)
Assuming, as most seem to be, that Roger Clemens will return for yet another curtain call in 2007, he could well put the Astros over the top in a close NL Central race. But, with his buddy Andy Pettitte gone to New York, would he choose the Astros over the Yankees (or Red Sox), if they’ve dug yet another huge hole for themselves early in the season? You often hear how important it is that a team get off to a good start. The reason in this case is unusual, but might actually be legitimate.
Was Carlos Lee worth $100 million over six years? Few think so, but all we’re concerned with is whether he will make the Astros a better team in 2007? If you look at the question in terms of Lee replacing Willy Taveras in the lineup, the answer has to be a resounding yes. (Still, no matter how much firepower the Astros cram into the first six spots in the batting order, so long as 7-8-9 are occupied by Brad Ausmus, Adam Everett and the pitcher, they’re always going to be playing with one bat tied behind their backs.)
Jason Jennings should be a solid addition to the rotation behind Roy Oswalt, but we project poor seasons for worn out newcomer, Woody Williams (9-12 5.42), as well as inconsistent holdover Wandy Rodriguez (8-12 5.35), which is another reason why the Rocket would be a big difference maker. Another concern for the Astros has to be Brad Lidge. There’s a lot of analysis about to the effect that his season last year wasn’t really that bad, but personally, we don’t buy it, and if we had to bet on whether he would bounce back in 2007, pitch about the same as in 2006, or deteriorate even further, we ’d bet on the latter, though our projection is “about the same” (5-8 27/36 4.72).
Cincinnati Reds (4th, 77-85, division title 5.7%, wild card 4.2%)
The Reds got off to a strong start in 2006 and, thanks mainly to a near historic collapse by the Cardinals, were still remotely in contention at season’s end. In between, their bullpen became known as one of the best places in Cincinnati for women to meet an ever-changing stream of available men.
The Reds have some nice players: a bit of speed here, a bit of power there, a mix of youth and veterans, but we just can’t get that enthused. What were they thinking about this past winter? Was there some inscrutable plan afoot that would actually bring about some improvement? Consider, for example, signing Alex Gonzalez to replace Royce Clayton. That’s about as horizontal as you can get. Or how about bringing Mike Stanton and Dustin Hermansen through the revolving bullpen door? (Actually, signing Hermansen was about the only move they made that we liked.)
There is one thing that could see me getting enthused about the Reds. The first time Ken Griffey Jr is shelved by injury, Josh Hamilton is slotted into the lineup and proceeds to go on a tear that propels him to both the NL Rookie of the Year AND Comeback Player of the Year awards. That, as they say, would be something we ’d pay money to see.
Milwaukee Brewers (5th, 76-86, division title 7.7%, wild card 1.1%)
The Brewers enter this season with the look of an up-and-coming team, so our projected finish for them may be disappointing.
1B Prince Fielder, 2B Rickie Weeks and RF Corey Hart are exciting young players, though defense is not their strong suit. Geoff Jenkins and Kevin Mench should form a productive, if unhappy and expensive, platoon in LF. Some question the move of Bill Hall to CF and insertion of J.J. Hardy at SS; one can’t help wondering whether, in light of the indefinite absence of 3B Cory Koskie, Hall at 3B makes more sense than some combination of Craig Counsell and Tony Graffanino. The lack of production from 3B certainly was a factor in our projection for the Brewers to score just 738 runs in 2007 (next to lowest in the league).
On the mound, Jeff Suppan failed to live up to his four year, $42 million deal, averaging just 9-12 4.88 over our simulated seasons. That may have something to do with the fact that these aren’t the Cardinals defensively. The Brewers were 14th in the league in fielding percentage in 2006, and could be even weaker defensively in 2007 with the loss of Koskie and the shift of Hall to CF.
So, there are at least two obvious ways the Brewers might better our projection for them: if Suppan outperforms his individual projection, and if the team makes some kind of move to address the hole left by Koskie at 3B.
Pittsburgh Pirates (6th, 72-90, division title 0.8%, wild card 1.3%)
Even with the addition of 1B Adam LaRoche, who we project will come pretty close to replicating his big 2006 season, the Pirates still have the weakest offense in the major leagues. The Pirates did win 37 of their final 72 games last season despite scoring the fewest runs in the league during that period, but that doesn’t necessarily mean they’re on the cusp of winning.
Yes, they have some good young starting pitchers in Zach Duke, Ian Snell, Tom Gorzelanny and Paul Maholm. Chances are, however, over any stretch of games, good or bad, last season or in 2007, you’ll be able to say that the Pirates scored the fewest runs in the league. When they’re losing, and they’re sure to do plenty of that, it will be because they’re not scoring. If they manage a streak where they’re winning (which for them basically means breaking even), it will be in spite of not scoring.
So, now that they’ve actually lined up some decent pitchers, the Pirates are faced with two tasks, neither of which, based on their track record, one can feel very confident in them pulling off: improving the offense substantially, and doing it without botching up the pitching in the meantime.
NL West
The NL West might not be the best division in baseball, but it could have the most interesting assortment of teams. The Padres’ pitching makes them the class of the division; Ned Colletti continues accumulating “name” veterans; there is a buzz in Arizona, where a young, potential-laden lineup has been paired with a solid veteran rotation; the Giants will be looking to set some kind of record for the oldest team to reach the postseason; and the Rockies will be hoping that they’ve finally found a winning formula that works equally well at home and on the road.
San Diego Padres (1st, 88-74, division title 64%, wild card 7.2%)
San Diego allowed just 679 runs in 2006, by far the fewest in the league (Houston’s 719 was next best). We project that number to increase to 729 in 2007 (one more than the league best Cardinals). However, we see their runs scored increasing by an even greater margin, from 731 to 806. Rookie 3B Kevin Kouzmanoff (projected OPS .875) gets a lot of the credit for that, as does Russell Branyan, who we installed in LF in the team’s manager profile, and who justified that decision by belting 30 homers. (The Padres may end up with a platoon of Branyan and Terrmel Sledge, which could be pretty potent too.)
It’s the Padres’ pitching that really shines. While we’ve projected the Padres to slip a bit in runs allowed in 2007, our methodology is inherently conservative, and they could easily do even better in 2007 than they did last year. Jake Peavy, Chris Young and Clay Hensley are back in the rotation, and Chan Ho Park and Woody Williams have been replaced by Greg Maddux and David Wells. Trevor Hoffman is still there, of course, as is Scott Linebrink, constant trade rumors notwithstanding.
No one doubts Peavy’s ability, and he wasn’t at his best for a good part of 2006. Young and Hensley should only get better with another year’s experience. As for Maddux and Wells, what a fascinating addition. We can’t wait to watch these guys in action this season.
Los Angeles Dodgers (2nd, 81-81, division title 15%, wild card 11%)
Imagine, if you will, Colin Clive (the actor who played Dr. Baron Frankenstein in the horror classic) in the role of GM Ned Colletti. He places his roster on a platform that he raises to the heavens, where lightning strikes it, again and again. He lowers it back down, and sees it move, ever so slightly (not much range, but sure hands), and cries, “It’s alive! It’s alive!”
Okay, I know I'm reaching here. Then again, there’s a bit of Karloff about Jason Schmidt, Luis Gonzalez, and DePodesta holdover Brad Penny (though definitely not Juan Pierre). The funny thing is, I actually like this team, even though it has something of a parts-stitched-together quality about it, and even though our projection sees it regressing from last year’s 88 wins to 81, with a drop of 52 in runs scored (from 820 to 769) as well as an increase of 34 in runs allowed (from 751 to 785).
The biggest hit to the offense was replacing the departed J.D. Drew with Luis Gonzalez. Pierre also projects to be a downgrade from what Lofton provided in 2006, and Garciaparra and Kent are at the stage in their careers where some decline can be expected.
On the pitching side, there is at least a reasonable prospect of players outperforming their projections in a way that could recapture those lost wins from 2006, with Jason Schmidt and Brad Penny (really more of a Jekyll and Hyde than a Frankenstein’s monster, now that I think about it) having disappointed in our simulated seasons (though Gonzalez in LF and Pierre in CF won’t be making it any easier for them).
Arizona Diamondbacks (3rd, 79-83, division title 11%, wild card 4.2%)
Gone are Johnny Estrada, Craig Counsell, Luis Gonzalez and Shawn Green, half the Opening Day lineup from 2006. Taking their place are Miguel Montero, Stephen Drew, Chris Young and Carlos Quentin. With holdovers Conor Jackson, Chad Tracy, Orlando Hudson and Eric Byrnes, the Diamondbacks lineup has been transformed from old, slow and boring, to young, fast and exciting seemingly overnight.
All that youth and electricity in the lineup is nicely complemented by a potentially dominant veteran rotation, led by 2006 NL Cy Young winner Brandon Webb. Behind Webb are Livan Hernandez, Doug Davis and, of course, the Big Unit, Randy Johnson.
Jose Valverde is a question mark closing, as are the rest of his supporting cast in the bullpen; Johnson, Hernandez and Davis all have to prove they’ve still got what it takes; and the lineup, while exciting, is largely young and unproven. If there’s a consensus about this team, perhaps it’s that they’re a year away, but if a belief takes hold that this team is good enough to win in 2008, that could well become a self-fulfilling prophecy that propels them to success right now. They definitely will be exciting to watch if they get a sniff of contending.
San Francisco Giants (4th, 78-84, division title 6%, wild card 4.4%)
C Bengie Molina 33 1B Rich Aurilia 36 2B Ray Durham 35 3B Pedro Feliz 31 SS Omar Vizquel 39 LF Barry Bonds 42 CF Dave Roberts 34 RF Randy Winn 32
It’s easy to get too caught up in the age thing. It’s hardly a given that this lineup would be good enough to win the division, even if all the guys were in their primes. On the other hand, perhaps their age translates into the intangible asset, experience.
In the baseball classic of oral history, The Glory of Their Times, Chief Meyers talks about the 1916 pennant winning Brooklyn Robins, “a team of veterans. Nap Rucker, Jake Daubert, Colby Jack Coombs, Rube [Marquard], Zack Wheat, Hi Myers – we’d all been around a long time. . . . We won the pennant that year by just outsmarting the whole National League, that’s all. It was an old crippled-up club, and you might say, figuratively, they had to wrap us up in bandages and carry us out to play the World Series. We were all through.”
So, perhaps there is hope for the Giants in 2007, after all. (Incidentally, in 1916 Rucker was 31, Daubert 32, Coombs 33, Marquard 29, Wheat 28 and Myers 27. The Chief himself was the senior member of the team at age 35.)
Colorado Rockies (5th, 77-85, division title 4.5%, wild card 4.8%)
The Rockies have the distinction, of sorts, of having the highest percentage of division titles and wild cards of any of the six teams we have projected to finish last in their division.
They’ve been patient, sticking with manager Clint Hurdle through five losing seasons, and building from within with players like 3B Garrett Atkins, outfielders Matt Holliday and Brad Hawpe, and top prospects SS Troy Tulowitzki and C Chris Ianetta. Stung by trade talks, Todd Helton is desperate to rediscover his offensive prowess. And whether it’s the pitchers, the baseballs, or some combination of the two, they’ve shown signs of finally mastering that strangest of all baseball venues, Coors Field, reducing their runs allowed from 923 in 2004, to 862 in 2005, to 812 in 2006 (their lowest total since 1995, their first season in Coors, in which they won the NL wild card).
They expect, and are expected by the powers that be, to start winning in 2007. Our projections, however, put them right back where they were in 2006, at the bottom of the division with 77 wins.
]]>Last updated: October 12, 2005
The last weekend of the 2005 season featured a tight race for the NL wild card. Philadelphia won its last four but was left out of the postseason parade when Houston took care of business. The folks at the Philadelphia Daily News wondered how a playoff game might have turned out had Houston dropped one of its weekend games to fall into a tie, and we were more than happy to help out.
