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BABIP and xBABIP differences

BABIP, or Batting Average on Balls In Play, is becoming a common tool in analyzing hitters over short spurts of time.  If a player has a .450 BABIP for a month, most people know that such success cannot be sustained due to luck.  Extreme cases are easy to evaluate, but a player with a .250 or .350 BABIP is not near league average (.290-.300), yet could not be lucky/unlucky.  Using batted ball profiles, one can determine an expected BABIP (xBABIP) to see if the hitter has over or under-performed.

The main components in my xBABIP model are line drives (LD%), ground balls (GB%), popups (IF/FB%), home runs (HR/FB%), strikeouts (K%), and Speed score.  Line drives are the most important factor, since they have such a high BABIP.  More line drives mean a higher BABIP.  Groundballs have a higher BABIP than flyballs, so a higher GB% means a slightly higher BABIP.  Popups are almost automatic outs, so these have a negative correlation with BABIP.  Since flyballs have such a low BABIP, the higher percentage of those that go out of the park lessens the negative effect on BABIP.  A higher K rate increases BABIP a bit, since it’s assumed that a hitter is swinging, and hitting the ball, harder which increases K’s but also helps BABIP.  Speed has a small effect on groundballs.

Here are the Braves hitters and their BABIPs and xBABIPs.

BABIP xBABIP
McCann 0.287 0.267
Freeman 0.339 0.327
Uggla 0.253 0.281
Gonzalez 0.285 0.283
Jones 0.295 0.292
Prado 0.266 0.269
Bourn 0.369 0.364
Heyward 0.260 0.273

As you can see, Uggla was fairly unlucky and Heyward had some bad luck.  McCann was pretty lucky and Freeman was a bit fortunate.  To show how much of an effect BABIP can have on results, Uggla’s .233/.311/.453 turns into a .253/.329/.473 slashline, maybe even better if a couple doubles could be added.  While past luck is good, the most important thing is projecting future batted ball profiles.  It does not make sense to use Bourn’s .364 xBABIP for this year, since it’s unlikely he can match the 26.6% line drive rate, which was 6% higher than his previous career high.

My quick calculator is nowhere near the best possible model, since it does not factor in linked skills.  A fast player should get a bit more of an advantage having a higher GB%.  A hitter with a high HR/FB% should get less of a penalty for hitting more flyballs.  I just wanted to show how much of a factor luck can play in hitting.  These differences are completely unpredictable, but knowing the base skillset makes for the best projection.

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