Baseball Player Won-Loss Records
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Fielding Player Won-Lost Records vs. Ultimate Zone Rating



Perhaps the most prominent modern fielding measure is Ultimate Zone Rating, which was originally conceived by Mitchel Lichtman and is now reported regularly at Fangraphs.com. For this article, I compared my Fielding won-lost records to UZR data for ten years, 2003 – 2012, for all players who played at least 3,000 innings at a given position over those 10 years, for the seven fielding positions other than pitcher and catcher. The total population of players that I compared here was 288 total players (counting some players more than once if they played 3,000 innings at multiple positions), ranging from 30 leftfielders to 49 centerfielders.

I chose 2003 as my cutoff for comparison (Fangraphs reports UZR data starting in 2002) because Retrosheet data since 2003 is very consistent in providing hit-type information (e.g., ground ball, fly ball, line drive), but not detailed location data, for all balls-in-play, hits as well as outs. I stopped in 2012 because that was the last season for which I had data when I first did this comparison (which I have since updated based on my most recently-calculated Player won-lost records). Overall, 2003 - 2012 makes for a nice even ten-year sample period.

Conceptual Difference between UZR and Fielding Player Won-Lost Records
     Basic Fielding: Outs vs. Hits on Balls-in-Play
UZR is described in great detail on Fangraphs' website here. The basic concept is that UZR calculates a probability of a ball-in-play being converted into an out, based on the location of the ball, how hard it's hit, the handedness of the batter, the ground-ball tendencies of the pitcher, and various other things. Fielders are then given credit or blame over and above this said probability, so, for example, for a ball-in-play that had a 75% chance of being an out, if the ball becomes a hit, the responsible fielder(s) are debited with -0.75 plays not made (0 - 0.75); if the same ball-in-play became an out, the responsible fielder is credited with 0.25 plays made (1 - 0.75). Plays are then converted to runs based on the average run value of balls-in-play based on the location, et al.

The calculation of my fielding Player won-lost records is described in some detail elsewhere. This aspect of fielding: whether balls-in-play are converted into outs or not corresponds to what I call Component 5 out of the nine components of my Player won-lost records.

The key difference between Player won-lost records and UZR is that while UZR's baseline for evaluating a play is detailed information about the location of a ball-in-play (as well as how hard it was hit, by whom, and against whom), the baseline for evaluating a play in calculating Player won-lost records is what the final result of the play was - out vs. hit, who fielded it, and what type of hit it was (bunt, ground ball, fly ball, line drive). This is largely because of data limitations with respect to Retrosheet play-by-play data. I discuss this difference and my treatment of location data in general in a separate article. In effect, UZR assumes that two hard-hit fly balls to medium center field are created equal. Player won-lost records assume that two fly-ball doubles fielded by the center fielder are created equal.

     Hits vs. Fielding Errors
UZR treats errors somewhat differently from hits. According to the UZR Primer at Fangraphs, errors are assumed to have been easy plays with high probabilities of being outs. Hence, UZR penalizes fielders more heavily for errors than for hits on balls-in-play.

In contrast, Fielding Player won-lost records treat errors the same as base hits. The key distinguishing characteristic of plays in my system is whether the batter reaches base (and, eventually, what base he reaches).

Michael Humphreys discusses the correct treatment of errors in evaluating fielding in his book Wizardry: Baseball’s All-Time Greatest Fielders Revealed and shows that the true cost to the fielding team of an error or a hit allowed are identical for any given play (see, e.g., Wizardry, pp. 77-78). I agree with Humphreys and believe that this is one way in which my Player won-lost records are clearly superior conceptually to UZR.

     Additional Components of Fielding
In addition to the basic "range runs" and "error runs" described above, UZR also calculates run values for fielders based on their ability to turn double plays (infielders) and their ability to control baserunner advancement (outfielders). Fangraphs reports these values separate from the UZR estimates based purely on whether balls-in-play are converted to outs or not, but combines them into a single final number which it reports as a player's total UZR.

In addition to Component 5, I also calculate four additional componentsthat are credited (at least partly) to fielders.

