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A Pure Measure of Fielding Ability: Predictive Ultimate Zone Rating

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Throughout the pre-sabermetric revolution days of baseball, the statistics that determine fielding ability (namely errors and fielding percentage) had generated much criticism of fielding stats and undeserving gold glove award winners (Derek Jeter et al), and had kept fielding ability a mystery. However, this mystery in part led to the sabermetric revolution in baseball statistics. In the current day and age, with improved measures of performance available publicly, measuring fielding ability is somewhat less of an enigma, but still far from perfect.

One of the most often used fielding metrics in this day and age is UZR or Ultimate Zone Rating (click the link for an excellent FanGraphs explanation). Instead of counting perceived plays and errors, UZR records every batted ball hit to each of the numerous zones on the baseball field at each trajectory and the runs lost/saved as the fielder gets to the ball or falls short. This is found by matching the average result of the play with the Run Expectancy Matrix. Therefore, UZR provides a very accurate measure of how valuable that fielder was in terms of runs saved/lost over the course of the season.

However, there are major problems with UZR. Sample size issues cause large fluctuations from month to month and even year to year. Moreover, it does not provide a stable basis of fielding ability. Even when all players’ impacts are averaged to a constant, UZR/150, averaged to runs saved/lost per 150 defensive games, the metric is very volatile.

The reasons behind this might actually be easier to identify and correct than you might think. Let’s face it: not all fielders get the same amount of balls hit to them in the same place at the same trajectory within the same number of outs or innings. Infielders with a good knuckleballer on the mound and a slap hitter at the plate are going to get more grounders to each zone than infielders whose teams have fly ball pitchers on the mound and face lots of power hitters at the plate.

However, while the actual amounts may fluctuate from pitcher to pitcher and hitter to hitter, many fielders get a decent sample size of each batted ball to each zone over the course of multiple seasons. Even with a staff of fly ball pitchers, infielders will still handle their fair share of ground balls to each zone over the course of a season. So if there was a way to average all the pitchers and hitters together and measure the value and frequency of making a play in each zone based on the entire AL, NL, or MLB* average batted ball chart, then we could create a similar metric that would be more predictive, rather than purely descriptive.

*The purpose of separating the leagues is the discrepancy of hitting ability with the DH in the AL and the increased frequency of bunts (from pitchers) in the NL.

If we take the average percentage of batted balls to each zone with each trajectory for the AL, NL, or MLB and multiply that by the average runs saved/lost for plays made or missed in that zone, we can find a universal batted ball sample from which to apply the fielders’ impact. While this would not be directly proportional to the runs saved/lost for the fielder during that season for that pitching staff and the batters faced, it would be a metric independent of the impact that the pitcher and hitter has on the fielders. It would measure pure fielding ability over multiple seasons in the form of runs saved, but unbiased by the specific ratio of batted balls per zone and trajectory hit to the fielder over the seasons.

Predictive UZR will have sample size issues but when taken over multiple seasons, a starting fielder should get his fair share of batted balls hit to each zone with each trajectory. The percentages for his success rates at each zone and trajectory can then be applied not to the actual ratio of batted balls per zone hit his way (from his team’s pitching staff and hitters faced) but rather the average ratio of batted balls per zone hit in the entire AL, NL, or MLB.

Both UZR and Predictive UZR are very valuable for different things. UZR is a good reflection of the fielder’s direct impact on defense for the season. However, this might not accurately reflect the fielder’s true talent level because of the assortment of batted balls hit his way. Predictive UZR, while not a concrete reflection of the past runs saved, is a more pure measure of fielding ability. It can provide a number that, when compared to UZR, tells which fielder got lucky and which fielder did not, based on his pitching staff and the hitters faced. Another interesting twist the concept of Predictive UZR brings is that it can be based on the average batted ball chart of teams, divisions, and differing pitching staffs in addition to the AL, NL, or MLB. So a fielder’s projected direct impact, or UZR, can be transferred more easily as he moves from team to team, forming the basis of more accurate fielding projections.

Predictive UZR is not by any means a substitute to UZR, but rather complements it and works with it in intriguing ways. It is a concept worth looking into that has the potential to leave fans, media and front office personnel better informed about the game of baseball.

Nik Oza
Georgetown Class of 2016
Follow GSABR on twitter: @GtownSports

Mythbusters: Home Run Derby Edition

If you watched the Home Run Derby on ESPN, you saw Yoenis Cespedes and his raw, yet explosive swing, hit 17 home runs in the first round of the derby. You also saw Chris Davis staying true to his swing and swinging at any pitch that he thought he could handle, hitting the ball where it’s pitched, and even swinging at some pitches that were borderline balls. If there was anyone to be concerned about changing his swing to fit the Derby, it was Davis–the guy who has so much strength that all he needs to do is stay within himself and swing easy to hit a homer. One might worry that Davis would swing too hard or try to pull everything, thus regressing into the “quadruple-A” player as he was once labeled, swinging and missing at a such a rate that he became a liability.

Anyone who has played baseball at a high level knows that a successfully executed sacrifice bunt, or grounder to the right side of the field with a man on second and nobody out, is frequently celebrated as much as a hit. Quality “team baseball” seems to be more effective than a mere amalgamation of flashy superstars that doesn’t mesh (I’m looking at you, 2012 Red Sox or 2013 Blue Jays). The Home Run Derby is kind of counter-intuitive to many MLB managers. Old-schoolers like Mike Scioscia would rather his players did not participate, saying, “I haven’t seen somebody come away from that derby and be a better player for it.”¹ The Home Run Derby turns the team game into an individual competition. Players exhaust themselves and risk tweaking their swings, but has the derby really affected the second-half performance of its participants?

To answer this question I looked at what goes into a player’s stats. There is a lot of luck involved in baseball, so I took a look at the differences in the way players hit the ball before the derby compared to after the derby. Looking at the past five derbies, I calculated the average batted-ball flight for players that were healthy for both halves of the season (38 players, excluding only Rickie Weeks in 2011 and Jose Bautista in 2012).

Pre HR Derby 19.1 40.8 40.1 8.5 .204
Post HR Derby 19.5 41.0 39.2 9.3 .166
Difference <1% <1% <1% <1% .038

The consistency in the way players hit the ball is incredible. Derby participants hit the ball almost the same before and after the derby as a group. The HR to FB ratio drops considerably, and could explain a decrease in batting average and slugging percentage, as well as on-base percentage. It seems that players hit the ball the same way, just with slightly less power. Here are some of their standard stats from the second half:

Pre HR Derby 17.87% 0.302 0.385 0.570 0.956 0.268 0.322
Post HR Derby 19.60% 0.282 0.369 0.499 0.869 0.217 0.316
Difference 1.73% 0.020 0.016 0.071 0.087 0.051 0.006

Isolated Power (ISO) measures a hitter’s power in extra bases per at-bat (2B+3Bx2+HRx3)/AB. The large drop is ISO shows that indeed power does decrease for derby participants in the second half, and the overall line shows that players do perform worse. It’s not merely a function of hitting the ball to the wrong place, as the .oo6 drop in Bating Average of Balls in Play (BABIP) is not really significant. Players strike out a little bit more, but the notion that players change their swings and have trouble hitting the ball the same way after participating in the derby seems misguided when considering the small change in K% along with the consistent batted-ball percentages outlined in the first table.

Data suggests that players do perform worse in the second half of the season after participating in the HR derby, but that their performance isn’t due to a change in their swings. There have, however, been some significant changes in performance for some individuals. Taking a closer look at some of them, the poor performances can be explained without blaming the Home Run Derby.

2008 Total derby HR pre/post AVG SLG OPS ISO BABIP HR/FB
Dan Uggla 6 pre 0.286 0.605 0.978 0.319 0.341 21.30%
 FLA post 0.226 0.396 0.739 0.17 0.295 13.60%

Uggla has a reputation as a streaky player, but he went from an MVP candidate in the first half to a guy who didn’t belong in the starting lineup after the derby. Taking a closer look, however, Uggla began slowing down in late June, and suffered a leg injury that kept him out nearly two weeks just prior to the All-Star Game. He only lasted one round, anyways, so it’s hard to blame the derby for his drop off, although it was certainly a big one.

2008 Total derby HR  pre/post AVG SLG ISO BABIP IFFB% HR/FB
Lance Berkman 14 pre 0.347 0.653 0.305 0.37 2.80% 20.60%
HOU post 0.259 0.436 0.177 0.298 13.20% 10.30%

By 2008 Berkman had been a good hitter for many years. His second half was hurt by the amount of pop-ups he hit. a 10.4% increase in infield fly balls mean close to a 10% increase in outs, and his average decrease supports that notion. His increase in pop-ups could have been a result of an uppercut swing that developed in the derby, but his average had dropped 20 points in 16 games prior to the derby, and his career IFFB% is 11.5%, not too far off from his second half percentage. Perhaps the derby hurt Berkman’s swing, but more likely  he was finally coming back down to earth after his torrid start.

2009 Total derby HR pre/post K% AVG SLG ISO BABIP HR/FB
Brandon Inge 0 pre 24.60% 0.268 0.515 0.247 0.304 .22
 DET post 29.10% 0.186 0.281 0.095 0.247 .08

Brandon Inge? Yeah, Brandon Inge was in a Home Run Derby. He only has a career HR/FB ratio of .10, and a career batting average of .233, so his second half was closer to what Inge’s career looked like. Plus Inge didn’t even hit one out of the park, so could ten swings really ruin his season?

2009 Total derby HR pre/post AVG SLG ISO BABIP IFFB% HR/FB
Ryan Howard 15 pre 0.257 0.529 0.272 0.301 1.10% .23
 PHI post 0.305 0.621 0.316 0.352 0.00% .28

Wait a second…? Was Ryan Howard better after participating in the derby? Yes! After the slugger hit 15 big flies in the derby, he went on to hit more homers in less at-bats afterward. With zero infield flies in the second half of the season, his swing was just fine.

2011 Total derby HR pre/post K% AVG SLG ISO BABIP IFFB% HR/FB
Jose Bautista 4 pre 14.40% 0.334 0.702 0.368 0.321 11.50% 27.40%
TOR post 20.40% 0.257 0.477 0.22 0.291 20.50% 15.40%

After a hot start in April and May, Bautista had his worst month of the season in June, before the HR Derby. While Bautista was better overall before the derby, he was better in the two months following the derby than he was before it.

2012 Total derby HR   AVG SLG ISO HR/FB
Prince Fielder 28 pre 0.299 0.505 0.206 16.10%
 DET post 0.331 0.558 0.227 20.00%

Prince puts a lot of power into his swings, and when he hits 28 balls out of the park, he exerts a lot of energy. Prince won the derby in 2012, and continued winning games for the Tigers after the All Star Break. Hitting for a better average, and with an improved HR to FB ratio, Prince shows that the derby can kick start a player’s second half.


Conclusion: The notion that participating in the Home Run Derby leads to a drop off in performance is a myth. Although data suggests that Home Run Derby participants do indeed regress in the second half of the season, the derby is not to blame. As baseball is a game of superstitions, players are aware that the derby can have harmful effects if they aren’t careful. Even Chris Davis was wary, saying, ”I wanted to be conscious of not changing my swing at all… I tried to stay up the middle and let the ball travel and not try to get pull heavy. But it looks a lot easier on TV than it really is. Once you get out there and start swinging and your adrenaline wears off, you realize how tough the Derby really is. It’s exhausting.”² While the derby curse isn’t real, it’s hard to continue chasing a 60-home-run season with a popped blister. Get some treatment on that hand, Chris.



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