2010 BABIP-xBABIP Splits So Far

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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A recent graduate of Duquesne University, David Golebiewski is a contributing writer for Fangraphs, The Pittsburgh Sports Report and Baseball Analytics. His work for Inside Edge Scouting Services has appeared on ESPN.com and Yahoo.com, and he was a fantasy baseball columnist for Rotoworld from 2009-2010. He recently contributed an article on Mike Stanton's slugging to The Hardball Times Annual 2012. Contact David at david.golebiewski@gmail.com and check out his work at Journalist For Hire.

13 Responses to “2010 BABIP-xBABIP Splits So Far”

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  1. Stephen R. says:

    Maybe it’s just me, but something doesn’t look right here. Two weeks ago you had Granderson’s xBABIP at .310. Now he’s at .349. Is this simply a matter of using a different calculator?

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  2. Stephen R. says:

    The biggest difference is, obviously, that you’re using Beyond the Box Score’s calculator and not THT’s. 0.310 (or whatever it is now) v. 0.349 is a giant difference; is there any reason you used THTs two weeks ago and are using BtB’s now?

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  3. Jason says:

    Good article, but your table is too hard to scan with some names taking up two lines…

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  4. David Golebiewski says:


    Yeah, it’s a different calculator this time.

    Granderson’s LD%, which was 23.4% at the time of that article, is now 25.3%. Putting his current numbers into the THT calculator I used for the other article, I get a .312 xBABIP. It is still a large difference.

    I used a different calculator this time because the THT calculator is not easily accessible for a large number of players — you have to punch in numbers for each individual player, and it’d take hours to do.

    Here are the two formulas:


    xBABIP =0.391597252 + (LD% x 0.287709436 ) + ((GB% – (GB% * IFH%) ) x -0.151969035 ) + ((FB% – (FB% x HR/FB%) – (FB% x IFFB%)) x -0.187532776) + ((IFFB% * FB%) x -0.834512464) + ((IFH% * GB%) x 0.4997192 )


    xBABIP = ((HR/FB)*(0.0261231)+(IFFB/FB)*(-0.0995367)+(LD%)*(0.0847392)+(FB/GB)*(-0.0317976)+(SB)*(0.0005908)+((AB-K)/AB)*(-0.0701565)+(Team)+0.3942664),”-“,((HR/FB)*(0.0261231)+(IF/FB/FB)*(-0.0995367)+(LD%)*(0.0847392)+(FB/GB)*(-0.0317976)+(SB)*(0.0005908)+((AB-K)/AB)*(-0.0701565)+(Team)+0.3942664))

    It seems as though line drive rate is weighted more heavily in the BTB calculator.

    Taking Granderson, as an example — for the THT calculator, a one percentage point drop in LD% does not change Granderson’s xBABIP (it takes a two percentage point drop to lower his xBABIP by one point). With the BTB calculator, a one percentage point drop in LD% lowers Granderson’s xBABIP by TWO points.

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  5. Andrew says:

    This is excellent work. Really appreciate it.

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  6. Drew says:

    So how might this help one when it comes to fantasy baseball?

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  7. David Golebiewski says:


    By giving an idea of who is likely to improve and decline in terms of hits on balls put in play.

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  8. James M. says:

    How difficult would it be to go back to last year’s midseason stats and do the same calculation, then compare the results to the actual 2nd half numbers? It would be nice to know how often this method produces a significantly better forecast than ZiPS.

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  9. Erik F says:

    Best post of the year, thanks so much for posting this. I have Quentin and Lind and felt their batting averages would definitely go up, but this gives me even more hope. Smoak could be a good sleeper for next year

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  10. Erik F says:

    Oh and could you maybe do this for pitchers? I’m curious because it seems certain pitchers such as Tim Hudson are good at having a low line-drive rate, which I would imagine mean a lower xBABIP.

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  11. ralph says:

    Where’s Adrian Beltre? He’s currently carrying a .362 BABIP, and as best as I can tell, he has something like a .320 xBABIP using either BTB or THT’s formula, so it seems like he should easily make the overachiever list.

    There’s a small chance I’m doing something wrong since I’m getting a different xBABIP number than you for Brennan Boesch no matter whether I use the BTB method (I get a .276 xBABIP) or the THT method (I get a .303 xBABIP), while you’re reporting a .318 xBABIP for Boesch. Boesch has been slumping recently, so I’m wondering if his xBABIP could have fallen that much since when you did your calculations and I did mine.

    …and I decided to check on Freese, too, since he’s been injured and we should be working from the same dataset… BTB gives me a.337 xBABIP, THT gives me a .336 xBABIP, and you’ve got a .334 xBABIP. It still seems like one of my numbers should match yours, but it does look like Boesch’s xBABIP really might have fallen that far since you did calculations.

    But either way, I do feel pretty confident that Beltre should included here.

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  12. Erik F says:

    Oh one more question. Where can you get spray ratings to use in the calculations?

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  13. Andrew says:

    An end-of-the-season update would be much appreciated if you ever have the time…

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