Archive for July, 2010

Was There a Canseco Effect?

In today’s Slate, Ray Fishman says yes. I disagree. What follows is repeat of a post that I wrote two years ago on the study that Fishman believes supports the effect. (Thanks to Alex for the pointer.)

— — —

Was Jose Canseco the Johnny Appleseed of Steroids?

Last week, I became aware of a study by economists Eric Gould and Todd Kaplan that evaluates at the impact of Jose Canseco on his teammates. They examine the belief that Canseco distributed his knowledge about steroids throughout baseball by introducing many of his teammates to performance-enhancing drugs. If this was the case, the authors hypothesize that he ought to have left a trail of improved performance among teammates in his wake.

The authors look at the careers of Canseco’s teammates to investigate this claim. Their method is to examine players to see how well they perform as a Canseco teammate and afterwards, relative to the years preceding involvement with Canseco. The idea is somewhat similar to what I did with my analysis of Leo Mazzone’s impact on pitchers (see Chapter 5 of my book).

After reading the study, I am not convinced by the authors’ conclusions. It’s not just one thing, but a collection of issues that form my opinion. I have problems with both the study’s design and the interpretation of the reported results. My disagreement does not mean that the effect does not exist, only that I do not see a pattern consistent with Canseco spreading steroids to his teammates.

First, I want to start with the sample. The authors look at players from from 1970–2003. I find this an odd range of seasons to select. Canseco’s career spans from 1985–2001. Why start a decade and a half before Canseco enters the league and stop two years after he exits? The asymmetry bothers me largely because the run-scoring environment preceding Canseco was much lower than it was during the latter part of his career. But even without this, it is a strange choice to make. I can only guess that there is some teammate of Canseco’s whose career extends back this far, but I still don’t agree with the choice. And why not extend the sample until the present?

Next, the authors set the cutoffs for examining player performance at 50 at-bats for hitters and 10 games for pitchers. These minimums are far too low even when stats are normalized for playing time, but the impact is much worse when looking at absolute statistics like total home runs, which the authors do. For pitchers—who I will not examine here—it’s possible to get pitchers who pitched very few innings.

The authors also make a strange choice to break hitters into two classes: power and skilled players. The idea is that we might see different effects on the different styles of play. I don’t agree with this, but that is not the weird part. The way they differentiate power and skilled players is by position played, weird but moderately defensible. The power positions are first base, designated hitters, outfielders, and catchers. The skilled positions are second base, shortstop, and third base. And it becomes clear that the authors are not all that familiar with baseball. Catcher is a “power” position? Third base is a skill position? I suspect that the catcher and shortstop positions produce the least offense of all the positions. Sure, you can point to a power-hitting catcher like Mike Piazza, but you can also point to a punchless first basemen like Doug Mientkiewicz, but in general catcher and first base are at the opposite ends of defensive skill with very different offensive expectations. Center field is also a defensive position that should not be lumped in with the corner positions. This highlights the problem of separating power potential by position. And, it’s not so much that the way that the sample spliced—which don’t like—but the fact that it is being spliced at all makes me suspicious.

The choice of dependent variables is also bit strange. While the authors are mainly looking for changes in power, they pick only a few metrics that measure power: HR, SLG, and HR/AB. The other statistics include AVG, RBI, K, BB, IBB, at-bats, fielding percentage, errors, and steals. I have no problem with AVG. RBI is completely useless since it is largely dependent on teammates. K, BB and IBB are chosen because they correlate with home run hitting. But, performance in this area is also correlated with other things such as plate discipline, and the authors are already looking at home runs. This just adds columns to the regression table, that would have been better-used doing robustness checks on the sample and control variables. I would have liked to have seen isolated power (SLG–AVG), HR/H, OBP, and OPS.

As for the control variables, many of the choices are not intuitive. The batting average of the division (subtracting out own-team performance), the manager’s lifetime winning percentage, the batting park factor, years of experience (listed as a continuous variable in the text, but reported as a matrix of dummies in the regression tables), year effects, and dummies for each division. Also, the equation is estimated with fixed effects to control for individual player attributes.

I wouldn’t have chosen some of these same variables, but I don’t think they make much difference. However, I am perplexed by the inclusion of manager’s winning percentage and division dummies. I don’t see any obvious potential bias from the quality of the manager. In any event, managerial dummies are probably the better choice. Mangers with players who perform better will have higher winning percentages, so a positive correlation is to be expected, but the causality is difficult to determine. However, this isn’t a huge issue.

