Fixing the Reds' bullpen

John Fay has been predicting that the Reds will target relievers in the free agent market this offseason in an effort to build up the ballclub's pitching, mostly because the market for starters is so dismal. The Reds most certainly need better run prevention, and their bullpen has been terrible since '03 or so. So it makes sense to target relievers, right?

Well, perhaps not. Tom Tango posted this about an article on the San Diego Padres' Kevin Towers by Tom Verducci at SI.com:

Looks like Kevin Towers and the Padres know what they are doing. Free-agent relievers are incredibly overpaid. You can pick out the teams that have no idea that the replacement level for pitchers as relievers is so high. Orioles last year, and Phillies this year. I’m always shocked when starters are released, and not given a try as a reliever. If they were fighting for a job as the 5th starter (and basically, the 7th best pitcher on the team), doesn’t it make sense that they could fight for the #3 or #4 guy in the bullpen?

I'll have more on valuation of relievers and starters in my next article on player value (sorry for the delay--been distracted with parents visiting, etc). But this argument here is not new. Think about all the ex-starters (and often failed starters) who have found their niche as relievers. Just naming those who have been associated with the Reds in recent years, we have David Weathers, Kent Mercker, Mike Remlinger, Chris Hammond, Josh Hancock, Ryan Dempster, Ryan Franklin... The list of those successfully going the other way is much more limited (Derek Lowe, .... uhhh... wait, I'm sure there's more...Danny Graves? No, that didn't work... hmm...).

Relieving is easier than starting. There are a variety of potential reasons for this, and I'm not sure that we really know the underlying causes. But there have been a good number of studies verifying that the little anecdote-based argument about this actually does correspond to the real differences in value.

Therefore, mining the failed starter ranks--especially those from the superior American League--might be a more efficient way to build up the Reds' bullpen than shelling out 3-4 million dollars at a time on established relievers. Might not get you a Cordero-esque closer, but it seems to be a good way to get some quality middle relief on the cheap.

Nuxhall in Hospital

Joe Nuxhall is in the hospital and is undergoing treatment for pneumonia. Early indications are that he is doing well, but I wanted to send out a quick get-well wish...'cause you know, I'm sure he's surfing around reading the various Reds blogs tonight. Hope he makes a quick recovery.

While we're at it, give Nuxhall a Hall of Fame vote. He sure as heck deserves it.

Update: Apparently, while the pneumonia seems to be under control, Nuxhall is due to get a pacemaker due to low pulse rate. I'm honestly not sure how substantial of a surgery a pacemaker installation is. But at the very least, it sounds like he's getting excellent care.

PMR on the Reds

David Pinto continues to release his PMR data at a steady clip. I consider PMR superior to RZR because of the additional adjustments it makes for batter handedness and park factors, so I will be updating my composite fielding estimates and total value estimates for Cincinnati Reds based on his data once he's run through the seven non-catcher/pitcher positions.

For now, though, it's nice to see some Reds getting love from Pinto's system:

• Brandon Phillips - +46 outs, or ~+35 runs. Wow.
  
• Josh Hamilton - +4 outs (+3.6 runs) in CF. This will push his overall fielding estimate in CF above average in 2007. The Fans also rate him as just above average, so I believe it. Maybe he'll be ok to have out there, at least for the next few years.
  
• Norris Hopper - +1 outs in CF.
• Alex Gonzalez - +0 outs at SS. That's a disappointment, though he'll still be above-average based on the other stats.
  
• Jeff Keppinger - -6 outs at SS (-4.5 runs).
  

Update: Edwin Encarnacion was rated just -7 plays below average (-5.6 runs) at 3B according to PMR, which is much better than his -21.9 RZR rating this season, and much more consistent with his Fans' and ZR ratings. Why? I'm not sure, as PMR and RZR use the same hit location data; maybe there were an unusually large number of lot of hard-hit balls through his position this year? Replacing Eddie's PMR value with his RZR value will give Eddie's total value a boost of ~5 runs or so, which will shoot him into Scott Hatteberg territory. Very nice to see some more numbers supporting the conventional wisdom that he improved significantly this season with his glove.