Component 6 gives credit or blame on hits-in-play based on how many bases the batter takes. That is, it distinguishes between singles, doubles, and triples among hits-in-play. To the best of my knowledge, there is no parallel to my Component 6 in UZR (or any other fielding metric of which I am aware). UZR uses run values based on the average hit value of a ball, based on its location, hit type, etc., but makes no distinctions between hits which actually end up as singles versus otherwise-identical balls-in-play which actually end up as doubles. Component 6 Player won-lost records are shared between pitchers and fielders at all fielding positions. Component 6 won-lost records are much more significant, however, for outfielders (for whom they account for approximately 14.8% of Fielding decisions) than for infielders (for whom they account for approximately 0.4% of Fielding decisions).

Component 7 gives credit or blame to infielders (and pitchers) for turning double plays on ground balls in double-play situations. This is essentially comparable to UZR's double-play runs. For Player won-lost records, Component 7 fielding decisions are shared between the fielder who fields a ground ball and the pivot man on the double play (pivot men only receive fielding losses for plays where they receive the ball in time to record a force out but are unable to complete the double play). So, for example, on a classic 6-4-3 double play, both the shortstop and second baseman will earn Component 7 fielding wins. It was not clear to me in reading the UZR Primer exactly who is credited with double-play runs on a 6-4-3 double play. Component 7 accounts for approximately 10.5% of infielder Fielding decisions.

Finally, Components 8 and 9 give credit or blame to fielders for baserunner outs and baserunner advancement, respectively. This is comparable to UZR's Arm runs. The difference here is that Components 8 and 9 are allocated across all fielders, while UZR Arm runs are only allocated to outfielders. Components 8 and 9 combine to account for approximately 13.2% of infielder Fielding decisions and 32.9% of outfielder Fielding decisions.

Components 5, 6, and 7 are shared between fielders and pitchers, while Components 8 and 9 are allocated entirely to fielders. Because of this, "arm ratings" make up a relatively larger share of Fielding Player won-lost records than they do of total UZR.

Raw Results
For this article, I compare two measures of UZR and (context-neutral, teammate-adjusted) Net Fielding wins (eWins minus eLosses): total UZR runs vs. total Net Fielding wins, and (Range + Error) UZR runs vs. net Component 5 Fielding wins. For outfielders, I also calculate a third measure of Net Fielding wins which excludes Component 6 - since UZR has no counterpart - which I compare to total UZR runs. To be clear, on this last measure, I exclude Component 6 only to allow for an apples-to-apples comparison to UZR. The fact that my Fielding records include this measure of the exact value of the hits allowed by fielders while UZR relies only on average hit values across similar plays is, in my opinion, a clear advantage of Fielding Player won-lost records over UZR as an overall measure of player fielding.

The first table summarizes the results for Ultimate Zone Rating (UZR) and net Fielding Wins (eWins minus eLosses).

UZR Net Fielding Runs (per 1,000 innings) Net Fielding eWins (per 1,000 innings)
Total UZR
UZR, Range+Error
Total Fielding eWins
Component 5 eWins
Total eWins, excl. Comp. 6
Position No. of Players Mean
Std. Dev.
Mean
Std. Dev.
Mean
Std. Dev.
Mean
Std. Dev.
Mean Std. Dev.
1B
36
-0.12
4.31
-0.09
4.24
0.008
0.209
0.013
0.181
2B
41
0.12
4.66
-0.05
4.46
0.001
0.279
-0.014
0.254
3B
49
0.80
6.24
0.77
6.08
0.028
0.341
0.018
0.332
SS
45
0.54
4.90
0.43
4.61
0.044
0.261
0.041
0.197
LF
30
-0.46
6.82
-0.21
6.36
-0.002
0.340
-0.002
0.280
-0.0070.307
CF
49
0.52
6.29
0.27
6.25
0.038
0.373
0.015
0.262
0.0320.357
RF
38
0.53
6.38
0.23
5.78
0.049
0.405
0.018
0.298
0.0520.393


The first thing that we have to do before we can compare UZR to Player wins is to put them on the same scale. UZR is expressed in runs while Player wins are, of course, expressed in wins. Traditionally, in sabermetric measures, one win is equal to approximately 10 runs. Looking at the standard deviations in the above table, however, the ratio of UZR to Player wins is more like 15-20. In other words, even if you converted UZR to wins, using a conventional run-to-win translation, the spread of players' net fielding wins is 40-50% lower than the spread of UZR.