We set up the rosters using the stats for the 2005 season, set up the fatigue information by entering the number of pitches thrown in recent games by the pitchers on both teams, and played the game once. The paper ran a game story in the October 4th edition, but they didn't have room for the boxscore and play-by-play. Here's how the game turned out:
10/3/2005, Hou05-Phi05, Citizens Bank Park
1 2 3 4 5 6 7 8 9 R H E LOB DP
2005 Houston 2 0 0 0 0 0 0 0 0 2 5 0 6 0
2005 Philadelphia 0 0 2 3 0 0 0 0 x 5 5 2 3 1
Houston AB R H BI AVG Philadelphia AB R H BI AVG
Biggio 2b 3 0 0 0 .000 Rollins ss 4 1 1 2 .250
Taveras cf 4 1 0 0 .000 Lofton cf 4 0 0 0 .000
Ensberg 3b 4 1 2 2 .500 Utley 2b 3 0 0 0 .000
Berkman lf 4 0 1 0 .250 Abreu rf 3 1 1 0 .333
Lamb 1b 3 0 1 0 .333 Burrell lf 4 0 0 0 .000
Bagwell ph 0 0 0 0 .000 Wagner p 0 0 0 0 .000
Lane rf 4 0 0 0 .000 Howard 1b 2 1 0 0 .000
Ausmus c 4 0 0 0 .000 Bell 3b 3 1 1 3 .333
Everett ss 3 0 1 0 .333 Lieberthal c 3 1 2 0 .667
Vizcaino ph 1 0 0 0 .000 Padilla p 1 0 0 0 .000
Backe p 1 0 0 0 .000 Michaels ph 1 0 0 0 .000
Palmeiro ph 1 0 0 0 .000 Madson p 0 0 0 0 .000
Qualls p 0 0 0 0 .000 Chavez lf 0 0 0 0 .000
Burke ph 1 0 0 0 .000 28 5 5 5
Gallo p 0 0 0 0 .000
Wheeler p 0 0 0 0 .000
33 2 5 2
Houston INN H R ER BB K PCH STR ERA
Backe L 0-1 4.0 4 5 5 3 3 75 41 11.25
Qualls 2.0 1 0 0 0 1 27 18 0.00
Gallo 1.2 0 0 0 0 3 22 16 0.00
Wheeler 0.1 0 0 0 0 1 7 5 0.00
8.0 5 5 5 3 8 131 80
Philadelphia INN H R ER BB K PCH STR ERA
Padilla W 1-0 7.0 4 2 1 1 6 101 70 1.29
Madson H 1 1.0 1 0 0 0 0 14 9 0.00
Wagner S 1 1.0 0 0 0 1 2 14 7 0.00
9.0 5 2 1 2 8 129 86
Hou: Palmeiro batted for Backe in the 5th
Burke batted for Qualls in the 7th
Bagwell batted for Lamb in the 9th
Vizcaino batted for Everett in the 9th
Phi: Michaels batted for Padilla in the 7th
Chavez inserted at lf in the 9th
E-Utley 2. 2B-Ensberg, Berkman, Everett, Abreu. HR-Ensberg(1), Rollins(1),
Bell(1). K-Taveras, Ensberg, Lamb, Lane 2, Ausmus, Backe, Vizcaino,
Rollins 2, Utley, Burrell 3, Bell, Michaels. BB-Biggio, Bagwell, Utley,
Abreu, Howard. SH-Padilla. WP-Backe, Wagner.
GWRBI: Bell
Temperature: 82, Sky: clear, Wind: out to center at 6 MPH.
10/3/2005, Hou05-Phi05, Citizens Bank Park
1 2 3 4 5 6 7 8 9 R H E LOB DP
2005 Houston 2 0 0 0 0 0 0 0 0 2 5 0 6 0
2005 Philadelphia 0 0 2 3 0 0 0 0 x 5 5 2 3 1
Score O Rnr BS Event
----- - --- -- -----
************** Top of the 1st inning, Houston batting
0-0 0 --- 22 Biggio grounded out to the mound (BBFCX)
0-0 1 --- 00 Taveras to first on an error by the second baseman Utley
(X)
0-0 1 1-- 01 Ensberg homered deep to left, Taveras scored (CX)
2-0 1 --- 10 Berkman flied out to left (BX)
2-0 2 --- 11 Lamb lined a single to right center (BFX)
2-0 2 1-- 21 Lane flied out to center (BBCX)
************** Bottom of the 1st inning, Philadelphia batting
2-0 0 --- 22 Rollins struck out (BFFBS)
2-0 1 --- 10 Lofton grounded out to second (BX)
2-0 2 --- 32 Utley walked (BBBCSB)
2-0 2 1-- 32 Abreu grounded out to second (FBBCB>X)
************** Top of the 2nd inning, Houston batting
2-0 0 --- 20 Ausmus flied out to center (BBX)
2-0 1 --- 22 Everett grounded a double down the first base line
(BCBFFX)
2-0 1 -2- 12 Backe struck out (BSSC)
2-0 2 -2- 12 Biggio grounded out to the mound (BCCX)
************** Bottom of the 2nd inning, Philadelphia batting
2-0 0 --- 32 Burrell struck out (BBCCFBC)
2-0 1 --- 01 Howard flied out to center (SX)
2-0 2 --- 11 Bell grounded out to short (FBX)
************** Top of the 3rd inning, Houston batting
2-0 0 --- 10 Taveras to first on an error by the second baseman Utley
(BX)
2-0 0 1-- 32 Ensberg struck out (BFFBBC)
2-0 1 1-- 02 Berkman grounded into a double play, Rollins to Utley to
Howard (CFX)
************** Bottom of the 3rd inning, Philadelphia batting
2-0 0 --- 10 Lieberthal lined a single to left (BX)
2-0 0 1-- 01 Padilla popped out on a bunt to first (CbXb)
2-0 1 1-- 31 Rollins homered deep to right center, Lieberthal scored
(BBFBX)
2-2 1 --- 10 Lofton grounded out to short (BX)
2-2 2 --- 02 Utley lined out to right (CCX)
************** Top of the 4th inning, Houston batting
2-2 0 --- 12 Lamb flied out to left (CFFBX)
2-2 1 --- 02 Lane grounded out to short (CCX)
2-2 2 --- 12 Ausmus struck out (CBFS)
************** Bottom of the 4th inning, Philadelphia batting
2-2 0 --- 32 Abreu walked (CBBFBB)
2-2 0 1-- 12 Burrell struck out (CBFC)
2-2 1 1-- 21 Backe threw a wild pitch, Abreu to second (CBBB)
2-2 1 -2- 31 Howard walked (CBBB.B)
2-2 1 12- 20 Bell homered deep to left, Abreu scored, Howard scored
(BBX)
2-5 1 --- 10 Lieberthal lined a single down the right field line (BX)
2-5 1 1-- 21 Padilla sacrifice bunted to the mound, Lieberthal to
second (BbBbFbXb)
2-5 2 -2- 12 Rollins lined out to center (SFFBFX)
************** Top of the 5th inning, Houston batting
2-5 0 --- 02 Everett flied out to center (CFX)
Palmeiro pinch hitting for Backe
2-5 1 --- 01 Palmeiro flied out to center (FX)
2-5 2 --- 31 Biggio walked (FBBBB)
2-5 2 1-- 02 Taveras struck out (CFC)
************** Bottom of the 5th inning, Philadelphia batting
Qualls now pitching
2-5 0 --- 22 Lofton grounded out to first (BFFFBFX)
2-5 1 --- 10 Utley grounded out to second (BX)
2-5 2 --- 32 Abreu lined a double to right center (FBCBBX)
2-5 2 -2- 12 Burrell popped out to third (FCBX)
************** Top of the 6th inning, Houston batting
2-5 0 --- 02 Ensberg lined out to center (FSX)
2-5 1 --- 32 Berkman lined a double to right center (BFFBBX)
2-5 1 -2- 22 Lamb struck out (BFCBS)
2-5 2 -2- 22 Lane struck out (BCCBFC)
************** Bottom of the 6th inning, Philadelphia batting
2-5 0 --- 10 Howard grounded out to third (BX)
2-5 1 --- 12 Bell struck out (CBFS)
2-5 2 --- 01 Lieberthal grounded out to short (FX)
************** Top of the 7th inning, Houston batting
2-5 0 --- 01 Ausmus flied out to left (FX)
2-5 1 --- 12 Everett lined out to first (BSCFX)
Burke pinch hitting for Qualls
2-5 2 --- 12 Burke flied out to left (CBSX)
************** Bottom of the 7th inning, Philadelphia batting
Gallo now pitching
Michaels pinch hitting for Padilla
2-5 0 --- 32 Michaels struck out (SBBBFS)
2-5 1 --- 22 Rollins struck out (CFFBFFFBS)
2-5 2 --- 01 Lofton flied out to center (CX)
************** Top of the 8th inning, Houston batting
Madson now pitching
2-5 0 --- 11 Biggio grounded out to short (FBX)
2-5 1 --- 22 Taveras flied out to center (CBFBX)
2-5 2 --- 01 Ensberg lined a double to right center (CX)
2-5 2 -2- 21 Berkman grounded out to second (BBCX)
************** Bottom of the 8th inning, Philadelphia batting
2-5 0 --- 12 Utley struck out (CFBC)
2-5 1 --- 00 Abreu grounded out to third (X)
Wheeler now pitching
2-5 2 --- 22 Burrell struck out (CCBBFFS)
************** Top of the 9th inning, Houston batting
Wagner now pitching
Chavez now playing left field
Bagwell pinch hitting for Lamb
2-5 0 --- 30 Bagwell walked (BBBB)
2-5 0 1-- 12 Lane struck out (BCCC)
2-5 1 1-- 00 Wagner threw a wild pitch, Bagwell to second (B)
2-5 1 -2- 10 Ausmus popped out to short (B.X)
Vizcaino pinch hitting for Everett
2-5 2 -2- 12 Vizcaino struck out (CCBS)
]]>
Tom Tippett
December 5, 2003
Each year, usually in November, Rawlings announces the winners of their annual Gold Glove awards, given to the top fielders in each league. The winners are chosen by a vote of the managers and coaches that is taken before the end of the regular season.
How much weight is put on great range versus soft hands or a good arm or the ability to turn the double play?
One hopes that the voters take all of those things into consideration, with the proper weight given to each skill. But we don't know. The announcement story rarely provides more than the basic info -- who won and how often each player has taken home the award. We're never given any proof that the best man won.
In contrast, when we're debating the MVP or Cy Young winner, nobody's at a loss for words ... my guy deserves the MVP because he nearly won the Triple Crown ... no, that's not right, you've got to give it to the man with the 11 game-winning hits in the second half ... a 2.20 ERA is worth more than 20 wins because, after all, the pitcher doesn't control how much run support he gets ... no, those 55 saves are far more valuable, because the game is always over as soon as he takes the hill, and everybody on both teams knows it.
Not so for the Gold Gloves. No statistics, no debate, no analysis. Nothing.
A few years ago, we began trying to fill this void with our own analysis of the Gold Glove selections, and we've been at it ever since. Writing this article is a natural extension of the work we do each winter (and have done since 1986) to develop fielding ratings for the annual Diamond Mind Baseball season disk.
That work involves looking at defensive performance from many angles in our attempt to form the clearest possible picture of the contribution made by each player to his team's defensive effort:
We believe very strongly that it is only through a combination of these methods that one can accurately evaluate defensive performance. (For a more detailed description of this approach, see the Evaluating Defense article on our web site.)
Do the Gold Glove voters have this information at their disposal when making their selections? It's doubtful. More likely, their votes are based on traditional fielding statistics, reputations, and appearances. That's not necessarily a bad thing. In a meaningful number of cases each year, our analysis concurs with the Gold Glove selections, in part because the best fielders are going to look good no matter how you evaluate them.
But there are some differences, so let's get right to it. We'll go position by position, commenting on the Gold Glove winners (who are listed in the title for all positions other than outfield) and other candidates that we believe were deserving of serious consideration. When we're done, we'll recap by comparing our Gold Glove choices to the official winners and offer a few comments on other players who caught our eye as we did the fielding ratings for our 2003 Season Disk.
If you're looking for pitchers who fielded their position without making an error, the list begins with Derek Lowe (65 error-free chances), Mark Buehrle (53), Mike Mussina (49), Brett Tomko (48), Danny Graves (47), Jon Garland (46), Cory Lidle (46), and Mark Mulder (45).
If you can forgive an error or two in favor of a guy who makes a lot of plays, then your leading candidates are Tim Hudson (2 errors in 76 chances), Roy Halladay (1 in 75), Greg Maddux (2 in 73), Carlos Zambrano (4 in 70), Mike Hampton (1 in 68), Derek Lowe (0 in 65), Livan Hernandez (1 in 63).
But this approach is a bit simplistic, mainly because a pitcher's own tendency to induce ground balls is a huge factor in the number of assists and putouts he gets. Fielding skill helps, of course, but you can really pad your numbers if you can get batters to hit it back to you in the first place. Five of the pitchers we've mentioned -- Lowe, Halladay, Hudson, Mulder, and Maddux -- are among the top twenty starters in ground-ball percentage.