Also interesting are his estimates of fielding behind pitchers, which can be considered a more precise way of assessing BABIP (or DER, depending on your preference) because it considers hit location, batted ball type, etc:

• Aaron Harang, +5 outs
• Matt Belisle, -0.5 outs
• Kyle Lohse (when with Reds), -3.7 outs
• Bronson Arroyo, -5.6 outs

If we convert those out values to an approximate runs stat (I'll use 0.8 runs/missed out), we can then subtract those values from actual runs allowed and get an approximate fielding-independent R/9 (listed below as PMR-R/9). I'll list that along with FIP, another way of trying to normalize pitcher performance:

Name	ERA	FIP	R/9	PMR-R/9
Harang	3.73	3.70	3.88	4.04
Belisle	5.32	4.54	5.62	5.60
Lohse	4.58	4.50	5.19	4.99
Arroyo	4.23	4.51	4.66	4.47

FIP is an estimate of ERA based strictly on readily available pitcher peripherals -- k/9, bb/9, and hr/9. Differences between FIP and ERA, in general, are due to poor fielding, "clumping" of offensive events in a manner that deviates from the norm, or non-hr park effects. Differences between R/9 and PMR-R/9, on the other hand, can probably be considered to be strictly due to differences in fielding.

Generally spreaking, the differences between FIP and ERA are much larger than differences between R/9 and PMR-R/9, at least among these four pitchers. In fact, differences between the two pairs of stats only move in the same direction twice! This indicates that the timing of offensive events (which is largely the result of luck) may have more to do with differences between actual and DIPS-based ERA estimates than the Reds' below-average fielding. That jives with Tom Tango et al.'s breakdown of variation in BABIP:

luck: 44%
pitcher: 28%
fielding: 17%
park: 11%

Neat stuff.

Total player value around MLB

Skyking162 is doing a wonderful position review series at his site, and he's ranking players following a very similar methodology to that I've discussed in the player value series. In fact, our e-mail conversations last month were a big part of the reason that I decided to do my series--he really helped me take my thinking to the next level. If you see differences in our rankings, it's because a) he uses a slightly different base runs model than I do to generate linear weights, and b) his fielding estimates are based on ZR and RZR only, whereas I threw in FSR as well. As far as Cincinnati Reds are concerned, the biggest discrepancy between his and my datasets is Brandon Phillips, for whatever reason.

Here are links to the positions he's covered thus far: First Base, Second Base, Left Field, and Center Field.

His blog has really become a great read for general baseball analysis and opinion since his return a few months back.

Player Value, Part 4: Position Player Wrap-Up

To view the complete player value series, click on the player value label on any of these posts.

Let's go over the main principles we've covered thus far:

1. A run saved is worth the same as a run scored. Therefore, estimates of player value require one to consider both offense and defense.

2. We often find it useful to compare players to replacement players, who we assume will hit at 73% of league average and play league-average defense at a given position.

3. A player's total offensive contributions can be estimated using linear weights, relative to replacement player offensive production levels.

4. A player's fielding performance can be best estimated using the combined estimates of several different statistics. Because replacement players can be assumed to be league-average fielders, we simply report all fielding statistics as +-average at a position.

5. We also need to add a position adjustment to a player's fielding estimate to signify the differences in value (i.e. average difficulty) of playing one position vs. another. This should be pro-rated for the player's playing time (defensive innings).

Now, let's put it all together and look at a case study.

The 2007 Reds Position Players

Fielding

While I've already posted offensive value estimates for the '07 Reds, I haven't yet posted fielding data, so let's start there.

Below I've used combined data from the Fans' Scouting Report, ZR, RZR, and the catcher statistics (C-Runs below) to estimate fielding runs saved vs. average. Methods are as described in my previous posts. ZR and RZR data are the summed values across all positions a player played. FSR data are calculated using Tango's custom weights for each position a player plays, pro-rated for his defensive innings at each position (Player innings out of 1440), and summed. I've also added a position adjustment (using Tom Tango's numbers) to the data to get an estimate of total fielding value.

Here are the data (sorry about the missing cells--nature of the beast). Fielding is the +-Fielding value relative to a player's position. TtlFldVal is a player's total fielding value, which is the sum of Fielding and the position adjustment (PosAdj). All values are reported as +-runs.