Why is the Spread on Player Fielding Wins lower than Defensive Runs?
I believe that the spread on my net fielding wins is less than the spread of UZR (and other fielding measures) because I assign more credit on balls-in-play to pitchers, whereas stand-alone fielding measures implicitly assign all of the credit on balls-in-play to fielders, since that's all that they are measuring.

Specifically, looking at my Components 4 (excluding home runs), 5, 6, 7, 8, and 9, I assign 46.4% of the (defensive) credit for these to pitchers and only 53.6% of the (defensive) credit to fielders (since 2003).

Is this reasonable on my part?

I believe that it is. Econometric research following up on DIPS theory has consistently found that pitchers have some effect on batting average on balls-in-play (BABIP). The extent to which I allocate such credit to pitchers is based on the extent to which Player winning percentages persist for pitchers in these components.

With specific regard to UZR, Mitchel Lichtman, the creator of UZR, looked at how UZR differs for specific pitchers on the same team (specifically, the 2012 Detroit Tigers) and found significant differences across pitchers. While this was a quick-and-dirty analysis that really doesn't even rise to the level of a "study", its results are consistent with the likelihood that there is some pitcher "ability" being captured within UZR.

More recently, Lichtman wrote an interesting blog post, “How Important is Bayes in Advanced Defensive Metrics?”. In discussing the a priori probabilities of a ball-in-play being converted to an out that form the basis for calculating UZR, Lichtman identifies one factor affecting these probabilities that UZR does not include: “whether the player caught the ball or not!” Working through a thought experiment of what UZR might look like taking into account “whether the player caught the ball or not”, Lichtman hypothesizes that UZR could be “regressed around 35-40% toward zero.” In other words, Lichtman suggests that the true range of player fielding value may be 40% more narrow than UZR - very close to what my Player won-lost records suggest.

Putting Things on the Same Scale
In order to really compare UZR and what I'll start calling NFW (net fielding wins), it is necessary to put them on the same scale. To do this, I created "z-scores" associated with both statistics. The basic formula for a z-score of variable x is (x - m) / s, where m is the mean of the statistic and s is the standard deviation. I calculated z-scores for each player for UZR and net fielding wins using a value of m equal to zero (since both of these statistics are constructed to be relative to league average by construction) and the standard deviations from the above table.

For example, Andre Ethier scores at -4.67 total UZR (per 1,000 innings in RF), -2.84 Range+Error UZR (reUZR), -0.422 total NFW, -0.248 Component 5 NFW (NFW5), and -0.515 NFW, excluding Component 6 (NFW589) in right field over the time period being analyzed here. From the previous table, the standard deviations associated with these five numbers are 6.38, 5.78, 0.405, 0.298, and 0.393, respectively. This translates, therefore, into z-scores for Andre Ethier in right field of -0.73 for total UZR, -0.49 for reUZR, -1.04 for NFW, -0.83 for NFW5, and -1.31 for NFW589.

I did this for every player referenced in the earlier table. I then calculated simple correlations between UZR and NFW by position (UZR to NFW, reUZR to NFW5, and, for the outfield positions, UZR to NFW589).

UZR v. NFW
Position Total Comp. 5 only excl. Comp. 6
1B 0.864 0.886
2B 0.812 0.826
3B 0.869 0.873
SS 0.778 0.793
LF 0.730 0.721 0.715
CF 0.558 0.494 0.519
RF 0.724 0.698 0.729


Keep in mind that correlations do not tell us which of two measures is more accurate, merely how similar they are to each other.

The correlations associated with the infield positions here are exceptionally high. To the extent that the correlations are somewhat higher for Component 5 only, I believe this is indicative of the extent to which UZR is missing information that I am capturing, particularly via Components 6, 8, and 9. But, to the extent that the difference in correlations is very slight, this is indicative of the fact that this additional information is fairly minimal (and/or that player fielding value is fairly highly correlated across components).

The correlations associated with the corner outfield positions, while not as high as those for the infield, are nevertheless extremely high. The relative correlations with and without Components 6, 8, and 9 differ between leftfield and rightfield such that I'm not inclined to really draw any conclusions in that regard. But overall, the level of correlation between these two systems is very encouraging to me. If UZR is capturing something that I am missing, it does not appear to be a very major factor in the infield or the corner outfield positions.