A different group of pitchers emerges when you consider the relationship of plays made to opportunities. Among the standouts in 2003 were Kenny Rogers (a Gold Glover in 2002), Jae Weong Seo, Jon Garland, and Javier Vazquez. But it's hard to judge pitchers on only one season because they typically get dozens of chances to make plays, while other fielders get hundreds of opportunities.
If we extend our review of pitchers who convert a high percentage of chances into outs to include the last three years, the list is topped by Rogers, Steve Sparks, Graves, Tom Glavine, Kirk Rueter, Livan Hernandez, Vazquez, Randy Wolf, Garland, Steve Trachsel, and Mussina. Buehrle, Hampton, Maddux, and Halladay are a little further down this list.
Mussina was a good pick, in my view, because he was in the league's top tier in turning batted balls into outs, was third in the league in error-free chances, controlled the running game (only 9 steals in 19 attempts), and has done these things well enough in the past to show that this was not a fluke.
But he wasn't the BEST pick. Kenny Rogers made more plays, both in absolute terms and relative to the number of balls hit his way, REALLY shut down the running game (only 4 stolen bases allowed all year, 3 pickoffs), and tied for second in the league (behind Sparks) with 4 double plays. Yes, he made two errors, but that doesn't cancel everything else, and Rogers gets my vote.
Mike Hampton is similar to Mussina in that he's done enough to be considered a serious candidate. Second in the league in total chances, only one error, very good in the running game (only 3 steals allowed in 9 attempts), and a good track record. But Hampton's a ground-ball pitcher who creates lots of chances to make plays, and he was only a little better than average in converting those chances into outs.
Javier Vazquez, on the other hand, is a fly-ball pitcher who still manages to accumulate a good number of successful chances each year. That's because he's always at or near the top of our rankings in converting opportunities into outs. And he allowed only three steals in five attempts all year.
Danny Graves is another impressive candidate. Second in the league in error- free chances handled, among the leaders in converting chances into outs, both this year and in recent years.
But my vote goes to Kirk Rueter. He handled 43 chances without an error in 2003. In fact, he hasn't made an error since 1999, successfully completing 209 plays in the last four years. Rueter had a hand in 5 double plays, one shy of the league lead. And he continues to be nearly impossible to run on. He may not have the greatest stuff in the league, but he does a lot of other things to keep himself in the game.
Ivan Rodriguez owned this award for a long time, but knee problems have taken their toll and it's no longer a slam dunk in his favor. Still, he continues to be a top contender. Opposing base stealers were successful 68% of the time, an ordinary figure, but only one other regular catcher was challenged less often, so it's clear that I-Rod's gun still has some bullets in it. But with 8 errors and 10 passed balls, I can't make him my choice.
Mike Matheny was the least-challenged catcher in the majors this year, with a runner taking off only once every 19.9 innings. But those runners arrived safely 77% of the time, an unusually high percentage with Matheny behind the plate. Still, St. Louis allowed the second-fewest number of steals of any NL team, and Matheny caught in 138 of those games without making a single error. He was also second (to Brad Ausmus) in the league in fewest passed balls allowed among catchers with at least 1000 innings.
Speaking of Ausmus, he's difficult to evaluate because his manager (Jimy Williams) has a history of telling his pitchers to forget about the running game and concentrate on the hitters. It wasn't long ago that Ausmus was throwing out half the runners who dared challenge him. This year, it was only 31%, but that's quite good on a Williams team. Plus, Ausmus made only 3 errors, allowed only 3 passed balls, and took part in a major-league leading 10 double plays.
Another candidate was Montreal's Brian Schneider, who led the circuit by throwing out 47% of enemy base runners and contributed to 9 double plays while making only 3 errors and allowing 3 passed balls. But Schneider started only 95 games, compared to 129 for Ausmus and 121 for Matheny, and that hurts his case.
All things considered, my pick is Matheny by a nose over Ausmus and Schneider.
In the AL, Bengie Molina tied for the league lead by nailing 41% of the runners who challenged his arm. His fielding percentage was only a hair above average, but he was among the league's best at preventing passed balls. His biggest weakness is fielding bunts and other balls around the plate, a category in which he's been well below average for three years.
Tampa Bay's Toby Hall is an interesting candidate this year. He's more agile around the plate than Molina, and like Molina, Hall wiped out 41% of enemy base- stealers. Further, 81 runners challenged Molina in 950 innings behind the plate, while only 78 tested Hall in his 1107 innings. Only Seattle and Chicago allowed fewer stolen bases than the D'Rays in 2003. On the other hand, Hall's 9 errors and 7 passed balls are unimpressive.
Chicago's Miguel Olivo is much like Hall. Olivo may have the league's best arm, but his 9 errors and 8 passed balls hurt his case, and he started 28 fewer games than Hall did.
If Dan Wilson (92 starts) didn't share the position with Ben Davis, he'd get my vote. He was part of the duo that led the league in fewest steals allowed, he led the league in fielding percentage (only one error), and shared the lead in fewest passed balls allowed among catchers with at least 800 innings. But it's hard to pick a guy who caught only 57% of his team's innings, so I'll concur with the voters and give the nod to Molina.
In the AL, the voters chose John Olerud for the second year in a row. In my view, it should have been a two-horse race between Doug Mientkiewicz and Travis Lee, with Mientkiewicz winning by a few lengths and the rest of the field a long way back.
But let's see how Olerud and Mientkiewicz compare:
All things considered, Mientkiewicz's advantage in range is much greater than Olerud's in the other areas, and he gets my vote for the third year in a row.
In the NL, Derrek Lee got the nod for the first time. In my view, Todd Helton and Tino Martinez are the only other serious candidates, but I'll focus on Lee versus Helton because both started at least 27 more games than Martinez and surpassed him in most key measures. Here's how I see these two:
So we have a big edge in range for Helton and advantages for Lee in errors by himself and his fellow infielders and in starting double plays. Add it all up and it's too close to call, so I'll take a page from the NFL's instant replay system. If there's no conclusive evidence, you go with the call that was made on the field, and that makes Lee my choice.
The AL race should have been between Oakland's Mark Ellis and Anaheim's Adam Kennedy.
This was Bret Boone's second Gold Glove, and as was the case the first time, his trump card was reliability. His .990 mark was good enough to share the league lead with Kennedy. Boone was also very good at starting double plays, though it's interesting to note that he was below average before he joined Seattle in 2001, so his teammates may deserve much of the credit for the improvement in Boone's numbers. He was around the league average in making the pivot on potential double play balls that were hit to others.
But Boone's range has never been anything to write home about. This year, his range factor was second-worst in the majors. It's true that his range factor suffered greatly because he played behind a fly-ball staff, but even after adjusting for that and other factors (such as strikeout rate and left/right mix), Boone is only a little above average. In fact, he was in the middle of the pack in just about every measure of range that we look at.
Kennedy, on the other hand, has been near the top of our range rankings three years running. Like Boone, he was very reliable. Kennedy was also above average in starting double plays, though not as much as Boone. Kennedy's pivot numbers aren't especially good, but it's hard to tell whether that's him or the guy feeding him the ball. Finally, the fact that Kennedy started only 125 games at the position is a negative.
Mark Ellis is a very interesting candidate. Ellis blew away the competition in our net plays analysis and the STATS zone rating, and was near the top (but behind Kennedy) in adjusted range factor. It's not unusual for a converted shortstop to shine at second, and Ellis put up very good numbers in a half- season at the position in 2002. Perhaps because he is a converted shortstop, Ellis lags behind his peers in both starting and making the pivot on potential double play balls. His error rate was average.
In my opinion, Ellis's huge advantage in range makes him more worthy than the more polished Boone. So my ballot, if I had one, would have read Ellis first and Kennedy second.
One more thing before I move on to the other league. ESPN.com's story about the Gold Glove selections included this comment by an unnamed AL coach: "I voted for Adam Kennedy because he made some great plays against us and I happened to catch Bret when he made a couple of errors." We have no way of knowing whether this is typical of the amount of thought that goes into the voting, but it wouldn't surprise me if it is.
In the NL, my choice is Atlanta's Marcus Giles. Castillo's fielding percentage was a little better, but we're only talking about a difference of one error every six weeks. Castillo has always excelled in making the pivot on the double play, but Giles isn't too far behind. Giles topped Castillo in net plays, the STATS zone rating, and range factor (though with the help of a ground-ball staff). It's extremely close, but I'll go with Giles.
By the way, I think Placido Polanco was the best defensive second baseman in the league, but he only made 99 starts at the position before moving to third when Philly had to get David Bell's bat out of the lineup. Pokey Reese's injury took him out of the running.
Eric Chavez took home his third Gold Glove, and I have no quarrel with this decision. Chavez led the AL in many categories, including range factor, putouts, assists, double plays, and our net plays analysis.
His standing in the first four of those categories is a bit artificial -- he played more innings than anyone but Tony Batista, his staff induces a lot of ground balls, and Oakland had by far the highest percentage of innings by lefty pitchers in the majors, so Chavez saw a steady stream of right-handed batters who tend to pull the ball in his direction.
Chavez is no Brooks Robinson, but he's solidly above average in range, and he's reliable (third in the league in fielding percentage, only a hair behind the leader), and he did those things almost every day.
My choice last year was Cory Koskie of Minnesota, who had another very good year in the field. He led the league in fielding percentage and was above average in range again, but he's my runner-up this time. Damian Rolls is someone to watch. He didn't play enough (68 starts), and may never hit well enough to be a full- time player, but he looked good in every measure that we use.
Scott Rolen is a perennial standout who has made far more plays relative to the norm for his position than any other NL fielder over the past five years. But his performance showed a marked decline in 2003. His range factor and STATS zone rating were slightly below average. His double-play numbers, normally a strength, were down. In our net plays analysis, we're accustomed to seeing him come in at 40 plays above the league, but he was in the middle of the pack in 2003.
It's possible that injuries are at the root of this decline. In the 2002 playoffs, Rolen collided with a baserunner and sprained his shoulder badly enough to keep him out of action for the rest of the postseason. He has a history of back problems and missed games in 2003 with stiffness in his neck and back and soreness in both shoulders.
Still, we're puzzled by the sudden drop in his defensive numbers. Rolen had a very good year at the plate, so his ailments couldn't have bothered him too much, at least not while he was batting.
All in all, it appears that Rolen may have gotten this Gold Glove on reputation, not performance. Having said that, who do you give it to? Nobody else stands out.
David Bell showed terrific range again this year, but his anemic bat cost him his job, and he started only 81 games at third. (Some years, it seems as if you can win a Gold Glove with your bat. Bell may have just lost one that way.)
Adrian Beltre showed good range and posted a league-average fielding percentage, so he's a possibility, though his home park helps him look good. Morgan Ensberg was pretty good but only played a half a season. Craig Counsell and Jamey Carroll also look good, but they didn't play nearly enough, either. Aaron Boone was traded out of the league. Vinny Castilla showed good range and was a plus on the double play, but made 19 errors.
It comes down to Rolen versus Beltre, and it appears to me that Beltre had a slightly better year in 2003, so he's my choice. I love watching Rolen play third, however, so I hope he bounces back in a big way next year.
It's a classic question. Would you rather have a guy with great range but is somewhat error-prone or someone who's steadier but doesn't cover as much ground?
Alex Rodriguez was very steady again this year, posting a major-league best .989 fielding percentage and making only 8 errors in 158 starts. And while A-Rod will never make people forget Mark Belanger or Ozzie Smith, his range is no worse than average most years, and sometimes better. In other words, he's a good all-around pick.
His chief rivals in 2003 were Anaheim's David Eckstein, who was very reliable and showed more range than Rodriguez but played only 116 games, and Chicago's Jose Valentin, who got to an awful lot of balls but made 20 errors. Eckstein didn't play enough to be a serious candidate, so I'll focus on Valentin.
Valentin is somewhat error-prone, there's no question about that. His fielding percentage has lagged the league average every year he's been in the majors, sometimes by quite a bit. Since 2001, however, he's gotten better, making only 2-3 more errors per season than the average shortstop.
But Valentin has also been consistently better than the league in range during his career. In 2003, he led all major-league shortstops in net plays made and adjusted range factor, and he was second (behind Eckstein) in the STATS zone rating. Depending on which of these measures you prefer to go with, Valentin made somewhere between 20 and 56 more plays than the average shortstop. Taking the strengths and weaknesses of each of these measures into account, I'd put his contribution somewhere in the range of 30-35 plays.