Name	PriPos	Inn	FSR	ZR	RZR	C-Runs	Fielding	PosAdj	TtlFldVal
D Ross	C	837	6.9			6.3	6.5	5.8	12.3
B Phillips	2B	1371	15.3	-0.8	15.1		9.2	0.0	9.2
N Hopper	CF	607	0.6	4.3	11.2		6.0	0.9	6.8
A Gonzalez	SS	872	2.2	3.2	5.4		3.8	2.4	6.2
R Freel	CF	577	-0.7	-4.7	7.8		1.0	1.5	2.5
J Ellison	RF	82		1.8	3.5		2.1	-0.2	1.8
D Wise	CF	13		0.4	0.6		0.5	0.0	0.6
M Bellhorn	3B	14		0.2	-0.1		0.0	0.0	0.0
R Hanigan	C	20				-0.1	-0.1	0.1	0.0
J Keppinger	SS	509	-3.4	2.9	-3.2		-1.0	1.0	0.0
R Jorgensen	C	34				-0.2	-0.2	0.2	0.0
J Valentin	C	472	-4.4			-3.1	-3.4	3.3	-0.1
J Hamilton	CF	663	2.0	2.3	-8.4		-1.8	1.6	-0.2
C Moeller	C	87				-0.9	-0.9	0.6	-0.3
B Coats	RF	68		0.0	-0.5		-0.3	-0.1	-0.4
P Lopez	SS	93		-2.6	1.2		-0.7	0.3	-0.4
E Cruz	SS	2		-0.7	-0.7		-0.7	0.0	-0.7
J Castro	SS	178	0.0	1.0	-4.9		-1.5	0.2	-1.3
J Cantu	1B	112		-1.7	0.1		-0.8	-0.6	-1.4
J Conine	1B	435	-1.9	1.4	-2.8		-1.0	-2.4	-3.4
J Votto	1B	188		-0.7	-5.7		-3.2	-0.9	-4.1
S Hatteberg	1B	772	0.6	7.4	-11.1		-1.2	-4.3	-5.5
E_Encarnacion	3B	1168	-1.1	3.0	-21.9		-7.4	-0.8	-8.2
K Griffey	RF	1163	-1.0	-1.2	-17.1		-7.1	-3.2	-10.4
A Dunn	LF	1189	-14.2	-4.1	-17.5		-11.6	-3.3	-14.9

According to these data, the Reds' most valuable fielders in 2007 were David Ross, Brandon Phillips, Norris Hopper, and Alex Gonzalez. Phillips was the best at his position of any Red in terms of runs saved vs. average, but Ross (who was ranked the 8th most valuable catcher in MLB) played a more valuable position, and this pushed his overall ranking to the top. Strangely, Phillips was rated as merely average by ZR, which speaks to the differences between the BIS and STATS hit location datasets.

In contrast, the Reds' worst fielders were Adam Dunn, Ken Griffey Jr., Edwin Encarnacion, and Scott Hatteberg. Dunn got hammered by all of the fielding statistics, so there's clear consensus that he's Not Good in left field. In contrast, Griffey was rated as roughly average by both Fans and ZR, but was hammered by RZR. This is largely because the latter statistic places far more weight on plays out of zone, which is where Griffey's performance really fell short. Edwin was also rated differently by the two datasets, though in his case it's not clear why RZR dislikes him so much more than ZR.

Overall, I really like how total fielding value ranks these players. Compared to rankings that are strictly relative to positions, the total fielding value rankings recognize the inherent differences in value among different positions, which results in appropriate looking boosts to guys playing hard positions (C, CF, SS) and penalties to guys playing easier positions (1B, LF, RF).

Total Value

Now, the moment we all (or, at least, I) have been waiting for: let's put together our offensive and fielding numbers and rank the '07 Reds non-pitchers based on their total value!!!

In the table below, RAR represents a player's offense (runs above replacement), while TtlFld represents a player's defensive value (+- fielding and a position adjustment). Total_Value is the combined value of offense and defense!

Name	POS	RAR	TtlFld	TotalValue
B Phillips	2B	30.2	9.2	39.4
A Dunn	LF	51.3	-14.9	36.4
K Griffey Jr.	RF	36.4	-10.4	26.0
J Hamilton	CF	25.2	-0.2	25.0
S Hatteberg	1B	26.9	-5.5	21.4
A Gonzalez	SS	13.3	6.2	19.5
J Keppinger	SS	17.1	0.0	17.1
N Hopper	CF	10.8	6.8	17.6
EEncarnacion	3B	24.8	-8.2	16.7
D Ross	C	-4.6	12.3	7.7
J Valentin	C	3.2	-0.1	3.1
J Cantu	1B	3.8	-1.4	2.4
J Votto	1B	7.3	-4.1	3.2
R Freel	CF	-0.2	2.5	2.2
D Wise	CF	0.3	0.6	0.9
J Conine	1B	4.6	-3.4	1.2
R Jorgensen	C	0.4	0.0	0.4
R Hanigan	C	0.3	0.0	0.3
J Ellison	RF	-1.8	1.8	0.0
E Cruz	SS	-0.2	-0.7	-1.0
M Bellhorn	3B	-1.4	0.0	-1.3
B Coats	RF	-1.4	-0.4	-1.8
P Lopez	SS	-3.6	-0.4	-4.1
C Moeller	C	-4.8	-0.3	-5.1
J Castro	SS	-8.2	-1.3	-9.4

Isn't that exciting? Seven long articles worth of methods just to produce this little table!!!! :)

After all of that work, I'm quite comfortable saying what many would probably have been willing to say from the get-go: Brandon Phillips was the most valuable position player on the Cincinnati Reds in 2007! He was their third most valuable hitter, and the second most valuable fielder. Overall, it was an outstanding season by the Reds' second baseman. Hopefully he can continue that success next year.