The lowest correlations are for centerfield. Even here, however, the correlation between overall UZR and total Net Fielding wins, 0.558, is fairly high. I look more closely at the centerfield numbers and what they might mean below.

The next section looks more closely at how UZR and Net Fielding Wins compare on a position-by-position basis.

Position-by-Position Analysis
     First Base
For the 36 players evaluated here, the average difference in z-scores between UZR and NFW (NFW minus UZR) is 0.064. The average absolute difference in z-scores between UZR and NFW is 0.451. For Component 5 only, the average difference is 0.094 and the average absolute difference is 0.395.

There are two players for whom the difference in z-scores is greater than one (in absolute value). Looking only at Component 5, there is only one player for whom the (absolute) difference in z-scores is greater than one. These players are shown in the next table.

Fielding Z-Score
UZR (Fangraphs) NFW (Thress)
Player Innings Total Range+Err Total Comp. 5
Mike Jacobs
3,236.1
-2.099
-2.083
-0.890 -1.295
Scott Hatteberg
4,777.0
-0.413
-0.400
0.607 0.800


The only first baseman whose Component 5 z-scores differ by more than one was minor Moneyball star Scott "Picking Machine" Hatteberg. My system thinks that Ron Washington did a pretty good job of teaching Hatteberg how to play first base.

     Second Base
For the 41 players evaluated here, the average difference in z-scores between UZR and NFW (NFW minus UZR) is -0.023. The average absolute difference in z-scores between UZR and NFW is 0.480. For Component 5 only, the corresponding numbers are -0.045 and 0.469, respectively.

There are a total of three players for whom the difference in z-scores is greater than one (in absolute value) for total UZR v. total Net Fielding Wins. For Component 5 vs. Range+Error UZR runs, there are four such players. These players are shown in the next table.

Fielding Z-Score
UZR (Fangraphs) NFW (Thress)
Player Innings Total Range+Err Total Comp. 5
Brandon Phillips
10,043.1
1.313
1.432
-0.389 -0.186
Brian Roberts
9,607.2
0.532
0.607
-0.600 -0.346
Freddy Sanchez
5,413.0
0.666
0.568
-0.146 -0.458
Rickie Weeks
7,703.2
-1.093
-1.055
-2.196 -2.260
Skip Schumaker
3,182.1
-1.997
-2.215
-0.725 -1.261


The difference in z-scores for Roberts and Schumaker exceed one for total fielding, but are within one (albeit not by a lot) when only Component 5 is considered. On the other hand, the overall z-scores are within one for Freddy Sanchez but UZR and Player won-lost records disagree more strongly about Sanchez's basic ability to turn batted balls into outs.

     Third Base
For the 49 players evaluated here, the average difference in z-scores between UZR and NFW (NFW minus UZR) is -0.047. The average absolute difference in z-scores between UZR and NFW is 0.409. Looking only at Component 5, the differences are -0.071 and 0.422, respectively.

There are a total of 4 players for whom the difference in z-scores is greater than one (in absolute value). Looking at only Component 5, however, there is only one player with z-scores that differ by more than one. These players are shown in the next table.



Fielding Z-Score
UZR (Fangraphs) NFW (Thress)
Player Innings Total Range+Err Total Comp. 5
David Bell
4,456.0
0.960
0.938
-0.171 0.018
Eric Chavez
6,700.1
0.624
0.633
1.645 1.499
Geoff Blum
3,256.0
1.157
1.223
0.144 0.333
Mike Lowell
8,048.0
0.163
0.121
1.337 0.953
Vinny Castilla
4,308.1
0.275
0.206
1.053 1.248


Two of the five players in the above table won Gold Gloves during the decade of interest here: Eric Chavez (who won 4) and Mike Lowell (who won 1). In both of these cases, my system views their defense more favorably – and, hence, more consistenly with Gold Glove voters – than does UZR. Of course, aligning oneself with Gold Glove voters can sometimes be a sketchy proposition historically.

     Shortstop
For the 45 players evaluated here, the average difference in z-scores between UZR and NFW (NFW minus UZR) is 0.057. The average absolute difference in z-scores between UZR and NFW is 0.590. For Component 5, the corresponding numbers are 0.112 and 0.540.

There are a total of five players for whom the difference in z-scores is greater than one (in absolute value) for total UZR and six players for whom the (absolute) difference exceeds one for (Range+Error) UZR. These players are shown in the next table.