This would make it his best defensive year, but it's not too far above the level he's set in previous years. Problem is, his tendency to make errors has occasionally cost him a full-time job, so we don't have a lot of recent history to go on. But if you extrapolate his part-time 2001 and 2002 seasons into full years, and if you adjust for all the errors he made in 2000, Valentin has consistently shown the ability to reach about 20 more balls per season than the average shortstop.
So my vote goes to Valentin, though not by a big margin. Rodriguez is a very solid choice, and I'm not knocking his game in any way, but Valentin has improved his error rate enough to convert his superior range into real value.
The NL winner, Edgar Renteria, is mister average. At no time during the past five years has he been more than four plays better or worse than the major- league norm in our net plays analysis. In 2003, compared with the average shortstop, Renteria made two fewer errors and converted two more batted balls into outs. He was a plus in making the pivot on double plays.
If that doesn't sound to you like a Gold Glover, I'd have to agree, so let's see who else shows up on the radar screen.
Chicago's Alex Gonzalez is a lot like Alex Rodriguez in that he's very reliable and, in a good year, shows above-average range, too. This was one of his good years, and Gonzalez converted 22 more batted balls into outs than the average shortstop. That's partly a reflection of range, and partly due to a very low error rate. Gonzalez tied for second in the majors in fielding percentage. He was also well above average making the pivot on double play balls.
Houston's Adam Everett led the majors in range factor, was fourth in net plays, and finished among the league leaders in the STATS zone rating. In both range and error rates, he was just a hair behind Gonzalez, but his double play performance was in the middle of the pack.
Cesar Izturis and Orlando Cabrera also deserve mention, but they didn't quite rise to the level of the other players I mentioned.
My vote goes to Gonzalez. And while we're talking about him, have you ever seen a postseason when so many highly-regarded fielders made critical errors? San Francisco's Jose Cruz misplayed a fly ball with Florida was on the ropes, an error by Gonzalez helped open the floodgates for Florida when they were on the brink of elimination in the championship series, and some bobbles by New York's Aaron Boone nearly helped Boston break through.
You won't get an argument from me about the AL choices, which were Mike Cameron and Ichiro Suzuki of Seattle and Minnesota's Torii Hunter.
Seattle's outfield was far and away the best in the majors at turning fly balls and line drives into outs. They can put three legitimate center fielders out there -- Mike Cameron, the best in the business right now, Ichiro, who was a Gold Glove center fielder in Japan, and Randy Winn, who played center in Tampa Bay before he was traded to Seattle last winter.
Cameron led all major league outfielders with 484 putouts, 47 more than runner- up Rocco Baldelli and 60 more than Hunter. It helps, of course, that he plays behind a fly-ball staff in a park that's very friendly to pitchers. But even when you account for those things, Cameron turned about 40 more batted balls into outs than did the average center fielder.
Ichiro's raw net-plays figure isn't all that impressive until you allow for the fact that he shares the right-field gap with Cameron, who was about 10 plays above average in those zones. Ichiro would have made some of those plays had Cameron not reached those balls first. In addition, Ichiro's speed and arm turned a bunch of doubles and triples into singles.
With the Seattle outfield performing at such a high level, I have no problem giving two of the league's three Gold Gloves to one team. Winn was among the leaders in left field, too, but there are other very good outfields in the league, and it would be a stretch to give all three to Seattle.
One of those very good outfields is in Minnesota, where Torii Hunter patrols center field and Jacque Jones is in left. Jones is once again our top-rated left fielder, but he started only 87 games in left after a midseason groin injury relegated him to a DH/PH role for much of the second half.
Hunter continues to be one of the leaders in highlight film plays, and he looked very good in all of our range metrics, too. We don't think Hunter makes quite as many plays as his reputation would suggest, but there's no question that he's one of the best center fielders in the game, and he's my pick as the third AL Gold Glove recipient.
There are several other AL outfielders who might be worthy of consideration if not for the presence of these three guys. Johnny Damon and Vernon Wells represent the next tier of AL center fielders and aren't all that far behind Hunter. Milton Bradley posted very good defensive numbers before he got hurt. Among the corner outfielders we noticed are Winn, Garrett Anderson, and (believe it or not) Carlos Lee.
I'm sure that last name will come as a surprise to many of you. It came as a big surprise to us, too, because Lee has a reputation as a defensive liability and has been removed for defensive purposes more often than any other fielder in recent years. As a result, we spent a lot of time studying his performance, and here's what we found:
Even after reviewing all of this information, I wasn't convinced. So I decided to spend some time with the MLB.com video clips service. I picked a six-week period and requested every play Lee was involved in.
(MLB.com's service isn't perfect, so I was able to get my hands on only about 80% of those plays. But think about that for a minute. I was able to call up dozens of video clips for a specific fielder in a matter of seconds, and it only cost me a few dollars. Yeah, it would have been nice if I found everything I was looking for, but how can I complain about some missing clips when such a thing wasn't even conceivable a few years ago?)
It took about three hours to view the clips that were available, and I came away very impressed. There must have been ten or eleven really good plays in that stretch. Among them were two long runs to flag down deep fly balls in the gap. On two other occasions, Lee reacted very quickly to line drives and made sliding catches to his left. Twice he went over the left field wall to save homeruns. And in what may have been his best play of that sequence, he covered a lot of ground to make a catch in foul territory while going up and over the bullpen mound at full speed.
Over in the NL, where the voters selected Andruw Jones, Jim Edmonds, and Jose Cruz, things weren't so clear.
None of the league's left fielders stood out. Rondell White is a very good fielder who doesn't get much credit, but he was traded to the other league. Geoff Jenkins has always been at or near the top of the class, but he battled injuries again in 2003. Neither was anywhere near Gold Glove caliber this year.
Three players stood out in right field. San Francisco's Jose Cruz topped our net plays analysis and led the majors in range factor and adjusted range factor. Florida's Juan Encarnacion wasn't far behind on all counts. And neither was Houston's Richard Hidalgo, who also led the majors with 22 outfield assists.
Cruz is a converted center fielder, and while he wasn't a standout at that position, it's not unusual for CFs to shine in the corner spots. In 2002, Cruz looked very good in a limited trial in left field, so I wasn't surprised when he showed well in right this year.
Park factors must be considered here. Pacific Bell Park is good for pitchers, especially on balls hit to right center, and that can artificially boost the numbers for the hometown right fielder. Cruz benefited from that in 2003, as did Reggie Sanders in 2002. But even with a significant park adjustment, Cruz remains among the leaders in right field. And he was second only to Hidalgo with 18 outfield assists.
Encarnacion also had a terrific season in right. In our net plays analysis, he and Cruz are very close after you make the park adjustments, and Encarnacion was number one in the STATS zone rating rankings. In addition, Encarnacion was the only major league outfielder to play at least 120 games without making an error.
Having said all that, the best defensive outfielders usually play center field, so we can't start nominating corner outfielders until we've considered the guys who play up the middle.
We might as well start this conversation with Andruw Jones. It hard to make it through a game, even if Atlanta's not playing, without hearing that he's the gold standard. But we've been seeing signs of a decline in his once-stellar defensive play for the past several years. We still think he's a good center fielder, but we believe he's been passed by Cameron, Erstad, Hunter, and a new wave of youngsters who haven't yet played enough to become household names.
Consider these facts about Jones:
All things considered, I don't think Jones is the same defensive player he was four years ago. But who in the NL is better? Most of the game's top center fielders are in the other league.
Among the NL regulars, San Diego's Mark Kotsay is on top of our rankings for net plays made, and Jim Edmonds is number one in the STATS zone ratings, though both lag the AL leaders on both counts. Kotsay also leads in baserunner kills, with Edmonds right behind him.
Juan Pierre led in putouts, but that was a combination of playing time (161 starts), a fly ball staff, and a pitcher-friendly park. His range factor was quite ordinary, he was below average on the STATS zone rating and in our net plays analysis. LA's Dave Roberts put up impressive numbers this year, but he started only 98 games in center. Age has caught up with Steve Finley.
Oh, before I forget, I promised to mention some of the guys who haven't played much. Jeff Duncan only played a quarter of a season but compiled defensive numbers that resembled Mike Cameron's. Tsoyoshi Shinjo once again posted outstanding range numbers in limited time; he's headed back to Japan, though, because he didn't hit well enough over here to become a starter. In the Carlos Lee discussion, I mentioned Willie Harris and Aaron Rowand, both of whom could become Gold Glove contenders if they hit well enough to play full time.
Well, I guess I've danced around the subject long enough, and it's time for me to go on the record with my NL picks. It's tough because none of the center fielders stood out. Center field is a more difficult position, though, so I don't think it's right to pick a bunch of corner outfielders just because they outperformed the other corner guys by a bigger margin than the CFs outperformed their peers.
So I'll choose two center fielders, Andruw Jones and Mark Kotsay, and the leading right fielder, Jose Cruz, as my 2003 picks. It's getting tougher every year to rubber-stamp the Jones selection, but I haven't seen quite enough evidence yet to conclude that he's no longer worthy. Kotsay, in my view, was a little better than Edmonds. Cruz wasn't too far ahead of Encarnacion and Hidalgo.
Here's how my selections compare with those of the voters:
------- American ------- ------- National ------- Pos Voters Diamond Mind Voters Diamond Mind P Mussina Rogers Hampton Rueter C Molina same Matheny same 1B Olerud Mientkiewicz Lee same 2B Boone Ellis Castilla Giles 3B Chavez same Rolen Beltre SS Rodriguez Valentin Renteria Gonzalez (Chi) OF Cameron same AJones same OF Ichiro same Edmonds Kotsay OF Hunter same Cruz same
We agree on nine of the eighteen selections. Last year we agreed on eight, and it was twelve in 2001.
Even though I would have gone in a different direction on half of these selections, I must say that the voters did a pretty good job. In most of the cases where we disagreed, the winner was on my short list, and even when he wasn't, the winner had some important things going for him.
Here are a few other players whose defensive performances we noticed, for better or worse, in 2003:
Jermaine Dye, RF -- Dye has been one of our top-rated right fielders for years but struggled to come back from a severely broken leg in 2002. He appeared to recover a little of his range this year, so we bumped him up from Poor to Fair.
Troy Glaus, 3B -- Glaus has bounced between our Average and Fair ratings over the years, but 2003 brought injuries to his right hand, left hamstring, left foot, back, and right shoulder. His performance suffered enough to drop his range rating to Poor, but could rebound a little next year if he's 100%.
Ken Griffey, CF -- For the third year in a row, Griffey tried to play through leg injuries, and once again he wasn't anywhere near his usual self. We rated him Poor because he just didn't make enough plays, but we expect his rating to improve with his health, assuming his health does improve at some point.
Vladimir Guerrero, RF -- He normally earns an Excellent or Very Good rating for range, but he played with a bad back for much of the season and his performance suffered enough that he was only Average this year. In fact, he was closer to Fair than Very Good.
Derek Jeter, SS -- Last in the majors in range factor. Last in the majors in adjusted range factor. Second last in the majors in zone rating. Last in the majors in our net plays analysis. And this year there were no mitigating factors. No brilliant third baseman who cut off a lot of balls that Jeter might have been able to handle, and his team was last in the league in converting ground balls into outs. So we gave him a Poor rating for range and an error rating that's around the league average.
Reggie Sanders, RF -- Earned our Excellent rating last year but slipped to Average in 2003. A year ago, we wondered whether his impressive defensive numbers had more to do with Pacific Bell Park than his own performance. After adjusting for the park, he was a borderline Ex/Vg, but we concluded that he had earned the Ex rating, in part because he had performed just as well in Arizona the year before. Now in his mid-thirties, a decline in his range is to be expected, but a drop of two rating points isn't something we see every day, so it's possible that we made the wrong call last year.
Larry Walker, RF -- It's always a challenge to rate Colorado outfielders because a much higher percentage of batted balls go for hits in Coors Field than any other place. We do our best to measure and adjust for those effects, but it's not an exact science. In most years, Walker's raw defensive numbers are below average, but he comes out looking pretty good after we adjust for the park. In 2003, his raw numbers were downright terrible and the park adjustment brought him up only to a Fair rating. All signs indicate that the decline was real but injury-related. During the season, Walker missed games due to a bad shoulder, a groin injury, and a knee problem, and is expected to undergo surgery on both the shoulder and the knee this winter.