For all my concerns about Adam Dunn's defense, he still came in a close second as the Reds' second most valuable position player thanks to his tremendous offensive performance. Josh Hamilton also had a strong showing, especially when you consider that he only had 337 PA's. Just think of where he'd be if he could get 650-700 PA's next season while maintaining this level of performance. I get misty-eyed when I think about it.

Without going back to repeat the math throughout his career, Alex Gonzalez may well have had the best all-around season of his career despite getting only 430 PA's. And Hopper and Keppinger may have had what will, in fact, turn out to be the best seasons of their MLB careers (though I'm obviously hoping they have more in the tank).

On the flip side, despite early-season struggles offensively, Edwin Encarnacion did manage to come out with positive value over replacement in this analysis, which is probably an improvement over 2006. His hitting slipped a bit this season overall, but he did show some improvement in his defense. If he can continue to improve on defense, while getting his offensive production back to his '06 rates over a full season, he could be a force to be reckoned with. But if he can't get his offense back on track for a full season, or his fielding slips to '06 levels of misery, he might not keep his job much longer. The kid's only 24, but the Reds will only wait so long for him to put things together. Next season might be his make-or-break season, at least as far as the Reds are concerned.

Finally, David Ross shows up better than I expected thanks to his strong showing on defense. Nevertheless, 8 runs above replacement-level is not acceptable performance from someone who got ~350 PA's on this team. Catching was very clearly the biggest hole in the Reds' lineup, and the place where they could potentially gain the most ground via an acquisition. ... not that the free agent market for catchers is particularly rich.

Coming up next: pitchers!

Brandon Phillips Photo by AP/David Kohl
Edwin Encarnacion Photo by Cincinnati Enquirer

Moved to Feedburner

Hi folks,

I've updated my RSS and Atom feeds to route through Feedburner. Feedburner has a lot of nice features that I'd like to take advantage of, including better compatibility and the availability of some usage stats. :)

Nevertheless, I ran into a couple of problems while porting it over related to the size of the feed. I think I've resolved those issues, but if you're still having difficulties with my feed, you may find it worthwhile to re-subscribe using this feed (click on it):

Sorry for the inconvenience. Please let me know if you have any additional problems!

Player Value, Part 3c: Fielding - Catchers

To view the complete player value series, click on the player value label on any of these posts.

Catchers play the most unique of all eight non-pitcher positions. Many of the skills required to be an effective catcher--calling a game, handling a pitcher, etc--are arguably unique from those required to be effective at other positions. Furthermore, their performance is tightly intertwined with the performance of their pitchers, which makes evaluating catchers harder than other positions, often requiring multiple seasons of data to do well.

Intuitively, one approach you might take is to try to assess a catcher's performance by assessing how pitchers do when interacting with that catcher. This is the reasoning behind catcher ERA (cERA), which tracks the ERA of pitchers while a particular catcher is behind the plate. Nevertheless, in Baseball Between the Numbers, Keith Woolner gave an overview of some of the past research evaluating catchers' influences on their pitchers' performance. Surprisingly, he reported that there was little if any consistent skill that catchers have on their pitchers. For example, cERA did not vary from overall team ERA in a manner that showed any predictive direction from year to year.

Catchers do apparently vary consistently, however, in statistics that are more directly under their control: throwing out baserunners, preventing wild pitches and passed balls, and avoiding errors. To be sure, the pitchers that a particular catcher is receiving can have a substantial effect on these rates. For example, Doug Mirabelli is likely to have a high WP+PB rate simply because he catches knuckleballer Tim Wakefield so often. Furthermore, left-handers are well-documented to hold runners better than right-handers. Nevertheless, catchers do seem to vary in consistent ways with these skills, so I think it's worthwhile to track them. What I describe below is a quick and unsophisticated way of doing this. Hopefully, in the future, I can expand upon this and make it better--but for now, I'd guess that it works pretty well.

For all of this work, I'm using The Hardball Times' catching statistics, which are the best easily-accessible data source on catching that I'm aware of. Here's how I'm estimating runs saved for each variable:

Runs saved via stolen bases

First, I calculate average caught stealing rate across MLB catchers as:

lgCSRate = CS/SBA

where SBA is stolen base attempts. You'll note that we have to back-calculate SBA, SB, and CS values from the reported SBA/9innings, CS%, and Inning values reported by THT. In 2007, lgCSRate = 0.22 CS/SBA.

I then find each catcher's +-caught steals by:

+-CS = CS - (lgCSRate*SBA)

where CS and SBA are the player's values.

Finally, I convert these value to a +-Runs value by:

+-CSRuns = +-CS * 0.63

where 0.63 is the difference in runs (according to linear weights) between a stolen base (0.19 runs scored) and a caught stealing (-0.44 runs scored). This is the "swing" in runs scored between allowing a stolen base and gunning down a runner.

