2015 Hitter BABIP on Pulled Ground Balls, by Handedness

Baseball Savant, a website maintained by Daren Willman, is a thing of beauty. Aside from some great leaderboards and applications, Willman hosts a database of PITCHf/x data. Using it without a game plan is like entering the Amazon without a machete — it can be unwieldy and overwhelming. Navigating just right bears ample fruit, however. I would like to share some of my fruit with you.

Because in 2015, PITCHf/x data began including batted ball velocity for most balls in (and out!) of play. Batting average on balls in play (BABIP) is a critical component to player success, and while there has been plenty of focus on it in the last decade — more so than, say, pitch framing, which is a popular but still-raw area of research — the baseball community would still benefit from a better understanding of BABIP, especially in light of more frequent employment of defensive shifts.

Intuition tells us that a harder-hit ball in play will have a greater probability of resulting in a hit. (Indeed, my expected BABIP equation from last year that helps corroborate such a claim.) Specifically, in regard to ground balls and defensive shifts, a hard-hit grounder will have a much greater chance of clearing a crowded first-base line than would a softly hit grounder.

Enter Baseball Savant and its very granular PITCHf/x data. With play results and batted ball velocities for (almost) every ball in play, we can strip away indecipherable bins — I’m referring to Baseball Info Solutions’ batted ball data, which FanGraphs hosts on the site and is categorized simply as “Soft%,” “Med%” and Hard%” — and uncover the marginal gains in an additional mile per hour (mph) of velocity, if such gains do, indeed, exist.

Thank you, Baseball Savant, for helping validate intuition.

The blue line represents batting average (equivalent to BABIP, in this case) for left-handed hitters on pulled ground balls, aka hit to the right side of the field (as determined by setting batted ball angle to split second base and first base down the middle). The orange line represents right-handed batting average pulled ground balls, aka hit to the left side of the field. The grey line indicates the total number of balls in play hit at that particular velocity, measured along the secondary Y-axis. The higher the count, the more reliable the batting average measurement.

Except reliability doesn’t really seem to be an issue; right-handed batting average on pulled grounders is higher at practically every batted ball velocity. Two causes likely immediately came to your mind:

(1) Second basemen have much shorter throws to first base than do shortstops and third basemen, and first basemen oftentimes do not have a throw to make whatsoever. Maybe it goes without saying, but I’ll say it anyway: those shorter throws allow for more time to get set and fire, and the shorter distance allows for greater ease of throwing a strike. Shortstops and third basemen don’t quite have that luxury.

(2) And, of course, the shift. With defensive shifts on the rise, an additional infielder shaded to the right of second base versus pull-happy lefties will convert more balls in play into outs, teams hope. Even without concrete shift data by specific hitter or batted ball velocity, we can see its effects play out in the data.

The disparity in batting average by handedness due to these causes was staggering: righties hit .303 on pulled ground balls in 2015, whereas lefties hit a meager .232. The frequency at which certain velocities were achieved likely differ by handedness, but given lefties outpaced righties in average batted ball velocity on pulled ground balls by more than a mile per hour, a more thorough investigation in this regard likely will not help their case.

So, that’s kind of it for now. The next step would be to calculate expected batting averages on strictly pulled ground balls for each hitter using this information. I envision an xBABIP calculated by tallying up each hitter’s batted balls by velocity and weighting them by their expected batting averages. Then those tallies can be inferred upon the hitter’s overall 2015 BABIP.

I envision it because I did it already. I did. For you. But it’s coming tomorrow. Because I’m writing this on an airplane and sometimes you’ll do anything for the man or woman or website you love. Except for making embeddable Excel tables. Sometimes, that has to wait.