Todd Walker, 2B -- Fans of range factors, take note. Walker was well above average in range factor in 2002, and that got some Red Sox fans talking about what an asset he was going to be in 2003. But that ranking had more to do with the Cincinnati pitching staff than Walker's own play that season, and our analysis put him near the Average/Fair boundary. He played well enough in 2002 to eke out an Average rating, but in 2003, he slipped back under that line. According to the local papers, the Red Sox didn't like his defense, and that's why they're not rushing to re-sign him despite his postseason batting heroics.
Rickie Weeks, 2B -- The number two overall pick in the draft in 2003, Weeks torched minor-league pitching in a brief stint before being called up in September. Normally, I wouldn't bother writing about a guy with a career total of 21 defensive innings, but Weeks made 8 errors in 23 professional games and his major-league range numbers were horrendous (albeit in a very small number of chances). This may be a statistical anomaly, but it's also possible that he's just not ready to play defense in the majors.
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Tom Tippett
December 5, 2002
Each year, usually in November, Rawlings announces the winners of their annual Gold Gloves for the best fielder at each position in each league. The announcement is normally carried in your local paper or on your favorite web site as a brief Associated Press story that tells us who won, which players are repeat winners, and how many times each player has won the award.
The selections are made by a vote of managers and coaches that is taken before the end of the regular season. I'm not aware of any guidelines that are provided to the voters, so I don't know how much weight they put on great range versus soft hands or a strong and accurate arm or the ability to turn the double play. One hopes that the voters take all of those things into consideration, with the proper weight placed on each skill, when they arrive at an overall assessment of each player's performance in the current season.Each year, usually in November, Rawlings announces the winners of their annual Gold Gloves for the best fielder at each position in each league. The announcement is normally carried in your local paper or on your favorite web site as a brief Associated Press story that tells us who won, which players are repeat winners, and how many times each player has won the award.
But we don't know how they made their decisions because the announcement story doesn't provide any justification for any of the selections. We never see any relevant numbers (except the occasional error total) or comments from the voters. Nothing.
So for the past several years, we've been offering up our own brand of analysis as we review the Gold Glove selections. What sort of analysis are we talking about? We look at defensive performance from several angles in our attempt to form the clearest possible picture of what each player contributed to his team's defensive effort. In the remainder of this article, you'll see the phrase "according to our analysis" a few times, and by that we mean a combination of the following:
We believe very strongly that it is only through a combination of these methods that one can accurately evaluate defensive performance. (For a more detailed description of this approach, see our Evaluating Defense article, which was first published several years ago and has been substantially updated for 2002.)
I'd be absolutely amazed to discover that the Gold Glove voters have any of this information at their disposal when making their selections. My assumption is that their votes are based on traditional fielding statistics, reputations, and appearances. That's not necessarily a bad thing. In a good number of cases each year, our analysis concurs with the Gold Glove selections, in part because the best fielders are going to look good no matter what methods you use to evaluate them.
But there are some differences, and we'll go through each position and discuss the players we view as being the most worthy candidates. At the end, we'll compare our Gold Glove choices to the official winners and offer a few comments on other players who caught our eye as we did the fielding ratings for our 2002 Season Disk.
Pitchers
There's a very strong tendency for Gold Glove voters to fixate on one guy and keep giving him the award year after year after year, as long as he doesn't get hurt or do anything to make it clear that something has changed. This tendency is especially strong for pitchers, perhaps because the voters don't get to see them as often as position players.
At other positions, we can judge performance over a span of 1,000 to 1,400 defensive innings, but even the most durable starting pitchers are in the field only for 200-250 innings. And relievers get only a fraction of the innings of a starting pitcher.
With 14 or 16 teams in the league, a voter might get to see a certain shortstop play 80 innings in the field. That's not much in the context of a whole season, but it sure beats the 10-20 innings they might see of a starting pitcher or the 4-5 innings a reliever might pitch in those games.
So it's hard for anyone to evaluate pitcher defense just by watching, because none of the voters is in position to watch enough pitchers in enough situations to get a complete picture. And it's hard to evaluate pitchers just by looking at their putouts and assists because a pitcher's tendency to induce ground balls can have a major impact on those numbers. Even if you're a brilliant fielder, you're not going to look good next to an extreme ground-ball pitcher like Greg Maddux if you're a fly-ball pitcher and they're using traditional fielding stats to evaluate you.
This year, Kenny Rogers was chosen for the second time in three years, and he's a good pick. He handled 62 chances successfully while participating in 5 double plays. He won despite making three errors. In fact, only eight pitchers had more errors than that. But Rogers was quite agile, earning our top rating for range, and didn't allow a single stolen base. (I don't know whether the voters consider holding runners as a factor in their voting, but it certainly adds to his value as a pitcher.)
Other worthy candidates include Steve Sparks, Mike Mussina (last year's winner), Corey Lidle, Mark Buehrle, and Roy Halladay, but I believe Rogers was the right choice.
In the NL, Greg Maddux won his 13th straight, and there's no question that he's a very good fielder. This year, he handled 69 chances successfully, making only one error in the process. Maddux gets a lot of assists because he's an extreme ground ball pitcher, but he's not the best in the league, at least not any more.
Kirk Rueter handled 53 chances without an error and took part in five double plays. He hasn't made an error in three years, he consistently converts more batted balls into outs than does Maddux, and he is almost impossible to run on. But he may not be the best this year, either.
At the top of the list of pitchers who bested Maddux in converting opportunities into outs are Steve Trachsel, Livan Hernandez, Rueter, and Tom Glavine.
Trachsel has consistently looked good in our fielding analysis, but this is the best he's looked. He made two errors and was involved in three double plays. I'd like to see him perform at this high level for another year before I believe he's really as good as the others on this list.
Hernandez is a great athlete who always makes a lot of plays. This wasn't his best year, but he handled 71 chances successfully and led the majors with seven double plays while making three errors.
Glavine handled 71 chances without making an error, turned three double plays, and held runners well. Hernandez and Rueter usually rank higher than Glavine in converting batted balls into outs, and while I'd pick Rueter as the league's best fielder over the past five seasons, I think Glavine was slightly better this year, and he would have received my vote.
Ivan Rodriguez has owned this award for a long time. Even though he threw out only 36% of opposing runners this year, he was still intimidating enough to deter enemy runners from challenging him in the first place, so he was a strong candidate again. But he also made seven errors and missed time due to his knee problems, opening the door for someone else. In a year when many teams split the position among several players and nobody stood out, Bengie Molina emerged as the deserving winner. Molina gunned down 45% of the runners who tried to steal and made only one error on the season.
In the NL, I'd second the selection of Brad Ausmus, too. He threw out only 32% of opposing runners this year, but he has a history of throwing very well and now plays for a manager (Jimy Williams) with a track record of advising pitchers to focus more on the hitter than on the runners. The only serious challenger would be Jason LaRue, who's arm didn't get tested very often and who threw out 45% of the runners who dared. But LaRue allowed 20 passed balls to Ausmus's two, made one more error than Ausmus, and was involved in four fewer double plays. Charles Johnson had a good year throwing but didn't play nearly enough to be a serious candidate.
First basemen
In a down year for AL first basemen, Doug Mientkiewicz should have been a slam dunk winner, and I don't understand the selection of John Olerud. For the second year in a row, Mientkiewicz turned a higher percentage of batted balls into outs than any other AL first sacker, and he matched Olerud in fielding percentage.
Olerud has been a very good fielder in the past, and before he went to the Mets and got noticed, we singled him out as someone who consistently looked very good in our ratings despite getting no credit for his defense. But he's getting up in years and we just don't see any evidence that he's making enough plays at this stage in his career. It's true that the other Seattle infielders made only 47 errors this year, the fifth lowest total in baseball, suggesting that Olerud may have bailed out his mates by scooping throws on more than a few occasions. But that's a very inexact measure. And Minnesota was one of the three teams that was even better on this score, so Mientkiewicz gets the nod here, too.
There was more competition in the NL, but Todd Helton stood out anyway, and I agree with this selection. Helton turned far more batted balls into outs than the other guys at this position, and that in my mind is enough to overcome a quite ordinary record in starting double plays and the seven errors he committed.
Tino Martinez, a contender in the AL a year ago, exhibited very good range and made only five errors, but didn't excel in starting double plays, either. Derrek Lee led the league in starting DPs and got to a lot of balls, too, but tarnished that record by making 12 errors. Travis Lee's defense must have been the main reason he was playing as much as he did, because he didn't have a great year at the plate. His range was good, his fielding percentage above average and his DPs nothing to write home about, but didn't do enough to match the year Helton had.
If Helton had a weakness this year, it might be found in the fact that the Rockies led the majors in errors (75) made by their other infielders, perhaps indicating that Helton wasn't taking care of as many bad throws as his counterparts. On the other hand, the Rockies have the 7th-lowest 2B/3B/SS error total over the five years that Helton has been the regular first baseman, so his track record doesn't indicate a problem in this area.
Unfortunately, we don't have good data on how well first basemen scoop throws. We can count the throwing errors made by other infielders, but the play-by-play files don't tell us how many errors were saved by a good scoop, a great stretch, or a clever sweep tag on a wide throw. And if there are runners on base, we can't tell from the data whether the throw went to first or some other base. Certain first basemen like J.T. Snow make their name on these plays, but it's difficult to measure just how valuable they are in that way.
With Roberto Alomar plying his trade in the other league this year, the battle for the AL Gold Glove was a fair fight for the first time in a long time. Last year, I committed several paragraphs to a detailed evaluation of Alomar's defense in 2001, concluding that his ability to cover ground had diminished with age to a degree that outweighed his excellent fielding percentage.
I believe Adam Kennedy deserved the honor last year, and Kennedy came through with another terrific defensive season in 2002. If it was my call, he'd have a Gold Glove for each hand right now. According to our analysis, Kennedy made 37 more plays than the average 2B this year, and when he got to a ball, he was above average in starting double plays and getting force outs. Other fielders were a little more inclined to settle for the out at first. And there were no weaknesses to offset these pluses; Kennedy was at or a little better than the league in making the pivot and avoiding errors.
Bret Boone was every bit as steady as he's been in the past, and that's likely what convinced the voters to give him his second Gold Glove overall and his first in the AL. Boone led the position in fielding percentage with only seven errors on the season, but he didn't get to nearly as many balls as did Kennedy and he was below average in turning double plays.
In my view, Texas's Mike Young was a slightly better candidate than Boone, nearly matching Boone's fielding percentage while getting to a few more balls and having a better pivot percentage on double plays. For the second year in a row, Jerry Hairston looked quite good in our analysis, and would have been a better selection than either Boone or Young.
But neither player came close to making as many plays as Kennedy. At age 26, he's young enough to get more chances, but there are some terrific young players who are ready to challenge him. I'm thinking of Cleveland's John McDonald and Oakland's Mark Ellis, both of whom looked terrific this year but didn't play enough to challenge Kennedy for the top spot in my mind. Both played shortstop almost exclusively in the minors, and it's not at all uncommon for converted shortstops to become outstanding second basemen very quickly.
Last year, I wrote that if Pokey Reese had played the entire year at second, instead of splitting his time between second and short, he would have gotten my vote. But he didn't, so I opted for Fernando Vina instead. In 2002, Vina repeated as the Gold Glove winner at this position. But Reese did play the entire year at second this time, and he would have been a much better choice, in my opinion.
Vina had a disappointing year at the plate, losing 33 points off his batting average and much of his extra-base power. I'm not saying this because I think hitting stats should be considered when picking Gold Glovers. I mention it because we saw a noticeable decline in his range as well, and sometimes these things are connected. His 13 errors and .981 fielding percentage were merely average, and while he continues to be terrific at turning the double play, he didn't create enough extra outs that way to make up for the many extra balls that Reese gets to.
According to our analysis, Reese made 26 more plays than the average 2B, while Vina was near the average. Reese made only 8 errors and posted a .988 fielding percentage, besting Vina in both categories. And Reese was above average in making the pivot, too. Not quite at Vina's level but close enough to make it clear that Reese was the better overall player this year.
Mark Grudzielanek, like Reese a converted shortstop, was another player with a strong all-around season, getting to plenty of balls (even allowing for the help given him by Dodger Stadium), notching a very impressive .989 fielding percentage, and turning double plays at an above-average rate.
At third base, the voters selected Eric Chavez and Scott Rolen. Both are repeat winners, with Rolen riding a three-year streak and picking up his fourth overall.