The division dummies make no sense. The divisions changed their compositions at several points during the sample—the most extreme change occurs when a Central Division was added to both leagues in 1994—and there are no common rules or kinds of play that are really unique to any division. If there was such an effect, the batting average of the division and year effects should catch this. It would have made more sense to include league dummies, because of the significant differences in play between the leagues after the introduction of the DH in 1973. In any event, the authors state that the control variables do not alter the results. I would have liked to see some results with different controls.

Now, to the variable(s) of interest. When I initially looked at the study, flipped to the regression tables first and noticed that there did not appear to be a “Canseco effect,” because the estimate on playing with Canseco was not statistically significant. But, that is not what the authors use to quantify Canseco’s impact; we are supposed to look at a second variable that identifies the seasons after playing with Canseco. The intuition is that “even if he did learn steroids from Canseco, we do not know when he learned about it during his time with Canseco, but we can be sure that he already acquired the knowledge after player with Canseco” (p. 10). I just don’t buy this. I understand that it might take a while for the effect to kick in, but this should still manifest itself in the “played with” variable, especially because many players played with Canseco for multiple seasons. At best this story makes sense only for guys who might have played one season with Canseco (more on this below). Second, anabolic steroids work quickly, so it’s unlikely that there would be a delayed effect.

After reading the paper, I came to the conclusion that the results are probably fragile. So, I designed a similar, but not identical, dataset. I did almost everything the authors did, except I did not break the sample into power and skilled players, and I included league dummies instead of division dummies, because I feel this is a superior choice. I also kicked out some partial seasons when guys switched teams to make life easier in developing the dataset. Thus, what I am doing is “replication” in the sense of looking for a similar result in the data, rather than trying to recreate the previous estimates. If the result is real, then I should find something similar. Here is what I found looking at raw home run totals (control variable estimates not reported).

		50 AB	200 AB	50 AB		200 AB		Corrected
With Canseco	-0.297	-0.199	-1.28E-03	-9.39E-04	-0.449
		[0.66]	[0.35]	[1.41]		[0.93]		[0.87]
After Canseco	0.667	0.737	3.49E-04	6.28E-04	-0.204
		[1.58]	[1.34]	[0.41]		[0.65]		[0.34]
Observations	15,644	9,234	15,644		9,234		12,759
Players		2,885	1,717	2,885		1,717		2,265
R-squared	0.13	0.14	0.09		0.13		0.08
Absolute value of t statistics in brackets					

The coefficient on for playing with Canseco is negative and insignificant and the after Canseco coefficient is positive with a p-value of 0.12, which is above the standard (0.05) and lenient (0.1) thresholds for statistical significance. That is the best that I could get. When I up the at-bat minimum to the more appropriate 200, normalize home runs for at-bats, and both, “played with” is negative and never significant, and “after’s” p-value is never as low as it was in the specification that most-closely resembles the study. Another potential problem that I encountered was serial correlation in the data. This is sometimes difficult to detect, and it is possible that it is a problem unique to my sample. However, when I correct for the problem, both Canseco variables consistently have high insignificant p-values. So, though the authors find some evidence of an effect in the after variable in their sample, the finding appears not be all that robust.

The one thing that bothers me most about this study is that we have to interpret why the “after Canseco” variable is important, but the “during” variable is not as important. And I think the author’s story really only applies to players who are with Canseco for one season. So, I ran some regressions using players who played with Canseco for only one year.

		One-year	One-year 
				10+ Career
With Canseco	-2.656		-3.450
		[3.02]**	[3.17]**
After Canseco	-2.562		-3.027
		[2.84]**	[2.95]**
Observations	1,200		940
Players		186		100
R-squared	0.18		0.23
Absolute value of t statistics in brackets		
* significant at 5%; ** significant at 1%		

The effects of during and after playing with Canseco are strongly negative, about 2.5 less homers. However, if they only played on year with him it could reflect that these players were not very good and were on their way out of the league. So, I limited the sample to players with careers of 10 or more seasons; and, the result is a decline in homers of about 3 HRs both with and after.

My point of offering this “replication” isn’t so much to say that my specifications are superior. I just want to show that the findings do not appear to be robust. To concur with the conclusions presented in the study you have to interpret the findings in a way that I do not believe is correct. Upon further examination, I believe the significant effect on home runs after playing with Canseco identified in the Gould and Kaplan study is a product of spurious correlation, and thus this tells us little about Canseco impact on disseminating steroids throughout baseball.