Runs saved via wild pitches and passed balls

I do this via the same general approach as above. First, I calculate league average wild pitch plus passed ball rate as:

lgWPPBRate = (WP+PB)/Inn

where WP+PB is the total number of wild pitches and passed balls in MLB during the season in question, and Inn is the total number of innings caught by all catchers. We have to back-calculate WP+PB from the WP+PB/9inning values reported by THT. Also, ideally, I'd use pitches caught instead of innings in the denominator, but I don't have those data on hand. So anyway, in 2007, lgWPPBRate = 0.042 WP+PB/Inn.

Next, I find each catcher's +-[wild pitches plus passed balls]:

+-WPPB = (WP+PB) - (lgWPPBRate*Inn)

where WP+PB and Inn are the player's values.

Finally, I convert this value to a +-Runs value by:

+-WPPBRuns = +-(WP+PB) * 0.28 * -1

where 0.28 is the average value of a wild pitch or passed ball, according to linear weights. The alternative to a wild pitch or passed ball is no change in base runner or out status (usually), so the straight-up linear weights value of a PB or WP is all we need. I multiply by -1 to make this a runs saved value rather than a runs allowed value.

Runs saved via errors

The final component to "my" catcher fielding estimates is runs saved via errors.

First, I calculate average throwing error (TE) and fielding error (FE) rates across MLB catchers as:

lgTERate = TE/Inn
and
lgFERate = FE/Inn

where TE, FE, and Inn are MLB totals. In 2007, lgTERate = 0.0053 TE/Inn, while lgFERate = 0.0015 FE/Inn.

I then calculate a player's +-TE and +-FE values as:

+-TE = TE - (lgTERate*Inn)
and

+-FE = FE - (lgFERate*Inn)

where TE, FE, and Inn are the player's values.

Finally, I convert these value to a +-Runs saved value like this:

+-TERuns = +-TE * 0.48 * -1
and
+-FERuns = +-FE * 0.75 * -1

You'll note that I'm using different runs values for TE's and FE's. Here's my reasoning. Fielding errors are usually made on plays that would otherwise be outs. For example, a catcher not being able to handle a good throw to the plate results in an error that would otherwise have resulted in an out. Therefore, the run value of making one error above average is the value of the advancing runners (0.48 runs allowed) plus the value of the out (0.27 runs saved), or 0.75 runs total cost to the team.

On the other hand, many catcher throwing errors are made on stolen base attempts, with the runner usually ending up at third. These plays are typically scored as a stolen base plus an error. Therefore, the difference between making the error and not making the error is just the advancement of the runner(s) (~0.48 runs allowed); we can't assume than an out would have been made. It's true that some throwing errors would have resulted in outs (e.g. throwing away a ball on an easily-fielded bunt), but I tend to err on the side of being conservative with fielding statistics. I am, however, very much open to suggestions on how to better handle this issue.

Note: After I completed the above work, I discovered (thanks to a tip by MB) this article by Chone Smith. He describes a very similar methodology for evaluating catchers, though mine differs in two small ways. First, I compare each player's CS's to league average caught stealing rate rather than just using raw runs values for SB's and CS's. I do this because I'm interested in fielding relative to the competition. Second, I don't adjust for the rate at which steals are attempted because I think there are probably too many other factors that can influence that rate. Chone also uses a 0.48 run value for errors, which is encouraging...though I still think an 0.75 run value for fielding errors is appropriate.

Fans' Scouting Report

One additional resource that we have available to us in evaluating catchers is the Fans' scouting report, which, as with players at other positions, can be converted to an approximate +-runs statistic using the skill weightings provided by Tom Tango as well as the assumption that each point is worth ~0.7 runs. These +-runs ratings should be pro-rated relative to the number of innings a player caught out of 1440 (~162 games worth of defensive innings).

These scouting measures are very useful, but my tendency is to down-weight them in recognition of their basis in the subjective impressions of (usually) untrained fans. Therefore, consistent with how I used FSR data with other position players, I'm estimating overall catcher fielding as:

+-Fielding = .75*([+-CSRuns] + [+-WPBPRuns] + [+-TERuns] + [+-FERuns]) + .25*FSR

2007 Catchers

Using the procedure outlined above, below are fielding estimates for 2007 catchers with a minimum of 400 innings behind the plate. FWIW, the correlation between the FSR data and the sum of the empirical fielding ratings (listed below as +-Runs) was 0.60, which is encouraging.