Rolen is a perennial standout who has made far more plays relative to the norm for his position than any other NL fielder over the past four years. For the second year in a row, Rolen is my choice for NL Defensive Player of the Year. You might argue that someone at a more demanding position, a shortstop or center fielder, should be given preference over the top third baseman. But Rolen has dominated his position like nobody else. And it's not as if third base is an easy position to play; it requires great reflexes, a strong arm, and the versatility to handle a wide variety of plays.
But he wasn't the only NL third baseman to have a very good year in the field. In the wake of Robin Ventura's move to the other league, Rolen's main rivals were David Bell, Placido Polanco (the man Philly received in the Rolen trade), Craig Counsell, and Aaron Boone.
Bell was on my short list of candidates for the AL Gold Glove at this position in 2001, but I gave the nod to Chavez partly because Bell didn't play as much (26 fewer starts). Bell played more often this year, but some of that time was spent at other infield positions, so his time at third was about the same. And he was better this year in both range and sure-handedness.
Polanco has played second, third and short for Tony LaRussa's Cardinals since his debut in 1998, but third base appears to be his best position. He covered a lot of ground, posted a fielding percentage that was 24 points better than the average, and was fifth in the majors in double plays despite playing at least 200 fewer innings at third than the four guys ahead of him. With the recent free agent signing of David Bell, the Phillies now have two of the league's best defensive 3Bs on the same roster. One, most likely Polanco, is expected to play second next year.
Counsell had a terrific defensive season at second base in 2001, and when he was moved to third to fill in for Matt Williams in 2002, Counsell excelled there, too. He might have given Rolen a run for his money had he been able to stay healthy all year. With Williams having vetoed his proposed trade to Colorado, we may not get a chance to find out what Counsell can do at third over a full season. Like Polanco, Counsell is a three-position player who may be best suited for third base defensively while hitting more like a typical second baseman.
According to our analysis, Boone has been less consistent than the other players just mentioned. He was terrific in 1999 and very good again this year, but didn't make as many plays in the two intervening seasons. He tied Rolen for the major-league lead in DPs this year with 42, but he made 20 errors and didn't cover quite as much ground as the other guys on this list. All in all, he's not really a serious challenger for the Gold Glove, but he is one of the league's better defensive 3Bs.
The AL produced three strong candidates, Robin Ventura, Corey Koskie, and Eric Chavez (the winner). According to our analysis, Koskie outplayed Chavez by a small margin this year, making a few more plays, posting a higher fielding percentage, but trailing in double plays. Ventura's range was quite a bit better than either of the other two, but his 23 errors were quite a bit worse. Overall, even with the errors, Ventura made a few more plays. Chavez led the other two in assists by a big margin, but that's largely a function of playing behind a ground ball staff with one of baseball's highest percentages of innings thrown by left-handed pitchers.
So the choice comes down to how much weight you put on range versus fielding percentage. If you don't care too much about the errors as long as a guy is making loads of other plays, Ventura's your guy. If you put a premium on fielding percentage, then Koskie's the pick. If you're looking for a blend of the two, it could go either way. My vote would go to Koskie, but only by the slimmest of margins.
When the Gold Gloves were announced, Omar Vizquel expressed surprise that (a) he didn't win it again and (b) it wasn't Mike Bordick who got it. Here's what he said, as reported by ESPN.com: "I didn't think I was going to lose the Gold Glove this year. I don't think I gave it up. I know I had the numbers to compete."
Vizquel's comments fit neatly with two longstanding patterns in the Gold Glove voting. First, when the voters settle on a player, he tends to get the award year after year as long as he doesn't give them a reason to change their minds, even if he's not the most deserving candidate that year. Unlike batting and ERA titles, you don't see two or three great players duking it out for the top spot year after year, with the lead changing hands based on which of them had the better year. The process of measuring fielding performances is murky enough that the voters often can't figure out which of the best players actually had the better year, so the incumbent has a big advantage.
Second, a lot of weight is placed on errors. When Vizquel was talking about having the numbers to compete, he was talking about errors. When he mentioned Bordick as a viable candidate, he was referring to the fact that Bordick ended the season with only one error in 569 chances, for a remarkable .998 fielding percentage, and a record-setting streak of 110 consecutive errorless games.
Because I have long attributed these beliefs to the voters, I was surprised to see these comments come from a player, especially a player who in his prime had as much or more range at shortstop as anyone in the league. I would have expected someone like Vizquel to look at the "numbers" more broadly than he apparently did. And I'm a little surprised that he talked as if the award was his to lose. Everything else in baseball starts over at zero on Opening Day. Nobody is supposed to have a head start on anything; you're supposed to earn it all over again.
So I was pleasantly surprised when the voters chose Alex Rodriguez despite all of this. Even though Vizquel did have a good year in the field, making only seven errors, his range was only slightly above average. It's impressive that a 35-year-old like Vizquel can still cover as much ground as his younger counterparts, but that's not the same as saying that Vizquel is still as good as he was ten years ago or that he's a Gold Glover at this stage of his career.
Like Vizquel, Bordick is getting up in years (he turned 37 in July), his range was only a little above average, and his main asset was reliability. That his reliability was of historic proportions makes him a Gold Glove candidate even though injuries limited him to 117 games.
But Rodriguez also had a very good year in the field, and I think he deserved the award. Awards should go to the player who accomplished the most that season, so it matters that A-Rod was able to play in every game while Bordick's season was truncated. Rodriguez was very steady, compiling an impressive .987 fielding percentage at a position where the norm is .973. And Rodriguez got to a higher percentage of balls than either Vizquel or Bordick.
In recent years, there hasn't been a huge gap between the best and worst fielders at this position. Teams have always been unwilling to trade off too much defense for offense at short, so you don't see awful fielders with big bats like you sometimes do at less challenging positions. With the emergence of a young crop of great-hitting shortstops, it seems as if fewer teams are willing to go with great-glove no-hit Mark Belanger types. Some of the more defense-oriented shortstops (like Bordick and Vizquel) are past their primes, while others (like Rey Sanchez and Pokey Reese) have moved to second base.
So it wouldn't be accurate to say that Rodriguez was far and away the best at his position this year. David Eckstein, Nomar Garciaparra, Royce Clayton, Chris Woodward, Carlos Guillen, and Miguel Tejada also played quite well. Nomar and Guillen covered a lot of ground but made too many errors. The others were steadier, but nobody rose to A-Rod's level this year.
It was much harder to pick out the strongest NL candidates. Juan Uribe and Jack Wilson were at or near the top in the range factor rankings, but their putout and assist totals were inflated because both played behind ground ball staffs. Last year's winner, Orlando Cabrera, led the majors with 29 errors, so he took himself out of the running in a hurry. Uribe tied with Rafael Furcal for second with 27 errors each. Cesar Izturis made a lot of plays, and could be the best fielder at this position, but he started only 109 games at short. Rey Ordonez and Jose Hernandez showed very good range but hurt their cases with 19 errors each. The voters' choice, Edgar Renteria, also made 19 errors.
I think I would have picked Ordonez, but because nobody really separated themselves from the pack, I can't really argue with the selection of Renteria.
In the third base comments above, I named Scott Rolen as my choice for the fictional NL Defensive Player of the Year award. Darin Erstad is my nominee in the AL. He led all major league outfielders with 452 putouts despite starting only 142 games. Mike Cameron was a distant second with 415 even though he played 90 more innings than Erstad. Andruw Jones was third with 404 in 129 more innings than Erstad. If all three had played 1357 innings, as Jones did, the numbers would have been 499 for Erstad, 427 for Cameron, and 404 for Jones.
Erstad and Cameron got a boost from playing behind fly ball staffs, but Erstad was the top outfielder even after you take this and all other factors into account. This shouldn't come as a surprise. Erstad won a Gold Glove in 2000 and, in my opinion, should have received one last year, too.
The Seattle outfield turned a higher percentage of fly balls and line drives into outs than any other team this year, with Anaheim and San Francisco tied for second and Minnesota fourth. The difference among these four teams was quite small, only about 7 batting average points, so the order could change if we took park effects into account. What is clear, however, is that these four outfields were the clear leaders in this category.
For Seattle, Mike Cameron and Ichiro Suzuki were responsible for their place in this elite group. Eight players shared left field, with Mark McLemore and Ruben Sierra getting about 70% of the playing time between them. Ichiro was selected as a Gold Glover for the second year in a row. We thought highly enough of Ichiro's defense to assign him our top rating for both range and throwing this year, but if you can only justify picking one player from the Seattle outfield, Cameron's my choice. Cameron made 44 more plays than the average center fielder given the array of chances presented to him, the second-highest figure in baseball this year behind Erstad, and well ahead of Ichiro's mark in right field.
The debate about the relative value of a center fielder and corner outfielder also applies to Minnesota's Torii Hunter (CF) and Jacque Jones (LF). According to our analysis, Jones was the top left fielder in baseball this year. (Rondell White was second.) Jones is a legitimate center fielder, too; in 147 games at that position from 1999 to 2001, he was among our top-rated players at that position.
Casual fans may remember Torii Hunter's 2002 season based on two plays, his spectacular homerun-saving catch in the All Star game and the ball he misplayed into an inside-the-park homerun in game three of the AL division series. But we can't define a player's entire season based on two plays. Overall, our analysis indicates that Hunter was one of the better center fielders in the league but trailed Erstad and Cameron.
In my view, the three AL Gold Gloves must come from the group that includes Erstad, Cameron, Jones, Hunter, Ichiro, and Johnny Damon of the Red Sox. With three teams dominating the league in outfield defense, it makes sense to pick the best outfielder from each team. Erstad's the easy pick for Anaheim. Cameron gets the nod for Seattle. The Minnesota pick is a tossup, but I think Jones had a slightly better year, so I'll go with him over Hunter.
As I mentioned a few paragraphs back, San Francisco's outfield turned more fly balls and line drives into outs than any other NL team. The next three teams in this category were St. Louis, Arizona, and Cincinnati.
The spacious dimensions of Pacific Bell Park boosted the San Francisco percentage a little, but the players deserve most of the credit. Specifically, center fielder Tsuyoshi Shinjo and right fielder Reggie Sanders were among the top-rated fielders at their positions this year, and both are Gold Glove candidates in my mind.
The St. Louis outfield was led by Jim Edmonds in center and JD Drew in right. Over the years, Edmonds has shown above-average range in the years when he's been healthy and below-average range when he's been playing with one of his many ailments. One constant is an athletic ability that often allows him to make memorable plays on the balls he does get to. In the past, Drew has shown terrific range in right field and below-average range in center. He was still above average in right this year even though he was playing with a bad knee, but didn't turn in the kind of performance to justify a Gold Glove.
Arizona's top outfielders were Steve Finley and Luis Gonzalez. Finley is quite similar to Edmonds in that his ability to get to balls is exceeded by his ability to make great catches when he gets there. That may sound like a back-handed compliment, but it's not meant that way, and Finley's range was above average this year. Gonzalez has always shown very-good-to-great range in left and still looks good even though he turned 35 in September.
Cincinnati's outfield defense was led by Austin Kearns, who topped our net plays rankings in right field and also played a little left and center. Kearns was fifth in the majors in putouts per nine defensive innings; all four of the guys ahead of him played behind staffs that generated a higher rate of fly balls, including three who played behind two of the more extreme fly ball staffs in the game.
We didn't find any NL left fielders who stood out this year. Geoff Jenkins has been very good for several years and was on pace to be the league's top LF again before he destroyed his ankle and missed 90 games. Luis Gonzalez played well, but left field is the easiest of the three positions, and he didn't do enough to be compared with top players at the other two spots.
The top center fielders were Shinjo, Jay Payton, Finley, Andruw Jones, and a bunch of part-timers who didn't play enough to be seriously considered for a Gold Glove. Shinjo lost his job because he didn't hit, but his defense was never a problem, and you could make a very good case that he was the league's top defensive CF in 2002.
Before taking ballparks into consideration, the top right fielders were Kearns and Sanders, with Drew a distant third. Colorado's Larry Walker is a difficult player to judge because he often plays hurt and his park makes outfielders look bad.
One way to judge the impact of a park on outfielders is to compare the percentage of batted balls that become hits in that team's home and road games, excluding homeruns. Coors Field yielded 897 more hits from 1999 to 2002, with 321 of them on ground balls and the remaining 576 on fly balls and line drives. That's 144 extra fly-ball and line-drive hits per year for both teams, or 72 per year for the Rockies alone, or 24 per year per position. In other words, in any fielding analysis that measures the percentage of batted balls turned into outs, Colorado's outfielders begin the season with a deficit of 24 plays compared to players in normal parks.