Fielding Z-Score
UZR (Fangraphs) NFW (Thress)
Player Innings Total Range+Err Total Comp. 5
Angel Berroa
5,673.1
-1.163
-1.442
-0.035 -0.769
Asdrubal Cabrera
4,380.0
-1.403
-1.610
-0.129 -0.520
Cliff Pennington
3,971.2
-0.242
-0.568
1.192 0.726
Clint Barmes
4,907.0
1.336
-1.172
1.687 2.242
J.J. Hardy
8,115.0
1.621
1.770
0.505 0.943
Khalil Greene
5,941.2
-0.498
-0.588
0.199 0.687
Marco Scutaro
5,734.2
-0.449
-0.378
0.317 0.776
Michael Young
6,737.1
-1.707
-1.546
-1.078 -0.415
Troy Tulowitzki
6,430.0
0.867
0.658
1.923 1.519


     Left Field
For the 30 players evaluated here, the average difference in z-scores between UZR and NFW (NFW minus UZR) is 0.063. The average absolute difference in z-scores between UZR and NFW is 0.575. For Component 5, the corresponding numbers are 0.025 and 0.580, respectively. For total Player won-lost records, excluding Component 6, the numbers are 0.045 and 0.603.

There are four players for whom the difference in z-scores is greater than one (in absolute value) for total Net Fielding wins and three players when Component 6 is excluded. Looking only at Component 5, there are five players for whom the (absolute) difference in z-scores is greater than one. All of the players for whom z-scores differ by one or more in at least one of these comparisons are shown in the next table.



Fielding Z-Score
UZR (Fangraphs) NFW (Thress)
Player Innings Total Range+Err Total (excl Comp. 6) Comp. 5 only
Alfonso Soriano
7,680.2
1.375
1.060
0.482 0.456 -0.050
Carlos Lee
10,568.1
-0.222
-0.134
-1.431 -1.333 -1.156
Cliff Floyd
3,757.0
-0.402
-0.690
0.607 0.832 0.603
Jay Payton
3,572.2
0.094
-0.026
1.108 0.872 0.959
Moises Alou
4,007.2
1.003
1.235
-1.225 -1.143 -0.859
Randy Winn
3,084.0
0.528
0.713
1.419 1.235 1.763


Two names that surprised me when I saw them on the above table were Carlos Lee and Moises Alou, not because my ratings surprised me, but because I thought it was a widely-accepted fact that Lee and Alou were below-average, and likely well below-average, defensive leftfielders.

The next table shows their year-by-year ratings in UZR and Net Fielding wins, expressed as z-scores.

Carlos Lee Moises Alou
Season Innings UZR NFW Innings UZR NFW
2003 1,328.2 0.86
-0.01
1,219.0 1.43 -0.73
2004 1,277.2 1.68
0.81
1,338.1 2.00 -0.93
2005 1,404.0 -0.25
-0.36
576.0 0.61 0.34
2006 1,259.1 -1.69
-2.19
79.0 -3.71 -0.95
2007 1,369.1 -0.32
-1.25
703.0 -0.65 -3.15
2008 915.1 0.02
-3.25
92.1 -0.48 -8.27
2009 1,272.1 -1.08
-2.57
2010 1,096.1 -2.37
-4.18
2011 645.1 1.95
2.03


     Right Field
For the 38 players evaluated here, the average difference in z-scores between UZR and NFW (NFW minus UZR) is 0.038. The average absolute difference in z-scores between UZR and NFW is 0.586. For Component 5, the corresponding numbers are 0.022 and 0.635, respectively. For total Player won-lost records, excluding Component 6, the numbers are 0.048 and 0.587.

There are a total of seven players for whom the difference in z-scores is greater than one (in absolute value) for total fielding, and eight players for whom the difference in z-scores is greater than one (in absolute value) in the other two comparisons (Component 5 and excluding Component 6). All of the players for whom z-scores differ by one or more in at least one of these comparisons are shown in the next table.