My estimates put the difference between the best (Yadier Molina) and worst (Josh Bard) catchers in 2007 as being ~20 runs, or about two wins. This is less of a difference than we see among other positions, but remember that catchers usually don't play as many innings as players at other positions. I'm also probably not accounting for all the ways that catchers may differ. But I think we're capturing at least an important component of catcher defensive abilities.

Last	First	Tm	Inn	CSRns	WPPBRns	TERns	FERns	+-Runs	FSR	+-Fielding
Doumit	Ryan M	PIT	224	-1.0	-1.0	0.6	-0.5	-1.9	#N/A	#N/A
Heintz	Chris J	MIN	137	-1.3	-0.9	0.3	0.1	-1.7	#N/A	#N/A
Thigpen	Curtis B	TOR	126	1.0	-0.2	0.3	0.1	1.2	#N/A	#N/A
Soto	Geovany	CHN	122	0.6	0.9	0.3	0.1	1.9	#N/A	#N/A
Blanco	Henry	CHN	109	-0.1	-1.0	0.3	0.1	-0.7	#N/A	#N/A
DiFelice	Mike	NYN	107	-0.4	0.1	0.3	-1.4	-1.3	#N/A	#N/A
Stewart	Chris D	TEX	105	0.8	-2.1	-0.7	0.1	-1.9	#N/A	#N/A
Towles	J.R.	HOU	95	1.1	-0.6	0.2	0.1	0.8	#N/A	#N/A
Moeller	Chad	CIN	87	-1.1	-0.1	0.2	0.1	-0.9	#N/A	#N/A
Cash	Kevin	BOS	82	-0.3	0.7	-0.3	0.1	0.2	#N/A	#N/A
Maldonado	Carlos L	PIT	79	-0.1	0.1	0.2	0.1	0.3	#N/A	#N/A
Miller	Corky	ATL	62	0.2	-0.1	0.2	0.1	0.3	#N/A	#N/A
Pena	Brayan E	ATL	59	0.4	-0.1	0.2	0.1	0.5	#N/A	#N/A
Molina	Gustavo	CHA	57	-0.3	-0.2	0.1	0.1	-0.2	#N/A	#N/A
LaForest	Pete	SD	57	-0.7	0.1	-0.3	0.1	-0.9	#N/A	#N/A
House	J.R.	BAL	46	0.1	-0.6	0.1	0.1	-0.3	#N/A	#N/A
Budde	Ryan D	LAA	46	#VALUE!	-0.3	-0.4	0.1	#VALUE!	#N/A	#VALUE!
Alomar Jr.	Sandy	NYN	41	1.5	0.2	0.1	0.0	1.8	#N/A	#N/A
Cota	Humberto	PIT	41	-0.5	-0.1	0.1	0.0	-0.4	#N/A	#N/A
Hammock	Robby	ARI	39	1.3	-0.4	0.1	0.0	1.1	#N/A	#N/A
Phillips	Paul A	KC	39	#VALUE!	-0.4	0.1	0.0	#VALUE!	#N/A	#VALUE!
Rivera	Mike	MIL	37	0.2	-0.1	0.1	0.0	0.2	#N/A	#N/A
Gil	Geronimo	COL	35	-0.3	-0.4	0.1	0.0	-0.6	#N/A	#N/A
LeCroy	Matthew	MIN	35	-1.1	-0.1	-0.4	0.0	-1.6	#N/A	#N/A
Jorgensen	Ryan W	CIN	34	0.0	0.1	-0.4	0.0	-0.2	#N/A	#N/A
Lucy	Donny	CHA	33	-1.1	0.1	0.1	0.0	-0.9	#N/A	#N/A
Moeller	Chad	LAN	29	#VALUE!	0.3	0.1	0.0	#VALUE!	#N/A	#VALUE!
Quiroz	Guillermo A	TEX	29	-0.7	0.1	-0.4	-0.7	-1.8	#N/A	#N/A
Riggans	Shawn W	TB	27	-0.4	-0.2	-0.9	0.0	-1.5	#N/A	#N/A
Molina	Gustavo	BAL	25	-0.1	-0.3	0.1	0.0	-0.3	#N/A	#N/A
Hanigan	Ryan M	CIN	20	-0.1	0.0	0.1	0.0	-0.1	#N/A	#N/A
Phelps	Josh	PIT	16	-0.6	-0.1	0.0	0.0	-0.6	#N/A	#N/A