Without adjusting for his home park, Walker ranked in the bottom third in our net plays analysis. Take the park into account and Walker ranks in the top third of the game's right fielders, and we rated him accordingly.
The voters awarded the three outfield Gold Gloves to Andruw Jones, Larry Walker, and Jim Edmonds, and all three were worthy of consideration. The pool of candidates is limited by the fact that some of the top outfielders didn't play enough. Neither Shinjo nor Kearns started 100 games in the outfield this year. Jay Payton had a very good year but started only 83 games in center and 109 overall. It would be a reach to pick any of them despite their fine defensive play.
All things considered, including playing time, my choices are Andruw Jones, Reggie Sanders, and Steve Finley. I believe Finley was a little better than Edmonds in center field, while Sanders showed just enough extra range to make up for Walker's superior throwing arm.
Here's how my selections compare with those of the voters:
------- American ------- ------- National ------- Pos Voters Diamond Mind Voters Diamond Mind P Rogers same Maddux Glavine C Molina same Ausmus same 1B Olerud Mientkiewicz Helton same 2B Boone Kennedy Vina Reese 3B Chavez Koskie Rolen same SS Rodriguez same Renteria Ordonez OF Erstad same AJones same OF Ichiro Cameron Walker Sanders OF Hunter JJones Edmonds Finley
We agree on eight of the eighteen selections. Last year we agreed on twelve, and at the time, I wrote that it was the highest number of matches I could remember. So I'm not surprised to see that we differed on a few more choices this year.
Here are a few other players whose defensive performances seem worthy of mention:
Eric Chavez, 3B -- In 2001, Chavez won his first Gold Glove, largely (I would guess) because he led the league in fielding percentage while making an above-average number of plays. Prior to that season, our analysis indicated that his range was slightly below average, and we had given him mostly Average and Fair ratings. He got to a lot more balls in 2001, however, and he was right at the boundary between our Excellent and Very Good ratings. It was a tough call, but we decided to take a chance and give him an Excellent rating even though his history didn't really support it. It now appears that a Very Good rating would have been a better choice, as his range reverted to the league average in 2002.
Tony Clark, 1B -- Clark has generally received our Very Good rating but dropped to Fr in 2001 because back problems limited his mobility. We predicted that he'd bounce back to the Very Good level if he was healthy, and he did just that. Of course, he didn't hit a lick, so his playing time was severely reduced despite his skills in the field.
Jermaine Dye, RF -- Dye has been one of our top-rated right fielders for years but struggled to come back from a severely broken leg. He missed the first few weeks of the season and after his return admitted that it was affecting his play in the outfield. His bat came around in the second half, suggesting that he may be on his way to better things for 2003, but his overall defensive numbers in 2002 were low enough to earn a Poor rating.
Brian Giles, LF -- Pittsburgh's outfield was by far the worst in the majors at converting fly balls into outs, so it's no surprise that Giles, the only Pirates outfielder to start more than 77 games, has to shoulder a major part of the blame. As a result, his range rating dropped to Poor.
Ken Griffey, CF -- For the second year in a row, Griffey tried to play through some leg injuries. He didn't play much, and when he did, he wasn't anywhere near his usual self. So he gets a Fair rating again. I'm hoping we get to see him back at 100% in 2003, and look forward to seeing how well he performs if he's healthy.
Derek Jeter, SS -- Once again, Jeter was at or near the bottom in just about every measure of range that we use. As was the case with Scott Brosius from 1998 to 2000, his raw numbers were hurt by playing next to a third baseman (Robin Ventura this time) who cuts off a lot of balls that might be playable by the shortstop. Without taking Ventura's impact into account, it would be tempting to rate Jeter's range as Poor. But he's better than the raw numbers indicate. Not enough to earn an Average rating, though, and we have again assigned him a Fair range rating and a better-than-average error rating.
Raul Mondesi, RF -- Once had a very good reputation for defense, mostly based on his great arm. In terms of range, our analysis shows that he's been slightly above average throughout his career. In 2001, it was reported that Mondesi came to camp carrying some extra weight, and his defensive numbers took a big dive. Coincidence? Maybe, but we felt a Fair rating was an accurate reflection of his 2001 performance. We thought he might rebound in 2002, but he continued his slide instead. As a result, we dropped him to a Poor rating.
Manny Ramirez, LF -- Ramirez has been an adequate corner outfielder in the past, but you wouldn't know it from his performance in 2002. Chronic hamstring problems have made him very cautious in the field and on the bases, and his Poor rating in left field reflects that. If Ramirez can find a way to overcome his hamstring problems and get back to playing at full speed, his rating might improve. But he's such a great hitter that he and the team may not feel it's not worth taking the chance to find out lest he pull another hammy and take his bat out of the lineup for a few weeks.
Rey Sanchez, 2B -- I fully expected Sanchez to emerge as a Gold Glove candidate this year. He's been one of our top-rated shortstops for several years. Most shortstops shine when they make the move to second, and Sanchez had the edge of having played second quite a bit in the past, so the transition should have been an easy one. For a couple of months, he did look like a Gold Glover, but then he pulled a hamstring and missed several weeks. The highlight film plays weren't nearly as abundant after that, and his overall numbers were quite ordinary in the end. As a result, he earned our Average rating for range.
]]>This month, I'm delighted to share this space with Tom Ruane, who recently joined the Diamond Mind team as a researcher, programmer and writer. Tom came to my attention earlier this year when he posted a large number of extremely well-researched and well-written items to the mailing list for the Society for American Baseball Research (SABR). He's one of the best baseball analysts I've come across in a long, long time, and I hope you enjoy reading his material as much as I do.
We're going to cover four mostly unrelated topics in this article. First, Tom will explore how much you can read into teams that get off to a quick start in April. Second, he'll delve into the question of babying the arms of young pitchers. And, finally, I'll present some grass/turf fielding statistics and talk about illusions that can be created by range factors.
Near the beginning of the 1998 baseball season, Don Zminda of Stats Inc. wrote a column for America On-Line. It dealt with the importance of the first few games of the season and started with a table correlating a team's mark in its first 10 games with its eventual record. For example, of the 218 teams to start the season 4-6 since 1951, only 38.1% of them managed to avoid a losing record, while 63.3% of the 226 teams starting out 6-4 finished up at .500 or better. He continued:
"Interesting stuff, but John Dewan wanted Don to go out an even more of a limb. He asked to see the same chart based on a club's first two or three games. This one simply knocked me over:
Teams W-L Record in First 2 or 3 Games 1951-97 Cumul Finish Ended Season # of ============== ============ Start Tms Record Pct .500+ Pct 0- 2 284 21279-23213 .478 119 41.9% 1- 1 494 38916-39137 .499 251 50.8% 2- 0 288 23648-21493 .524 189 65.6% 0- 3 146 10936-11925 .478 56 38.4% 1- 2 383 29470-30800 .489 182 47.5% 2- 1 388 30937-30317 .505 213 54.9% 3- 0 149 12500-10801 .536 108 72.5%Would you have guessed that there'd be such a big difference between starting out 0-2 versus going 1-1 or 2-0? But there it is, and it seems to be anything but a fluke. At every number of games we looked at from two to 10, there's the same straight-line progression."
Which led Don to conclude that "early-season games ARE a lot more important than you might think. That old baseball adage - 'A win in April is worth two in September' - is not so crazy."
I thought it was very interesting, but decided to see if the phenomenon was unique to games at the start of the season. So here's what I did: rather than concentrate on the first 3 games of the season, I looked at ALL 3-game stretches during the season. If a win in April is really worth 2 at other times, you'd expect the spread of winning percentages to be more pronounced in games 1-3 than, for example, in games 135-7. But it isn't.
Teams W-L Record in Games 135-137 1951-96 Cumul Finish Ended Season # of ============== ============ Record Tms Record Pct .500+ Pct 0- 3 143 10556-12246 .463 48 33.6% 1- 2 344 26666-28265 .485 145 42.2% 2- 1 354 28918-27679 .511 214 60.5% 3- 0 143 12450-10400 .545 114 79.7%
NOTE: I've removed ties from the equation. I guess I should call them decisions not games, but you get the idea.
The median percentages of these 4 groups of records, starting with the games 1-3 and going through to games 160-162, is the following:
Record Win Pct .500+ Pct 0- 3 .469 35.8% 1- 2 .490 46.8% 2- 1 .511 58.7% 3- 0 .530 70.0%
So what their study shows is not that early games are more significant than later ones, but that a lot more bad teams go 0-3, at ANY time during the season, than do good teams. And so on.
By the way, the worst single game to lose if you want to have a winning season? Game 81. Only 43.4% of the teams losing their 81st decision since 1951 were able to avoid a losing season. The best game to lose? It's a tie: all 5 teams that dropped their 163rd, 164th or 165th decision of the season had great records. I wonder why.
With Kerry Wood in the headlines recently, there has been a lot of discussion in the press about how best to protect his (and other young pitcher's) arm. In May, Rob Neyer wrote a "Stats Class" column on ESPN.com about the price pitchers ultimately pay when they rack up a lot of innings at a tender age. He looked at the 19 pitchers who have thrown 200 innings in a season since 1969 and compared their performance in seasons 1-3 with their performance in seasons 4-6. Here's what he found:
IP K/9 ERA Years 1-3 11375 5.97 3.31 Years 4-6 8565 6.24 3.43 Pct. +/- -24.7 +4.5 -3.6
He noted the large dropoff in innings pitches, gave a few examples (including Mark Fidrych) and concluded with:
"You've heard a lot about Kerry Wood and Nolan Ryan. . . . But there is one important difference between the two pitchers. Nolan Ryan didn't total 200-plus innings in a major-league season until he was 25 years old. No one ever talks about this, but it goes a long way toward explaining why Ryan was still throwing 90-plus fastballs two decades later."
Well, that got me interested in what a similar chart would look like for pitchers over the same period (1969-1991) who waited until they were 25 to pitch 200 or more innings. (I picked 1991 as the cutoff because it gave me six years of data to examine for today's pitchers.) This list includes 54 pitchers and looks like this:
IP K/9 ERA Years 1-3 31240 5.32 3.77 Years 4-6 22448 5.44 3.81 Pct. +/- -28.1 +2.3 -1.1
So the innings pitched totals for the pitchers who saved themselves until their mid-twenties fell off even more than those who were rushed into action. And given that their performance over the second period was almost identical to the first, I'm assuming that their drop in innings pitched was primarily due to arm problems. Of course, it is possible that their teams expected them to get better and dumped them when they failed to improve. You could argue that we're talking about two different classes of pitchers here and you'd be right; pitchers with the ability to break into a starting rotation in their early twenties are as a group a lot more talented than those who come around four or five years later. Still, the differences in their ability should not have affected their susceptibility to arm woes.
By the way, I looked at a sampling of these pitchers, and with some exceptions (like Doyle Alexander), they did not exceed 200 innings pitched in the minors either.
Of course, I'm not recommending that it's okay for Kerry Wood to start throwing 150 pitches a game, or that it is somehow beneficial for a pitcher to top the 200 inning mark early. I guess my point is two-fold:
1) Innings pitched might not be the best measurement here. How many pitches he throws, especially when his arm is tired, is probably a better indicator than yearly innings pitched. Livian Hernandez pitched 96 innings in 1997 (not counting the post-season) and was probably overworked.
2) Pitchers' arms are fragile throughout their twenties. My guess is that the charts above wouldn't have changed that much (except for the number of pitchers involved) if I had picked 23, 27 or 29 as the target age. Pitching strategies are still evolving as we learn more and more about how to protect players from arm problems. It wouldn't surprise me if in a decade or so, most teams have six-man rotations and starting pitchers average from 150-175 innings a year. Of course then you'll have to listen to me complain about how they don't make pitchers like they use to. (Why in my day pitchers were men--tough guys who thought nothing of pitching seven, maybe eight innings on occasion-- especially if they had their good stuff, and the weather wasn't too hot or muggy.)
I'm sure by now you've seen your share of situational breakdowns in batting and pitching stats in various magazines, books and web sites. But I'll be you haven't seen too many fielding splits.
I spend a lot of time studying fielding, probably because I'm a little guy who grew up as a good-field, light-hitting shortstop. I have no illusions about being good enough to play professional ball -- I'm definitely not -- but if there was a Designated Fielder position in baseball, I'd be first in line. It wouldn't matter to me if I never came to the plate so long as I could play shortstop every day.
One of the things I've long been curious about is the effect of grass and turf fields on fielding statistics. Do infielders get to more grounders when the infield grass has a chance to slow them down? How much does playing on turf cut down on error rates? Is there any impact on the stats for outfielders?
