Last winter, we took a gander at the MLB hitters with the biggest difference between their respective batting average on balls in play (BABIP) and expected BABIP (xBABIP) totals. Today, let’s update those lists for 2010.
To get the full methodology, here’s a link back to last year’s article. xBABIP, developed by Peter Bendix and Chris Dutton, estimates a hitter’s BABIP based on components such as batted ball distribution, speed and power. For the purposes of calculating xBABIP, I’m again using a formula developed by Slash 12 of Beyond the Box score based on the work of Bendix and Dutton. The model uses the following to find a hitter’s xBABIP:
– Line Drive Percentage (LD%)
– Ground Ball Percentage (GB%)
– Fly Ball Percentage (FB%)
– Infield/Fly Ball Percentage (IFFB%)
– Home Run/Fly Ball Percentage (HR/FB%)
– Infield Hit Percentage (IFH%)
Hitters with high LD%, HR/FB% and INFH% totals tend to have higher BABIPs than those who don’t. Grounders have a higher BABIP than fly balls, and infield flies are BABIP killers.
From last year’s post, a disclaimer:
These lists of “lucky” and “unlucky” hitters are based on just one year of data. To get a better feel for how a hitter will perform in the future, it’s vital to take a good hard look at multiple seasons worth of performance. This is just a quick-and-dirty exercise.
In this case, the numbers are based on even less data — I used a 200 plate appearance cut-off. These lists basically tell you, “who has underperformed or over performed based on their batted ball inputs so far in 2010?” A player might have certain line drive, infield fly and HR/FB percentages to this point, but that does not mean those numbers will persist in the months to come. To provide more context, I have included the rest-of-season ZiPS BABIP for the players with the biggest BABIP/xBABIP splits.
Without further ado, here are the 25 hitters with the biggest negative BABIP/xBABIP splits. These guys have BABIP totals that are significantly lower than their xBABIPs:
And here’s how these guys rate in terms of the difference between their actual BABIP and their projected rest-of-season ZiPS BABIP. This is a more pure measure of who has been “unlucky,” as ZiPS incorporates multiple years of data and regresses it:
(Note: Smoak’s ZiPS BABIP is actually his pre-season projection; he doesn’t seem to have a rest-of-season projection)
Who has gotten some favorable bounces so far? Here’s the list of players with significantly higher BABIP totals than their xBABIPs suggest:
And finally, here’s how they rank in terms of the difference between actual BABIP and projected rest-of-season ZiPS BABIP. This is a good measure of who has truly been “lucky”:
(Note: Like Smoak, Boesch’s ZiPS BABIP is his pre-season projection.)
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