Hoover	Paul	FLA	13	-0.1	0.2	0.0	0.0	0.1	#N/A	#N/A
Sammons	Clint J	ATL	9	0.4	0.1	0.0	0.0	0.5	#N/A	#N/A
Johnson	Rob	SEA	6	#VALUE!	0.1	0.0	0.0	#VALUE!	#N/A	#VALUE!
Bellorin	Edwin	COL	5	0.0	-0.2	0.0	0.0	-0.2	#N/A	#N/A
Morales	Jose G	MIN	4	#VALUE!	0.0	0.0	0.0	#VALUE!	#N/A	#VALUE!
Biggio	Craig	HOU	2	#VALUE!	0.0	0.0	0.0	#VALUE!	#N/A	#VALUE!
Phelps	Josh	NYA	1	#VALUE!	0.0	0.0	0.0	#VALUE!	#N/A	#VALUE!
Esposito	Brian J	STL	1	#VALUE!	0.0	0.0	0.0	#VALUE!	#N/A	#VALUE!
Feliz	Pedro	SF	0	0.0	0.0	0.0	0.0	0.0	#N/A	#N/A
Molina	Yadier	STL	861	8.1	1.6	-0.2	0.2	9.7	17.6	11.5
Johjima	Kenji	SEA	1107	8.2	0.7	2.8	0.5	12.1	5.1	10.3
Mauer	Joe	MIN	778	6.5	0.0	1.5	0.8	8.9	15.2	10.3
Laird	Gerald	TEX	987	11.1	0.0	-1.8	-1.2	8.1	9.6	8.4
Varitek	Jason	BOS	1064	0.5	6.9	0.8	-0.3	7.9	6.3	7.4
Redmond	Mike	MIN	483	3.2	3.5	1.2	0.5	8.4	4.4	7.4
Martin	Russell	LAN	1254	4.3	2.3	-3.5	1.4	4.5	16.5	7.3
Ross	David	CIN	837	6.3	-0.8	0.7	0.2	6.3	6.9	6.4
Snyder	Chris R	ARI	891	1.8	0.3	1.8	1.0	4.8	11.7	6.4
Schneider	Brian	WAS	1051	2.8	0.3	-0.2	1.1	4.0	12.7	6.1
Martinez	Victor	CLE	1043	5.0	3.2	2.2	-1.1	9.3	-4.2	6.0
Ruiz	Carlos	PHI	913	1.4	1.7	1.4	1.0	5.5	6.8	5.8
Ausmus	Brad	HOU	907	-2.4	5.9	0.4	1.0	4.9	7.9	5.5
Miller	Damian	MIL	446	2.0	-0.3	1.1	0.5	3.3	2.9	3.2
Shoppach	Kelly B	CLE	420	3.2	0.3	-0.4	-0.3	2.8	3.9	3.0
Rodriguez	Ivan	DET	1053	2.9	-4.3	0.8	-0.4	-1.0	13.8	2.6
Coste	Chris R	PHI	243	0.9	1.4	0.6	0.3	3.2	0.3	2.5
Iannetta	Chris D	COL	497	-0.5	2.2	0.8	0.5	3.0	0.1	2.3
Flores	Jesus M	WAS	395	1.7	-0.4	0.0	0.4	1.8	2.0	1.8
Torrealba	Yorvit	COL	935	-1.9	3.5	-1.0	1.0	1.7	2.1	1.8
Molina	Jose	NYA	169	0.7	0.3	0.4	0.2	1.6	1.1	1.5
Hill	Koyie	CHN	232	0.1	1.7	0.1	0.3	2.1	-0.8	1.4
Quintero	Humberto	HOU	152	1.3	0.4	-0.6	0.2	1.3	0.6	1.1
Paul	Josh	TB	278	2.6	-1.5	-0.3	0.3	1.2	-0.5	0.8
Lo Duca	Paul	NYN	974	-1.7	4.2	-1.8	1.1	1.8	-2.5	0.7
Bowen	Rob	CHN	76	-0.1	0.9	-0.3	0.1	0.6	0.3	0.6
Barajas	Rod	PHI	303	1.2	-1.1	0.8	0.3	1.2	-2.8	0.2
Alfonzo	Eliezer J	SF	122	0.9	0.9	-0.6	-0.6	0.5	-1.0	0.1
Molina	Jose	LAA	323	0.9	-0.7	-1.1	0.4	-0.5	2.1	0.1
Suzuki	Kurt K	OAK	539	-0.7	-1.5	0.9	0.6	-0.7	2.5	0.1
Nieves	Wil	NYA	169	0.0	0.9	0.0	-0.6	0.3	-0.6	0.1
Melhuse	Adam	OAK	64	0.2	-0.4	0.2	0.1	0.0	-0.2	0.0
Lieberthal	Mike	LAN	167	0.0	0.8	-0.5	-0.6	-0.3	0.0	-0.2
Hernandez	Ramon	BAL	855	-1.1	0.0	-0.2	-0.6	-1.9	4.9	-0.2
Castillo	Alberto	BAL	92	0.7	-1.2	0.2	0.1	-0.1	-0.8	-0.3
Bennett	Gary	STL	370	-2.3	1.6	0.5	0.4	0.2	-2.1	-0.4
Saltalamacchia	Jarrod S	ATL	187	-0.5	-0.6	0.0	0.2	-0.9	1.1	-0.4
LaRue	Jason	KC	474	2.7	-1.2	-0.2	-1.0	0.3	-2.6	-0.4
Rodriguez	Guillermo S	SF	227	0.3	-0.4	-0.4	-0.5	-1.0	1.1	-0.5
Bowen	Rob	OAK	131	-0.5	-0.4	-0.1	0.1	-0.9	0.6	-0.5
Paulino	Ronny	PIT	1088	-1.2	2.0	1.3	-1.1	1.1	-6.0	-0.6
Buck	John R	KC	924	-1.7	1.4	0.0	-0.5	-0.8	-0.2	-0.6
Molina	Bengie	SF	1104	0.9	-2.8	-0.5	0.5	-2.0	2.5	-0.9
Fasano	Sal	TOR	120	-0.1	-0.3	-1.1	0.1	-1.4	0.0	-1.0
Casanova	Raul	TB	169	0.7	-1.1	-1.0	0.2	-1.2	-1.8	-1.3
Stinnett	Kelly	STL	203	-0.1	0.2	-1.4	0.2	-1.1	-2.2	-1.3
Mirabelli	Doug	BOS	293	0.0	-2.7	0.3	0.3	-2.2	0.5	-1.5
Treanor	Matt A	FLA	441	-2.7	-0.1	-0.3	0.5	-2.7	1.5	-1.7
Montero	Miguel	ARI	511	-0.6	-0.5	-0.1	-0.9	-2.1	-0.5	-1.7
Pierzynski	A.J.	CHA	1058	-2.8	-0.3	1.7	1.2	-0.2	-6.4	-1.7
McCann	Brian	ATL	1139	-1.1	1.8	-0.9	-2.5	-2.8	1.4	-1.8
Saltalamacchia	Jarrod S	TEX	186	-1.7	-0.9	-0.5	0.2	-2.9	1.1	-1.9
Castro	Ramon R	NYN	331	-2.7	1.1	-1.1	0.4	-2.3	-0.9	-2.0
Barrett	Michael	SD	293	-2.6	0.6	0.3	0.3	-1.4	-3.9	-2.0
Kendall	Jason	OAK	714	-2.6	0.2	0.4	0.0	-2.0	-2.6	-2.1
Melhuse	Adam	TEX	123	-0.9	-2.4	0.3	0.1	-2.9	-0.3	-2.2
Napoli	Mike A	LAA	599	-0.4	-1.1	0.6	-2.3	-3.3	0.9	-2.3
Bowen	Rob	SD	208	-2.2	-0.1	0.1	-1.3	-3.5	0.9	-2.4
Navarro	Dioner	TB	956	1.8	0.3	-3.3	-0.5	-1.7	-4.8	-2.4
Posada	Jorge	NYA	1111	0.0	-5.2	1.9	-1.0	-4.4	3.3	-2.5
Mathis	Jeff	LAA	467	-1.5	-2.6	-0.2	-0.2	-4.6	3.8	-2.6
Rabelo	Mike G	DET	395	0.4	-2.3	-0.4	-1.1	-3.4	-0.6	-2.7
Burke	Jamie	SEA	322	-1.6	-2.6	0.8	-0.4	-3.8	-1.0	-3.1
Zaun	Gregg	TOR	838	-4.3	2.9	-1.2	0.2	-2.5	-5.5	-3.2
Munson	Eric	HOU	309	-2.2	-1.3	-0.2	0.3	-3.4	-3.2	-3.3
Valentin	Javier	CIN	472	-3.1	-1.2	0.7	0.5	-3.1	-4.4	-3.4
Bako	Paul	BAL	421	-0.9	-1.7	-0.8	0.5	-3.0	-4.9	-3.4
Hall	Toby	CHA	293	-2.2	-0.7	-0.2	-0.4	-3.6	-4.9	-3.8
Phillips	Jason	TOR	364	-5.1	1.2	-0.5	0.4	-4.0	-6.4	-4.5
Barrett	Michael	CHN	475	-3.4	-0.9	-0.2	-0.2	-4.8	-6.4	-5.1
Kendall	Jason	CHN	432	-6.2	-0.8	-1.3	0.5	-7.8	-1.6	-6.2
Olivo	Miguel	FLA	990	2.7	-7.1	-1.3	-1.9	-7.6	-2.3	-6.3
Estrada	Johnny	MIL	961	-7.0	0.0	0.5	1.0	-5.4	-16.0	-7.9
Bard	Josh	SD	927	-13.0	2.6	1.4	0.3	-8.7	-5.7	-7.9

Photo of Yadier Molina by AP/Rick Bowmer