more BABIP thoughts
kcdc1 recent post on the inherit problems with applying xBABIP to hitters lead me to do some work on if certain hitter types produce different results from batted balls. Obviously as technology improves, individual analysis will be possible, but for now, I still try to group players in well defined profiles. The data I’m using is from 2007 and includes only qualified batters, so this is an analysis of MLB regulars only. Also, home runs and infield flies are properly accounted for this time.
I preformed a number of regressions, but I’ll spare most of the details as too technical for most, but if you are interested in them and/or the data email zeppelindz@gmail.com and I’ll help you out.
Power hitters:
I sorted players by both HR/AB and HR/FB and derived a new xBABIP equation for varying degrees of power and the results were unexpectedly conclusive.
Power hitters generate significantly more hits from ground balls and significantly less from fly balls. There was a jump in line drives as well, but it wasn’t as conclusive.
K Rates:
Sorted by K rate and had pretty much the same result as power hitters.
The conclusion on this, I believe, is fairly straight forward. These guys hit balls hard, causing more grounders to get through for hits (as postulated by kcdc1). However, hard hit fly balls result in fewer hits. This is likely caused by expectations. Opposing defenses know power hitters and play them deep frequently which takes these deep hits away at high rates. Also, just postulating here, power hitters likely hit fewer bloop singles, which classify as fly balls.
Speed:
I created a speed statistic using stolen bases, triple to double ratio, and infield hits to sort players. It’s somewhat arbitrary, but looking at the players used, I think it got a list of speed players for a valid result.
Surprisingly, speed seemed to have little effect. The rate of hits from ground balls actually went down very slightly (probably statistical noise).
Clutch:
I couldn’t resist, Fangraphs had a clutch stat which I sort players on to see its effect.
Well, no changes.
Clutch is independent of hit type it seems (remember, HRs are factored out which might change things).
As for the royals, this changes very little from my first post. The royals are great at having average players. Guillen barely fits in as a power hitter compared to the guys I analysis. Gathright, although very fast, doesn’t use his speed effectively to really be considered in the same category as some of my speedster sample. Bottom line, no result substantially change from, but there would be a few players who cross a threshold from say, slightly lucky to neutral or something like that. After the season is over, I’ll go back through with some of these findings and get a better idea of our roster and luck.
Questions, comments, or any additional player types you want tested?
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Power Hitters
Is the lower hit rates on flyballs for power hitters attributable to subtracting home runs as part of the BABIP equation?
I know previous articles have suggested that power hitters tend to have lower BABIPs partly because 20%+ of their flyballs go for home runs and thus are excluded from the BABIP calculation.
i recomputed it all
i compared a xBABIP model of all 162 qualified batters and a xBABIP model of the top 25 and 50 power hitters, so HRs are not to blame
to be more clear
HRs were taken out of all the computations and stats. I only used them to sort and classify players
great post
Thanks for running those numbers. A lot of interesting stuff here. I found it especially interesting to see that power actually probably helps more for generating hits on ground balls than speed does. I don’t want to bog you down with more calculations, but it might be fun to see if you could come up with a pool of players who make especially weak contact and see if they underperform xBABIP and the like. I’m thinking Gathright and TPJ might fall into this category. I don’t know what parameters you’d use to define the class, but few to no HR’s in the past 2 years might be a start.
Platton splits
sorry if I missed it in either discussion (I read both, but just came back to it today). How should we understand platoon splits in relation to BABIP analyses such as yours? I’m thinking Mike Aviles, whom we all agree has been a bright spot, but also very lucky as far as overall BABIP this year. Maybe his example can be illluminating for other players, as well.
He’s been regressing a bit lately, so his BABIP is down (!) to .361 for the year.
However, his BA/OBP/SLG/OPS and BABIP splits are interesting.
vs. RHP .298/.315 /.438 /.753 .331BABIP
vs. LHP .400/.440 /.700/1.140 .439 BABIP
I realize that the platoon split is nearly universal — that’s fine. But I guess my question is whether Aviles’ [or anyone else’s] “luck” has been evenly distributed, or whether he’s been especially lucky against lefties, etc. Is the data available for you to make such a calculation? What can this tell us about him going forward — will he decline a bit against righties, and alot against lefties, equally against both, etc.?
Thanks for your posts and hard work on this stuff. It’s quite cool, even for a bonehead like me.
OMG Banny. FWIW I am only crdtng u w/3 runs allwd bc of DDJ OMFG
It could be that he makes better contact against LHP, which would make sense
…and, therefore, gets more line drives against them. In that case, you would expect his BABIP to be higher against LHP than RHP. But that does seem to be a large BABIP platoon split.
This is just my opinion. I could easily be wrong.
by Scott McKinney on Aug 15, 2008 1:49 PM EDT up reply actions
Really big splits
he has Dunn-esque ISO against lefties, too
OMG Banny. FWIW I am only crdtng u w/3 runs allwd bc of DDJ OMFG
by Matt Klaassen on Aug 15, 2008 2:42 PM EDT up reply actions
aviles ops when he pulls the ball is something amazing like 1200
so there very well could be something to this, i don’t know of any data set that is sufficiently large enough to get results from with splits included. the math is fairly simple (especially now that i have done it a few times thru), but collecting the data can be tedious for some of these questions. its something i will take a look at.
correction,
1.595 ops with .500 BA when pulling the ball on .417 babip
which would go with the hitting the ball hard leads to more hits conclusion
James, you magnificent bastard! I read your book!
OMG Banny. FWIW I am only crdtng u w/3 runs allwd bc of DDJ OMFG
by Matt Klaassen on Aug 16, 2008 8:15 PM EDT up reply actions

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