Fielding Z-Score
UZR (Fangraphs) NFW (Thress)
Player Innings Total Range+Err Total (excl Comp. 6) Comp. 5 only
Gary Sheffield
3,925
-1.478
-1.566
-0.226 -0.369 0.068
J.D. Drew
7,805.1
0.922
1.393
0.000 -0.082 0.324
Jason Heyward
3,524.1
1.708
2.166
0.185 0.111 0.850
Jeremy Hermida
3,745
-0.360
-0.180
0.950 0.509 0.466
Jose Bautista
3,450.2
-0.354
-1.280
1.041 1.169 -0.685
Kosuke Fukudome
3,273.2
-0.043
-0.138
1.152 1.161 1.240
Mike Cuddyer
6,064
-0.724
-1.222
-1.144 -1.129 -2.458
Randy Winn
3,622.1
1.874
2.027
0.631 0.615 0.948
Trot Nixon
3,924
1.019
1.129
-0.694 -0.646 -0.633
Xavier Nady
3,579.1
-0.473
-0.145
-1.409 -1.476 -1.176


Randy Winn shows up on the lists for both corner outfield spots. Interestingly, UZR rates Winn as an above-average left fielder and an outstanding right fielder, while Player won-lost records rate Winn as an outstanding left fielder but merely an above-average right fielder.

Jose Bautista just misses showing up on two lists. He shows up here in right field; at third base, the difference in z-scores for UZR and Net Fielding Wins was 0.891. In both cases, Bautista scores slightly better in terms of converting balls-in-play to outs in net Fielding wins, but with a difference in z-score of considerably less than one. But in both cases, Bautista scores especially well in the extra components of Fielding Player won-lost records. Specifically, Jose Bautista scores extremely well at preventing baserunner advancement, Component 9, at all of the positions which he has played over the years; in fact, he's in the top 5 in career net Component 9 fielding wins among all players for whom I have calculated Player won-lost records. The next table shows Bautista's career record in Component 9 by position.

Position eWins eLoss Win Pct Net Wins
3B1.81.30.5820.5
LF0.40.20.6070.1
CF1.20.90.5760.3
RF10.37.30.5843.0
Total13.69.70.5843.9


     Center Field
For the 49 players evaluated here, the average difference in z-scores between UZR and NFW (NFW minus UZR) is 0.018. The average absolute difference in z-scores between UZR and NFW is 0.747. For Component 5, the corresponding numbers are 0.015 and 0.774, respectively. For total Player won-lost records, excluding Component 6, the numbers are 0.007 and 0.767.

There are a total of 15 players for whom the difference in z-scores is greater than one (in absolute value) in at least one of the three comparisons made here. This is 30.6% of all of the centerfielders that I evaluated. All of the players for whom z-scores differ by one or more in at least one of these comparisons are shown in the next table.



Fielding Z-Score
UZR (Fangraphs) NFW (Thress)
Player Innings Total Range+Err Total (excl Comp. 6) Comp. 5 only
Adam Jones
6,371.1
-0.487
-1.030
1.110 1.086 -0.272
Andruw Jones
7,327.0
2.223
1.894
0.340 0.250 0.049
B.J. Upton
7,024.2
0.421
0.080
1.420 1.652 1.347
Denard Span
3,712.1
0.617
0.953
-1.638 -1.632 -1.607
Drew Stubbs
4,034.2
0.418
-0.147
0.976 0.754 1.038
Endy Chavez
3,200.0
0.731
0.040
2.167 2.024 1.141
Franklin Gutierrez
3,875.1
1.995
1.925
2.344 2.213 3.078
Jacoby Ellsbury
4,030.2
0.746
1.207
-1.579 -1.683 -1.635
Johnny Damon
5,524.0
-1.287
-0.658
-2.027 -1.992 -1.939
Josh Hamilton
3,148.1
-1.132
-1.073
-0.010 0.068 0.173
Juan Pierre
7,316.1
0.387
1.079
-1.418 -1.494 -0.794
Ken Griffey, Jr.
3,198.0
-3.278
-3.269
-2.139 -1.990 -2.316
Mark Kotsay
5,805.1
-0.225
-0.433
0.733 0.787 0.332
Matt Kemp
6,025.0
-1.170
-1.371
0.176 0.320 -0.037
Michael Bourn
6,280.1
1.307
1.096
0.376 0.221 -0.147
Rick Ankiel
3,063.2
-0.306
-0.789
0.563 0.700 -0.003
Rocco Baldelli
3,332.0
0.086
-0.663
1.020 1.153 -0.350
Scott Podsednik
3,326.1
-0.765
-0.669
0.988 1.184 1.507


Of the players in the above table, the fewest z-score differences greater than one were actually found comparing total UZR to total Net Fielding wins and, interestingly, the most z-score differences greater than one were found when I removed Component 6 decisions from Fielding Player won-lost records. The correlation between the two measures is also strongest (0.558) comparing totals and actually slips just below 50% (0.494) when net Component 5 fielding wins are compared to (Range+Error) UZR.

In other words, Fielding won-lost records correlate most strongly to centerfield UZR when Component 6 is included. It seems to me that my Component 6 - which measures whether a fielder gives up singles, doubles, or triples on hits-in-play - acts as an effective proxy for outfield location data.

Focusing on total UZR vs. total Net Fielding Wins, there are 10 players (20.4%) with z-scores which differ by more than one: Adam Jones, Andruw Jones, Denard Span, Endy Chavez, Jacoby Ellsbury, Josh Hamilton, Juan Pierre, Ken Griffey, Jr., Matt Kemp, and Scott Podsednik.

In the case of Griffey, while his two z-scores differ by 1.14, they basically agree that he was an extremely bad defensive centerfielder over the time period in question here. His UZR z-score is -3.3 versus an NFW z-score of -2.1. In fact, both of these z-scores are the lowest among the 49 centerfielders considered here. Really, UZR and Fielding won-lost records agree more than they disagree about Griffey's late-career fielding.

Let me focus, though, on 10-time Gold Glove winner, Andruw Jones. Jones scores significantly better in UZR than in Net Fielding wins over the sample period considered here. For his career, I actually agree that Andruw Jones was a brilliant defensive centerfielder, among the best of all-time. In fact, he ranks third all-time in career Net Fielding wins among all centerfielders for whom I have calculated Player won-lost records. He rates as the best defensive centerfielder in the National League for five consecutive seasons from 1998 through 2002, in most cases by a lot (he led 2nd-place Terry Jones in net wins 1.8 - 0.5 in 1998). I still think that (Andruw) Jones was pretty good from 2003 - 2006, although not the best in the league anymore, but had fallen to below-average by 2007.

In contrast, the UZR numbers at Fangraphs show Jones as remaining an excellent defensive centerfielder through 2007 (average UZR from 2003 - 2007 of 20.8 runs per season).

Except for one little twist. The UZR numbers at Fangraphs aren't the only UZR numbers for Andruw Jones that Mitchel Lichtman has calculated.

Sensitivity of UZR to Data Source
In a separate article, I explain how I use location data in calculating Fielding won-lost records and defend my decision not to use this data directly even for those seasons where Retrosheet provides location data. In that article, I cite two studies that were reported on the Internet that looked at differences in UZR calculated using different source data.

In August, 2007, Hardball Times published an article by Michael Humphrey (the author of Wizardry), titled Ghosts in the Outfield. In this article, Humphreys reported a comparison of what he called "simplified UZR" ratings using two sets of location data: one from BIS (Baseball Information Systems) and one from STATS. For 2003 - 05, over a sample of 24 outfielders, the two systems - which should have been identical in all respects except for the firm/person recording the location and hit-type data - had a correlation of only 0.60.

Humphreys' sample included 9 centerfielders. The results for these nine players are shown below. Humphreys' numbers are for 2003 - 2005 and are presented as runs saved per 1,450 innings played (~162 games).

Simplified UZR
Player BIS STATS Difference
Andruw Jones +19 -2 21
Carlos Beltran +9 +14 5
Jim Edmonds +8 -2 10
Johnny Damon -6 0 6
Juan Pierre -1 -1 0
Mark Kotsay +1 -19 20
Marquis Grissom -18 -6 12
Mike Cameron +28 +21 7
Vernon Wells -6 +6 12
Correlation 0.522
Std. Deviation 6.87
Median 10


So, Humphreys is showing a correlation of UZR with itself of 0.60 for all outfielders and 0.52 for centerfielders. Suddenly, my correlations of net Fielding wins to UZR of 0.66 for all outfielders and 0.56 for centerfielders look pretty good, don't they?

In a more detailed analysis along the same lines, Mitchel Lichtman, the creator of UZR, calculated UZR data for 2003 - 2008 using data from BIS (bUZR) and again, using the same UZR system, using data from STATS (sUZR) for 240 players. The results were discussed by Licthman, Tom Tango, and others here. That discussion did not include a correlation between the two, but it was noted that "5 of the top 9" differences in players were for centerfielders. The top two differences were Andruw Jones, +112 runs using BIS data vs. -5 using STATS data, and Carlos Beltran, +9 using BIS data vs. +86 using STATS.

Using the UZR standard deviation (per 1,000 innings) for centerfielders reported above (6.29) and Jones's and Beltran's innings played from 2003 - 2008, the two numbers quoted in the previous paragraph for Jones translate into z-scores of 2.45 (BIS) and -0.11 (STATS), a difference of 2.56. For Beltran, the two z-scores are 0.19 vs. 1.78, a difference of 1.59.

Of the 288 players that I looked at for this article, the difference in z-scores between UZR and Net Fielding wins exceeded 1.60 in only eight cases (2.8%): 5 CFs, 1 LF, 1 RF, and 1 2B. The difference in z-scores between UZR and NFW exceeded 2 in only three cases: Moises Alou (LF), Jacoby Ellsbury (CF), and Denard Span (CF). And there were zero players for which the difference in z-scores exceeded the difference in z-scores for Andruw Jones with BIS vs. STATS UZR.

Tom Tango reported that the standard deviation of the difference between bUZR and sUZR in Lichtman's study was 6.0 runs per 150 games with a median difference of 4.0 runs per 150 games and 10% of players having a difference of at least 10 runs per 150 games. I converted my Net Fielding wins into a UZR-level number (by multiplying my NFW z-scores times the UZR standard deviations by position) and calculated differences between UZR and this UZR-level Fielding wins number per 150 games (actually, per 1,350 innings). These results, compared to the results reported by Tango are shown in the next table.

UZR vs. Net Fielding wins bUZR vs. sUZR
Std. Deviation of Difference 5.5 6.0
Median Difference 3.5 4.0
Difference > 4 41% 50%
Difference > 10 6% 10%


Or, in words, my results are closer to UZR (using BIS data) than UZR results are to themselves.

Conclusions
Overall, I'm quite pleased with the results here. The total correlation across all seven positions (288 players) investigated here, between UZR and my Fielding won-lost records - expressed in terms of z-scores - was 0.745. The z-scores associated with these two systems differed by more than 1 in 36 cases (12.5%).

For infielders, the results are even better. The correlation between total UZR and Net Fielding wins for infielders was 0.835 for the 171 infielders that I evaluated here. The z-scores associated with these two systems differed by more than 1 in 15 of 171 cases (8.8%).

As close as these two results are, some of the difference between the two systems is because Fielding won-lost records incorporate factors which are not considered in measuring UZR, including fielders' abilities to limit extra-base hits and control baserunner advancement. When only the common factor of simply converting balls-in-play into outs (what I call Component 5) is compared in the two systems, the correlation across all infielders rises to 0.870 and the number of cases where the z-scores differ by more than one falls to 11 (6.4%).

For corner outfielders, the results are not quite as close as for infielders, but are still very similar. The correlation between total UZR and Net Fielding wins for corner outfielders was 0.728 and the z-scores differed by more than 1 in 11 of 68 cases (16.2%).

The results for centerfielders show the lowest correlation, 0.558, with z-score differences greater than one in 10 of 49 (20.4%) cases. My results for centerfielders are closer to UZR including Component 6 - the extent to which hits-in-play are singles, doubles, or triples. This suggests to me that taking explicit account of hits-in-play may serve as a useful proxy for more detailed location data.

The lack of correlation between Net Fielding wins and UZR for centerfielders is not necessarily an indication of a weakness in Net Fielding wins as a measure of fielding ability. In fact, my results vis-a-vis the UZR numbers presented by Fangraphs are comparable to comparisons of UZR calculated using different data sources.

Overall, I believe that my Fielding Player won-lost records stack up extremely well as measures of player fielding with any other fielding measures out there.

This article was updated to incorporate revised data for Player won-lost records on March 30, 2016.



All articles are written so that they pull data directly from the most recent version of the Player won-lost database. Hence, any numbers cited within these articles should automatically incorporate the most recent update to Player won-lost records. In some cases, however, the accompanying text may have been written based on previous versions of Player won-lost records. I apologize if this results in non-sensical text in any cases.

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