To find out, I compiled fielding totals by position on a grass/turf basis for the period from 1980-1997. The data originated from Retrosheet (www.retrosheet.org), Project Scoresheet (now defunct), The Baseball Workshop (now part of Total Sports), and Total Sports. Here are the totals:
Fielding Totals, Grass/Turf, 1980-97
Pos S Innings PO A E DP PB FPct PO/9 A/9 DP/9 PB/9
---- - ------- ------ ------ ---- ----- ---- ----- ----- ----- ----- -----
p t 244805 15818 33919 2365 2246 .955 .582 1.247 .083
p g 419065 26917 58977 3940 4325 .956 .578 1.267 .093
c t 244805 164211 13642 2198 1682 2158 .988 6.037 .502 .062 .079
c g 419065 279432 24399 3585 3206 3796 .988 6.001 .524 .069 .082
1b t 244805 247210 19629 1994 21838 .993 9.088 .722 .803
1b g 419065 418629 33284 3681 39900 .992 8.991 .715 .857
2b t 244805 58706 83712 2503 17445 .983 2.158 3.078 .641
2b g 419065 101227 141417 4939 31429 .980 2.174 3.037 .675
3b t 244805 20266 55217 3859 4592 .951 .745 2.030 .169
3b g 419065 35137 92969 6994 8468 .948 .755 1.997 .182
ss t 244805 44333 83451 4000 16365 .970 1.630 3.068 .602
ss g 419065 76695 143548 7658 29614 .966 1.647 3.083 .636
lf t 244805 55022 1879 1206 300 .979 2.023 .069 .011
lf g 419065 97607 3215 2311 526 .978 2.096 .069 .011
cf t 244805 72902 1611 972 386 .987 2.680 .059 .014
cf g 419065 125194 2507 2000 666 .985 2.689 .054 .014
rf t 244805 55953 2228 1182 445 .980 2.057 .082 .016
rf g 419065 96373 3632 2330 769 .977 2.070 .078 .017
As you might expect, fielding percentages among infielders are a little higher on turf than on grass.
The rates of putouts and assists per nine defensive innings are almost identical, which probably says that (a) balls that are slowed down a little by grass are about equally offset by the truer hops you get on turf, (b) because there are exactly 3 outs per inning on both grass and turf, it would be hard for these rates to deviate by much, and (c) one can safely ignore the effects of grass and turf when evaluating players based on range factors or similar stats.
The DP rate is noticeably higher on grass for all infield positions. It's been said that players run faster on turf (based on noticeable increases in stolen base percentages on turf), so this difference is probably due to the turf runner getting to second a little more quickly and the batter getting to first a little more quickly.
Passed ball rates are about 4% higher on grass. My guess is that this is just random and doesn't mean anything.
For years, I've been writing about the danger of reading too much into range factors. This subject came to my attention again this summer when I received my copies of two terrific books from STATS, Inc.: the All-Time Major League Handbook and the All-Time Baseball Sourcebook. Both volumes are packed with valuable information, including some that you cannot find anywhere else. They're very expensive, but I recommend both.
The All-Time Major League Handbook includes season-by-season fielding statistics for every player, and for each position they played. I'm not aware of any other book with complete fielding information. An early edition of Total Baseball had some fielding data for players with relatively long careers, but this section was dropped in later editions. The MacMillan Baseball Encyclopedia has some fielding data for some players, but it's not broken out by position. In the electronic world, the Fan Park Electronic Baseball Encyclopedia has good fielding data.
The All-Time Major League Handbook includes each player's range factor at each position along with the league average range factors for that position. This is very important, because it gives you a baseline against which to evaluate the player. I wish all sources included league averages to help us put player numbers in context.
Unfortunately, the range factor statistic has some serious weaknesses, and if you're going to use these numbers to draw conclusions about fielding prowess, you need to keep these weaknesses in mind. STATS computes range factors using this simple formula:
Range Factor = (Putouts + Assists) / Games
Here are some of the reasons why you need to be careful about ranking players based on range factors:
Missing positions. STATS doesn't compile range factors for pitchers, catchers and first basemen, so if you're looking for a way to evaluate fielding at these positions, you're out of luck.
Playing time. The first problem with range factors is that not all games are created equal. Defensive specialists who often enter games in the late innings or are frequently lifted for pinch hitters are still charged for a full game played despite having many fewer opportunities to make plays than the starters. As a result, some of the best fielders in the game have their range factors artificially depressed.
For modern seasons, we use defensive innings to get a more precise measure of playing time. Defensive innings are just like pitcher innings. If you are in the field when an out is recorded, you are credited with one-third of a defensive inning. Defensive specialists and other part-time players are more fairly represented this way. But the STATS All-Time Handbook doesn't use this method because they didn't start compiling defensive innings until the late 1980s.
Grouping outfield positions. For most of baseball history, the official fielding records lumped all three outfield positions together. The STATS book is no different. Almost all of the CFs in the book have range factors higher than the league average. And most of the LFs and RFs are below the league average. But that's largely because (a) the STATS data groups all OF positions together and (b) more balls are hit to CF than the other two positions. So, if you're looking at a player with a range factor of 2.20 in a season where the average outfield range factor was 2.00, and you don't know which OF position he played, you don't really know whether he was better than average or not.
Strikeouts vs balls in play. If someone plays behind pitchers who strike out a lot of batters, fewer balls are put in play. In 1996, for example, the Indians fielders saw 4516 balls put in play (excluding homeruns), while the Yankees defense saw only 4348, in large part because the New York pitchers struck out 106 more batters. And the Athletics defense was presented with 4720 batted balls, almost 400 more than the Yankees. These extra chances can inflate range factors very quickly.
Ground ball percentages. In 1996, the Twins infielders had a crack at only 1746 ground balls, while the Indians saw 2148 grounders. On the flip side, the Twins outfielders got to chase 1956 fly balls, more than any other defense. Range factors made the Twins outfielders look like Gold Glovers, and their infielders look weak, but it had much more to do with their pitchers than their defense.
Left/right splits. Some teams have more left-handed pitchers than others, which usually means they face more right-handed batters than others. And right-handed batters are about twice as likely to hit a ball to 3B than to 1B. In 1996, left-handed batters (including switch-hitters batting left) accounted for 43.5% of the atbats, yet the Indians saw 50.1% lefties. That's a difference of 371 batters.
Statistical quirks. Not all putouts and assists are created equal. A second baseman, for example, gets a putout each time he makes the tag on a steal play or takes a throw from a shortstop on a force play. Neither play has anything to do with his range, and both can be artificially boosted by playing with a great throwing catcher or a superior defensive shortstop, especially when playing behind a pitching staff that puts a lot of runners on base.
Degree of difficulty. Some fielders might have benefited from an unusually high number of routine plays, such as lazy popups and soft line drives, while others were cowering under a barrage of screaming line drives. It stands to reason that you'll see a few more tough plays when you're playing behind an awful pitching staff than when Maddux, Smoltz, and Glavine are on the mound.
An example -- Ryne Sandberg versus Frank White. For all these reasons, it's very hard to look at range factors and determine how much is due to the player's ability versus external forces. One interesting example is Ryne Sandberg's 1983 season, when he led the majors with 571 assists, a fielding percentage of .986, and a range factor of 5.74. The average range factor for 2Bs was 4.53, meaning that Sandberg had about 27% more chances per game than the average 2B. From these numbers, you would be forced to conclude that Ryno had the best range of any second baseman that season. But there's a little more to the story.
Sandberg's Cubs had a predominantly right-handed pitching staff that year. Consequently, when Ryno was in the field, 45% of the batters who put the ball in play were lefties, compared to a league average of 40%. That translates into 225 more lefties than normal, and since lefties are much more likely to hit the ball to 2B than righties, he got quite a few more chances to make plays.
The Cubs staff was third last in the NL in strikeouts. As a result, Cubs pitchers put 4663 balls in play, second most in the league, and 75 more than the league average. Sandberg got his share of those extra chances.
Sandberg picked up a bunch of assists and putouts on double plays, and he led the majors in DPs by second basemen with 126. But the Cubs staff had the highest on-base percentage in the league, meaning that he had more than his share of double play opportunities.
Finally, and most importantly, the Cubs pitching staff led the majors in ground ball percentage, and that translated into more than 300 extra ground balls over the course of the season.
When you remove the effects of facing 225 more lefties, seeing 75 more balls put in play, picking up some extra PO and A on double plays, and seeing 300 more ground balls, Sandberg's numbers are not all that different from the league average. Most of his impressive range factor derived from the large number of opportunities presented to him.
Please understand that I'm not knocking Sandberg. I'm just using his 1983 season to point out that an outstanding range factor doesn't necessarily indicate outstanding range. It can also mean "decent range and good hands and an unusually large number of balls hit his way."
The season before, in 1982, Frank White had a range factor of 5.21, which was 0.51 above the league average. He was helped a little by a Royals staff that put 64 more balls in play than the average team. On the other hand, that staff included more than the normal number of lefties and flyball pitchers, and White saw 317 fewer lefty batters and 175 fewer ground balls than is normal for a 2B who played the same amount. So, even though Sandberg's 1983 range factor was much higher than White's in 1982, I would argue that White had a much better defensive season, because he produced a lot of outs despite many fewer chances to make plays.
Note: Total Baseball publishes a number called Fielding Runs that rates players based on putouts, assists, DPs and errors. Like the simple range factor, Fielding Runs doesn't adjust for left/right splits, ground-ball percentages and some of the other factors I've listed. According to Total Baseball, White cost his team 15 runs defensively that year. I couldn't disagree more. When you take into account the characteristics of his pitching staff, I figure he was the best 2B in the league. He won the Gold Glove that year, and I believe the voters got it right.
Adjusted Range Factors. To help us come up with accurate range ratings for our past season disks, I developed a new type of range factor that adjusts for these external forces. It measures playing time by counting balls put in play while each fielder was at his position. It counts only those PO and A in which a fielder's range was really being tested (such as turning a grounder into an out, or catching a line drive or fly ball), while ignoring plays that don't measure range (taking a throw on a steal play, catching a popup). It tracks balls put in play by left- and right-handed batters separately. It produces values for every defensive position and treats the three outfield positions separately. And it adjusts for the fly-ball/ground-ball ratio of the pitching staff. The result is a measure of range that eliminates most of the biases of the simpler range factors published by others.
But I'd be remiss if I left you with the impression that I believe these adjusted range factors are the ultimate in fielding statistics. They rest on the assumption that one can make a much better estimate of the number of opportunities to make plays by taking these external forces into account. At the end of the day, it's still an estimate. And, regrettably, it's not yet possible to compute adjusted range factors for much of this century because we don't have enough play-by-play data for older seasons.
Consequently, when I'm rating fielders for Diamond Mind Baseball season disks, I use one of three methods. For modern seasons in which we have access to detailed play-by-play data that includes the location of every batted ball, I use a technique that evaluates the number and difficulty of the chances each fielder was presented with. For slightly older seasons (1980s), where we have play-by-play data without hit location data, I use our adjusted range factors. And for seasons without any play-by-play data, I look at the traditional measures of assists and putouts per game and try to make mental adjustments for playing time, strikeout rates and other factors.
So I'm happy to see that STATS is publishing the fielding statistics and range factors in their All-Time Major League Handbook, partly because I like to see more attention given to fielding, and partly because it gives me more material to work with when rating players for older seasons. But, please, do yourself a favor. Before you take their range factors as a pure measure of defensive range, stop to think about some of the things that might be distorting the numbers.
Copyright © 1998. Diamond Mind, Inc. All rights reserved.
]]>As you know, offense goes sky high in Coors Field. We can see that in the park factors and the home/road splits for individual players. Here are some hitting stats for the NL as a whole (including Coors), Coors only, the NL without Coors, the Coors numbers prorated to 700 plate appearances, and park-adjusted norms for players who play half their games at Coors.
NL w/o Per Half Total Coors Coors 700PA Coors G 1007 72 935 162 162 AB 69049 5194 63855 638 638 H 18184 1637 16547 201 183 2B 3367 305 3062 37 34 3B 418 59 359 7 5 HR 1917 241 1676 30 23 W 6668 509 6159 62 62 K 13309 880 12429 108 116 R 9329 975 8354 120 102 AVG .263 .315 .259 .315 .287 SPC .408 .536 .397 .536 .464 Runs/Tm/Gm 4.63 6.77 4.47 5.62
What can we learn from this? Quite a bit, actually: