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September 23, 2009 Ahead in the CountSlugging on Balls in Play
Two of the first things you learned about sabermetrics were that batting average is not a very good measure of a hitter, and that fielding percentage is not a very good measure of a fielder. Not all hits are created the same, and extra-base hits cause a lot more damage than singles, which is why we know that slugging is better than batting average at approximating hitting ability. We also know that fielding percentage is a poor measure of defensive skill, because it only considers the balls that a fielder can reach, and therefore does not account for range-being able to get to more balls, all else equal, implies that a fielder is better. Being able to record outs on more balls in play is indicative of better team fielding. That is why the most widely used measure of team defense is Defensive Efficiency, which is effectively one minus your opponents' BABIP (including errors). However, there is a fairly obvious disconnect between those two facts: on the one hand, we avoid using batting average to measure a team's offensive ability, but then we encourage the use of batting average to measure a team's fielding ability. However, teams frequently guard the lines to avoid doubles late in a game-at the expense of making singles in the hole more likely-and play their outfielders deep to avoid extra-base hits-at the expense of letting singles drop in more frequently. These can be wise tactics in certain situations, since there are times when an extra-base hit would be particularly costly. Sometimes acquiring fielders who make doubles and triples less likely is wise, even if it means not acquiring fielders who can prevent singles. Teams that prevent hits are not necessarily the teams that prevent extra-base hits-and therefore are not necessarily the teams that prevent runs. For this reason, I am introducing a new statistic we can use to measure team defense: Slugging on Balls in Play. It's measured exactly how you think it should be: it is simply slugging on all at-bats that do not end in home runs, strikeouts, walks, hit by pitches, or sacrifice bunts, but it counts reaching on errors as singles. Here are the list of all teams' 2009 SLGBIP and RBIP (slugging on balls in play, and reaching on balls in play-which I define as batting average including errors), through Saturday's action: SLGBIP RBIP Team Rank SLGBIP Rank RBIP Mariners 1 .354 2 .289 Dodgers 2 .360 1 .285 Reds 3 .371 4 .297 Giants 4 .371 3 .293 Cubs 5 .372 6 .299 Rangers 6 .374 5 .297 Cardinals 7 .379 11 .305 Yankees 8 .379 7 .302 Padres 9 .380 12 .306 Phillies 10 .381 8 .302 Rays 11 .383 9 .303 Tigers 12 .383 10 .304 Twins 13 .386 15 .309 Angels 14 .391 18 .312 White Sox 15 .392 21 .313 Athletics 16 .394 19 .313 Mets 17 .395 17 .311 D'backs 18 .397 20 .313 Rockies 19 .397 16 .311 Brewers 20 .397 13 .307 Indians 21 .400 25 .318 Marlins 22 .402 26 .317 Astros 23 .406 27 .320 Pirates 24 .406 14 .308 Red Sox 25 .406 29 .320 Braves 26 .406 23 .315 Blue Jays 27 .406 24 .318 Royals 28 .406 30 .324 Orioles 29 .410 28 .320 Nationals 30 .411 22 .315 I have also included each teams' rank at preventing slugging on balls in play, and at preventing hits on balls in play. Perhaps with an eye towards the example set by the Rays' worst-to-first shakeup in part through their defensive improvement in 2008, the Mariners are one the latest teams to stress defense as a means to improvement. In particular, they've stressed outfield defense, and after putting together an excellent defensive outfield, they lead the league in SLGBIP. Another interesting example is the Pirates, who are 14th at getting outs on balls in play, not bad, but they are 25th at slugging on balls in play, at .406. As a result, their ERA is a little bit higher than their FIP. Note that I have used FIP, fielding-independent pitching, which is a statistic that approximates what pitchers' ERA should be based on their home-run, walk, and strikeout rates. As has been discussed previously, FIP is not as stable a statistic as xFIP or QERA, but it is what we are looking for here because we're looking for a statistic that accounts for the fact that home runs have been hit, and delivers an expected ERA conditional on the number of home runs, strikeouts, walks, and hit batsmen occurred, assuming average defense and average luck. Much of the difference between ERA and FIP is therefore team defense. A statistic like xFIP or QERA might be better at measuring a pitchers' run prevention, but the difference between realized FIP and ERA will be highly correlated with defensive abilities. Looking back at 2008, we see the following rankings for SLGBIP: SLGBIP RBIP Team Rank SLGBIP Rank BIP Brewers 1 .372 7 .302 Mets 2 .373 6 .302 Rays 3 .373 1 .290 Padres 4 .376 9 .304 Blue Jays 5 .378 3 .296 Red Sox 6 .378 5 .301 Athletics 7 .381 4 .300 Cubs 8 .382 2 .295 Dodgers 9 .383 16 .309 Angels 10 .383 15 .308 Orioles 11 .383 18 .312 Marlins 12 .385 13 .307 Phillies 13 .386 11 .305 Indians 14 .388 22 .314 Twins 15 .389 19 .313 Astros 16 .389 8 .302 Nationals 17 .390 14 .311 Royals 18 .393 17 .310 D'backs 19 .394 21 .314 Cardinals 20 .396 10 .305 Yankees 21 .396 25 .318 White Sox 22 .399 20 .314 Braves 23 .400 12 .306 Giants 24 .401 23 .315 Tigers 25 .403 24 .315 Mariners 26 .405 26 .318 Reds 27 .415 29 .327 Pirates 28 .416 28 .325 Rockies 29 .416 27 .322 Rangers 30 .421 30 .330 Perhaps the most interesting result here is that Milwaukee tops the list, even though they were only seventh in getting outs on balls in play. However, they led the league in the difference between their ERA and their FIP. This was partly due to Mike Cameron, who had a UZR of 11.3 in just 119 games. Surprising nobody, the Rays were also particularly good at defense in 2008; while going from worst to first in Defensive Efficiency, they were also very nearly the best in SLGBIP too, thanks to superb outfield defense. The Orioles are also an interesting team. They were 18th in the league in getting outs on balls in play, but were 11th at keeping their club SLGBIP down, so their ERA and FIP were nearly identical. Something particularly interesting about the O's is that they had such a difference between their infield and outfield defense: their infield defense had a combined UZR of -32.2, but their outfield defense had a combined UZR of +38.9. Nick Markakis, Jay Payton, Adam Jones, and Luke Scott combined to help them prevent a lot of extra-base hits, as the team gave up twelve fewer doubles to center than the league average, and four fewer triples to center than the league average. They also gave up ten fewer doubles to right than league average, and about 1.5 fewer triples than average. Melvin Mora at third base and the handful of players manning shortstop combined to allow a lot of hits on balls in play-but many were singles. Let's have a look at the SLGBIP tables for 2007: SLGBIP RBIP Team Rank SLGBIP Rank BIP Blue Jays 1 .363 1 .294 Padres 2 .375 4 .297 Red Sox 3 .375 3 .296 Cubs 4 .376 2 .295 Nationals 5 .378 6 .301 Braves 6 .381 7 .302 Mets 7 .382 5 .299 Rockies 8 .387 8 .303 Orioles 9 .388 20 .316 Cardinals 10 .389 9 .303 Dodgers 11 .390 19 .315 Athletics 12 .391 11 .308 Indians 13 .392 18 .315 D'backs 14 .393 14 .310 Tigers 15 .393 12 .308 Twins 16 .394 17 .314 Yankees 17 .395 15 .310 Giants 18 .397 10 .307 Rangers 19 .399 22 .318 White Sox 20 .400 23 .319 Phillies 21 .401 16 .313 Astros 22 .402 13 .309 Angels 23 .402 24 .320 Brewers 24 .410 26 .325 Pirates 25 .412 28 .330 Reds 26 .412 25 .325 Royals 27 .414 21 .317 Mariners 28 .415 27 .327 Marlins 29 .418 29 .337 Rays 30 .437 30 .343 Toronto led the league in SLGBIP and Defensive Efficiency in 2007. Interestingly, although they stayed near the top of the league in 2008, they fell quickly towards the bottom in 2009. Looking at 2007-09 together, we can see some patterns emerge. Preventing hits on balls in play seems to be largely related to infield defense, but preventing extra-base hits on balls in play is largely related to outfield defense. The correlation between UZR of infielders and reaching on balls in play was .39, but the correlation between UZR of outfielders and reaching on balls in play was slightly lower at .36. The correlation of UZR of infielders and SLGBIP was .22, however, and the correlation of UZR of outfielders and SLGBIP was .40. It certainly seems that the Mariners' 2009 charge towards improving outfield defense is the way to go. Of course, the correlation between UZR of a team's infielders combined from year-to-year is much stronger than that of outfielders at the team level (.34 versus .10), so it may be tougher to help your team consistently prevent those extra-base hits. But what we have learned here is that looking at slugging on balls in play is important, and is a helpful additional way to look at team defense. As defensive metrics get more and more refined at the individual player level, it remains true that these are often difficult to truly interpret, and separating the effect of different players is still a challenging endeavor. These metrics at the team-wide level remain far more reliable and important to use as a benchmark, and slugging on balls in play is another way to measure it.
Matt Swartz is an author of Baseball Prospectus. 14 comments have been left for this article.
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Do you have any explanation or theories on why the Orioles, who were 9th in SLGBIP in 2007, and 11th in 2008 have fallen all the way to 29th in 2009? Jones has still been the predominant center fielder, and Markakis is still in right. Does the change in left field really make that much of a difference?
To what degree do team numbers stay consistent from year to year? We know, for instance, that measures of "clutch hitting" such as BA with RISP can be generated, but are not considered to measure an "ability" because they fluctuate so much from year to year.
To what degree is this pitching rather than defense? For example, one would assume that a ground ball pitcher would have a lower SLGBIP than a fly ball pitcher.
These are interesting concepts. I look forward to greater refinement.
I'll look into this. I tried to do some tests and it seemed like the correlation of BABIP and SLGBIP were similar, but I'll try and dig up a larger sample to check.