Why the Oswalt Deal Worked

Yesterday, the Philadelphia Phillies acquired Roy Oswalt for J.A. Happ, Anthony Gose, and Jonathan Villar from the Houston Astros. The general reaction of to the deal has been quite negative toward the return to Houston. Oswalt is an ace starter. Gose (whom the team immediately sent to Toronto for Brett Wallace) and Villar are low-minors prospects. How could Astros GM Ed Wade get so little in return?

It’s interesting that several years ago Ed Wade was on the other side of one of these supposed heists for the Phillies, acquiring Kevin Millwood for Johnny Estrada. It just so happens that the opening chapter of my book on valuing players Hot Stove Economics (forthcoming in October) is titled: “Why Johnny Estrada Is Worth Kevin Millwood: Valuing Players as Assets.” In the chapter, I explain how players so different in ability were swapped for each other without bringing stupidity into the equation. The difference was their salary requirements. While I can’t go into the details here, Millwood would receive $11 million the following year (which was way more than his expected worth), while Estrada would get less than $1 for two years of service before being traded for two relievers.

Now we have Oswalt, who is owed just under $25 million for the remainder of his contract (the Phillies are kicking in and extra million for the buyout of his option). The Phillies are winning team and thus value his performance much more than the Astros, because there are increasing returns to winning. I estimate his expected performance through 2011 is worth about $28 million to the Phillies—a little over $3 million more than his salary obligations. The Astros are also sending along $11 million, which seems excessive until you remember the prospects. A year ago, Happ was pitching decently in the majors, and considered untouchable commodity. He’s been injured, but injuries heal. Let’s assume that Happ pitched at his true performance last year for the Phillies. Based on the value of his performance, and his expected salary obligations (one more purely-reserved year and three arbitration years), I estimate he’s worth about $12 million (discounted present value of performance for four years under team control). But, his injury risk lowers his expected return somewhat. Then the other prospect come into the deal. They are at such a low level that I won’t try and project them from minor league stats (stats below high-A are close to useless), but they certainly have value.

My point here isn’t to calculate the exact value to see whom got the better end of this deal. I want to understand why this trade was made. And while a lot of people aren’t high on Ed Wade, he and his baseball people have some sense of what players are worth. I think the deal is defensible from the Astros perspective, especially considering that Oswalt has some post-season value to the Phillies that the Astros can’t capture without trading him to where his services are more highly valued.

Addendum: Bottom line, Osawlt is the superior player, but expensive. Happ and the other prospects are inferior, but cheap.

The Bobby Cox Effect

Thomas Lake has a nice retrospective article on Bobby Cox’s ejections in the current issue of Sports Illustrated. If you have read it, you might have seen my brief contribution.

FEW HUMAN endeavors have been studied so closely by so many people with such fascination for such a long time as the game of baseball. Historians, economists and statisticians scrutinize everything that happens and compare it with everything else that already happened, going back to 1871. This ocean of numbers can tell us a lot about Bobby Cox. For example: He makes pitchers better. J.C. Bradbury, author of the 2008 book The Baseball Economist: The Real Game Exposed, looked at pitchers who had thrown for multiple teams and compared their performances for Cox with their performances for other teams. Using a sophisticated technique called multiple regression analysis, Bradbury factored out variables such as hitter-friendly ballparks, league ERA differences, team defense and pitchers’ ages. What remained was a meaningful Cox Effect, worth about a quarter of a run every nine innings. (True, the Leo Mazzone Effect was even larger, but the Cox Effect existed even in the 14 years Mazzone wasn’t Cox’s pitching coach.)

I looked at pitchers with more than 30 innings pitched in a season and hitters with more than 100 plate appearances who played for Bobby Cox and at least one other manager. The tables below report the estimates. The performance numbers are park corrected.

Bobby Cox       -0.256

Career ERA      0.833

LgERA   	0.249

Tm BABIP        10.839

Age     	-0.341

Age2    	0.006

Constant        1.686

Observations    1519
R-squared       0.29
Robust t statistics in parentheses      
* significant at 5%; ** significant at 1%  
Bobby Cox       -0.006

Career OPS      0.935

LgOPS   	0.415

Age     	0.028

Age2    	-0.00046

Constant        -0.670

Observations    1833
R-squared       0.52
Robust t statistics in parentheses      
* significant at 5%; ** significant at 1%   

PrOPS Leaders at the All-Star Break

Here are the PrOPS leaders for the first half of the season (minimum 250 PAs). Introduction to 2010 PrOPS. Introduction to PrOPS.

Top performers:

PrOPS Leaders
	Player			Team	PrAVE	PrOBP	PrSLG	PrOPS
1	Carlos  Gonzalez	COL	0.321	0.411	0.572	0.984
2	Miguel  Cabrera		DET	0.316	0.386	0.594	0.980
3	Joey  Votto		CIN	0.297	0.388	0.567	0.955
4	Justin  Morneau		MIN	0.308	0.392	0.563	0.955
5	Vladimir  Guerrero	TEX	0.309	0.374	0.570	0.943
6	Corey  Hart		MIL	0.285	0.371	0.570	0.941
7	Albert  Pujols		STL	0.305	0.381	0.559	0.940
8	Adrian  Beltre		BOS	0.317	0.405	0.524	0.929
9	Carlos  Quentin		CHW	0.284	0.376	0.553	0.929
10	Paul  Konerko		CHW	0.292	0.368	0.553	0.921
11	Josh  Hamilton		TEX	0.286	0.358	0.557	0.915
12	Andre  Ethier		LAD	0.301	0.379	0.528	0.907
13	Ian  Stewart		COL	0.300	0.395	0.512	0.907
14	Torii  Hunter		LAA	0.311	0.386	0.520	0.907
15	Jose  Bautista		TOR	0.261	0.351	0.555	0.906
16	Magglio  Ordonez	DET	0.332	0.386	0.519	0.905
17	David  Ortiz		BOS	0.264	0.364	0.539	0.903
18	Robinson  Cano		NYY	0.306	0.386	0.516	0.903
19	Miguel  Olivo		COL	0.290	0.378	0.521	0.899
20	Vernon  Wells		TOR	0.288	0.362	0.537	0.899
21	Matt  Holliday		STL	0.301	0.379	0.517	0.897
22	Adrian  Gonzalez	SDP	0.288	0.370	0.524	0.894
23	Kevin  Youkilis		BOS	0.277	0.374	0.519	0.894
24	Adam  Dunn		WSN	0.255	0.359	0.534	0.894
25	Aubrey  Huff		SFG	0.292	0.368	0.520	0.888
26	Ryan  Howard		PHI	0.281	0.375	0.512	0.887
27	Mike  Napoli		LAA	0.277	0.378	0.507	0.885
28	Brennan  Boesch		DET	0.294	0.364	0.517	0.881
29	Scott  Rolen		CIN	0.276	0.360	0.521	0.880
30	Prince  Fielder		MIL	0.273	0.367	0.512	0.880

Second-half rebounds coming?

Top-30 Under-Performers
	Player			Team	OPS	PrOPS	Diff	
1	Yadier  Molina		STL	0.595	0.744	-0.149	
2	Justin  Smoak		TOT	0.657	0.789	-0.132	
3	Adam  Lind		TOR	0.640	0.768	-0.128	
4	Carlos  Lee		HOU	0.682	0.807	-0.125	
5	Hunter  Pence		HOU	0.743	0.867	-0.124	
6	Jose  Lopez		SEA	0.610	0.732	-0.122	
7	Ian  Stewart		COL	0.788	0.907	-0.119	
8	Skip  Schumaker		STL	0.642	0.761	-0.119	
9	Juan  Rivera		LAA	0.708	0.818	-0.110	
10	Carlos  Gonzalez	COL	0.878	0.984	-0.106	
11	Derek  Jeter		NYY	0.732	0.836	-0.104	
12	Pedro  Feliz		HOU	0.546	0.648	-0.102	
13	Cesar  Izturis		BAL	0.569	0.670	-0.101	
14	Aaron  Hill		TOR	0.631	0.732	-0.101	
15	Mike  Napoli		LAA	0.786	0.885	-0.099	
16	Kurt  Suzuki		OAK	0.716	0.812	-0.096	
17	Aramis  Ramirez		CHC	0.648	0.743	-0.095	
18	Todd  Helton		COL	0.646	0.737	-0.091	
19	Aaron  Rowand		SFG	0.681	0.764	-0.083	
20	Alcides  Escobar	MIL	0.630	0.713	-0.083	
21	Orlando  Cabrera	CIN	0.612	0.690	-0.078	
22	Carlos  Pena		TBR	0.737	0.812	-0.075	
23	Russell  Martin		LAD	0.679	0.752	-0.073	
24	Clint  Barmes		COL	0.721	0.791	-0.070	
25	Miguel  Tejada		BAL	0.691	0.761	-0.070	
26	Howie  Kendrick		LAA	0.708	0.778	-0.070	
27	Ty  Wigginton		BAL	0.768	0.837	-0.069	
28	Shane  Victorino	PHI	0.766	0.835	-0.069	
29	Jorge  Cantu		FLA	0.734	0.798	-0.064	
30	Juan  Uribe		SFG	0.758	0.821	-0.063	

Second-half declines on the way?

Top-30 Over-Performers
	Player			Team	OPS	PrOPS	Diff	
1	Ian  Kinsler		TEX	0.831	0.688	0.143	
2	Carl  Crawford		TBR	0.901	0.774	0.127	
3	Andres  Torres		SFG	0.861	0.736	0.125	
4	Nick  Markakis		BAL	0.847	0.726	0.121	
5	Brennan  Boesch		DET	0.990	0.881	0.109	
6	Justin  Morneau		MIN	1.055	0.955	0.100	
7	Rafael  Furcal		LAD	0.898	0.798	0.100	
8	Josh  Hamilton		TEX	1.014	0.915	0.099	
9	Evan  Longoria		TBR	0.895	0.796	0.099	
10	David  DeJesus		KCR	0.855	0.760	0.095	
11	Miguel  Cabrera		DET	1.074	0.980	0.094	
12	Fred  Lewis		TOR	0.779	0.689	0.090	
13	Cliff  Pennington	OAK	0.726	0.637	0.089	
14	Kevin  Youkilis		BOS	0.981	0.894	0.087	
15	Jayson  Werth		PHI	0.881	0.796	0.085	
16	Ben  Zobrist		TBR	0.783	0.699	0.084	
17	Ichiro  Suzuki		SEA	0.785	0.704	0.081	
18	Angel  Pagan		NYM	0.845	0.769	0.076	
19	Troy  Tulowitzki	COL	0.877	0.806	0.071	
20	Andrew  McCutchen	PIT	0.798	0.727	0.071	
21	David  Wright		NYM	0.924	0.854	0.070	
22	Billy  Butler		KCR	0.873	0.805	0.068	
23	Adam  Dunn		WSN	0.959	0.894	0.065	
24	Daric  Barton		OAK	0.772	0.708	0.064	
25	Jason  Bay		NYM	0.779	0.720	0.059	
26	Blake  DeWitt		LAD	0.728	0.670	0.058	
27	Kelly  Johnson		ARI	0.870	0.813	0.057	
28	Joey  Votto		CIN	1.011	0.955	0.056	
29	Josh  Willingham	WSN	0.913	0.857	0.056	
30	Lastings  Milledge	PIT	0.739	0.686	0.053	

Valuing Prince Fielder

Buster Onley ($) has a piece this morning in which he discusses the potential free-agent valuePrince Fielder after his agent Scott Boras made some comparisons to Mark Teixeira. Olney points to the Fielder in the living room when making such comparisons, and notes that several MLB insiders feel his weight is going to prevent him from aging as gracefully as most players. Fielder is so heavy that it’s hard to know what to expect. I think he will ultimately be a DH, and this may keep him in the game longer.

Yet despite his weight, which many talent evaluators thought would keep him from excelling at all, he has been an elite and valuable hitter. If he ages like the average players (possibly a dubious assumption, but it’s hard to know what to expect) and signs a five-year deal (equivalent in length to Ryan Howard‘s extension) after the 2011 season, I estimate the value of the deal in total dollars paid out would be $104 million, or a little under $21 million per year. It’s not quite Teixeira money, but it’s in the neighborhood. Concerns about his weight, justified or not, will probably prevent him from signing a deal this long, but I guess we’ll just have to “weight” and see.

2010 PrOPS Over- and Under-Performers (Through 07/01)

PrOPS updated through July 1 (minimum 240 PA). I report the top-30 over- and under-performers. Introduction to 2010 PrOPS. Introduction to PrOPS.

Top-30 Over-Performers

Rank	Player			Team	OPS	PrOPS	Diff	PA
1	Andres  Torres		SFG	0.814	0.680	0.134	269
2	Ian  Kinsler		TEX	0.811	0.684	0.127	244
3	Carl  Crawford		TBR	0.869	0.742	0.127	322
4	Nick  Markakis		BAL	0.821	0.699	0.122	340
5	Justin  Morneau		MIN	1.059	0.938	0.121	327
6	David  DeJesus		KCR	0.875	0.756	0.119	330
7	Andrew  McCutchen	PIT	0.825	0.710	0.115	332
8	Josh  Hamilton		TEX	0.993	0.880	0.113	328
9	Jayson  Werth		PHI	0.919	0.813	0.106	308
10	Daric  Barton		OAK	0.798	0.692	0.106	352
11	Kevin  Youkilis		BOS	0.983	0.878	0.105	322
12	Ichiro  Suzuki		SEA	0.813	0.716	0.097	351
13	Ben  Zobrist		TBR	0.797	0.710	0.087	336
14	Franklin  Gutierrez	SEA	0.767	0.681	0.086	311
15	Lastings  Milledge	PIT	0.715	0.634	0.081	263
16	Jason  Bay		NYM	0.812	0.732	0.080	323
17	Fred  Lewis		TOR	0.774	0.695	0.079	272
18	Brandon  Phillips	CIN	0.841	0.766	0.075	357
19	Troy  Tulowitzki	COL	0.877	0.806	0.071	265
20	Evan  Longoria		TBR	0.870	0.803	0.067	342
21	Colby  Rasmus		STL	0.921	0.856	0.065	275
22	Miguel  Cabrera		DET	1.040	0.976	0.064	325
23	Brett  Gardner		NYY	0.811	0.747	0.064	278
24	Cliff  Pennington	OAK	0.704	0.644	0.060	296
25	Adam  Dunn		WSN	0.917	0.858	0.059	327
26	Johnny  Damon		DET	0.753	0.695	0.058	302
27	Elvis  Andrus		TEX	0.706	0.649	0.057	344
28	David  Wright		NYM	0.929	0.874	0.055	338
29	Martin  Prado		ATL	0.857	0.803	0.054	367
30	Albert  Pujols		STL	0.989	0.936	0.053	346

Top-30 Under-Performers

Rank	Player			Team	OPS	PrOPS	Diff	PA
1	Hunter  Pence		HOU	0.730	0.876	-0.146	313
2	Ian  Stewart		COL	0.738	0.866	-0.128	270
3	Yadier  Molina		STL	0.615	0.742	-0.127	267
4	Carlos  Lee		HOU	0.669	0.796	-0.127	319
5	Jose  Lopez		SEA	0.603	0.726	-0.123	325
6	Adam  Lind		TOR	0.608	0.729	-0.121	322
7	Skip  Schumaker		STL	0.655	0.768	-0.113	288
8	Justin  Smoak		TEX	0.697	0.800	-0.103	250
9	Derek  Jeter		NYY	0.754	0.857	-0.103	361
10	Carlos  Gonzalez	COL	0.825	0.925	-0.100	301
11	Juan  Rivera		LAA	0.725	0.820	-0.095	258
12	Pedro  Feliz		HOU	0.572	0.664	-0.092	255
13	Todd  Helton		COL	0.657	0.749	-0.092	281
14	Aaron  Hill		TOR	0.642	0.719	-0.077	287
15	Carlos  Pena		TBR	0.728	0.804	-0.076	323
16	Clint  Barmes		COL	0.706	0.781	-0.075	257
17	Mike  Napoli		LAA	0.838	0.912	-0.074	262
18	Derrek  Lee		CHC	0.699	0.772	-0.073	334
19	Miguel  Tejada		BAL	0.695	0.768	-0.073	325
20	Jason  Bartlett		TBR	0.631	0.702	-0.071	258
21	Alcides  Escobar	MIL	0.640	0.710	-0.070	282
22	Orlando  Cabrera	CIN	0.625	0.692	-0.067	337
23	Russell  Martin		LAD	0.678	0.743	-0.065	300
24	Carlos  Quentin		CHW	0.784	0.848	-0.064	279
25	Shane  Victorino	PHI	0.767	0.829	-0.062	346
26	Melky  Cabrera		ATL	0.653	0.715	-0.062	265
27	Howie  Kendrick		LAA	0.718	0.779	-0.061	336
28	A.J.  Pierzynski	CHW	0.651	0.711	-0.060	250
29	Ty  Wigginton		BAL	0.808	0.865	-0.057	299
30	Mark  Teixeira		NYY	0.757	0.812	-0.055	354