##
BABIP as a Defensive Metric *October 11, 2014*

*Posted by tomflesher in Baseball, Economics.*

Tags: BABIP, BJ Upton, models, statistics

trackback

Tags: BABIP, BJ Upton, models, statistics

trackback

I follow OOTP on Facebook, and this Reddit thread about editing the Braves to go 0-162 popped up the other day.

I went into commissioner mode and basically ranked everyone’s stats to go 0-550 with 550 Ks (although when I went back, OOTP changed it to give them all a few hits and a couple of walks, etc.) I did not have to edit

BJ Upton, as he was already programmed to do so.

One reply asked whether 1-BABIP is a valid defensive metric, and that got the wheels turning. (Note that for statistical purposes, summary statistics for 1-BABIP will be the same magnitude and the opposite sign as statistics for BABIP, so I went ahead and just used BABIP.)

For a quick check, I checked in at Baseball Reference to get the National League’s team-level statistics for the last 5 years, then correlated BABIP to runs allowed by the team. That correlation is .741 – that’s a pretty strong correlation. Similarly, the correlation between BABIP and team wins was about -.549. It’s a weaker and negative correlation, which is expected – negative because an added point of opposing team BABIP would mean more balls in play were falling in as hits, and weaker because it ignores the team’s offensive production entirely.

If BABIP accurately describes a team’s defensive power, then a statistical model that models team runs allowed as a function of fielding-independent pitching and pitching-independent fielding should explain a large proportion, but not all, of the runs allowed by a team, and thereby explain a significant but smaller proportion of the team’s wins.

I crunched two models to test this, each with the same functional form: Dependent Variable = a + b*FIP + c*BABIP. With Runs as the dependent variable, the R^{2} of the model was .8625; with Wins as the dependent variable, the R^{2} was .5246. Since R^{2} roughly describes the percent of variation explained by the model, this makes a lot of sense. In the Runs model, about 14% of runs come due to something other than home runs, walks, or hits, such as baserunning and errors; in the Wins model, about 47% of team wins are explained by something other than defense and pitching. (Say…. offense? That’s crazy.) In both models, the coefficients are statistically significant at the 99% level.

BABIP’s coefficient in the Runs model is 3444.44, which means that a batting average on balls in play of 1.000 would lead to about 3444 runs scored over a season; more realistically, if BABIP increases by .01, that would translate to about 34 runs per season. Its coefficient in the Wins model is -328.757, meaning that an increase of .01 in BABIP corresponds to about 3.29 extra losses. That’s surprisingly close to the 10 runs-1 win ratio that Bill James uses as a rule of thumb.

Since the correlations were strong, this bears a closer look at game-level rather than simply team-level data.

## Comments»

No comments yet — be the first.