Updated Hitter Shift Data: Effect on xBABIP

It’s official. 100% of my value is bothering @jeffwzimmerman to run data and then use that data for said content. I did just that this morning for updated shift data. I believe the last time he posted related content on RotoGraphs was here back in May looking at: Early Hitter Shift Data.

It was because of this post and his balls in play (groundball and pop-up rates) that led me to believe Albert Pujols was going to drop off/continue his downward trend. I guess I pushed out the fact that he was the best hitter of his generation. While the .375 BABIP (from the above link) against shifts did not last, he’s at a very respectable .284. Between this and additional discipline (best contact rate since 2008 and hacking less at stuff outside of the zone), he is able to hover around a .280 BA, which remains elite in conjunction with 30HR, 90R and 100+RBI.

On the other side of the spectrum (in a similar, non-career threatening way), Freddie Freeman is getting shifted against (134 times). He’s only pulling the ball 42.7% of the time. In fact, his center spray is only a few % less (35%) and he still has a .308 BABIP on all pulls, however, that’s dragged down by a .269 BABIP into the shift. Relative to his overall BABIP (.338), for hitters that have been shifted on over 30 times, that’s the 14th biggest differential. Here are the 13 with a larger differential to keep in mind in case the shift is put on even more: Adrian Beltre, Brandon Belt, Danny Valencia, Nate Schierholtz, Ian Kinsler, Adam Lind, Jason Kipnis, Michael Brantley, Matt Kemp, Domonic Brown, Miguel Cabrera, Chris Iannetta and J.P. Arencibia. This is about the only complaint I have on Freeman. His spray is relatively comprehensive so I don’t think this will kill him, but it’s worth monitoring.

So whom do we (all wish that we) own and laughs into the proverbial shift’s face? @joneslandon reminded us how elite Paul Goldschmidt is, and related to this post, he linked to his spray page. Paul Goldschimdt has only been shifted on 21 times this year (albeit missing time) and unless he starts pulling the daylights out of the ball, the shift switch and effect should be limited. His BABIP is. .476 when the shift is on! This provides some reason why his actual BABIP (.368) is about 26 points higher than his expected BABIP (.342).

Here are some others not worth shifting against per this year’s results (in order of positive differential + a 50-shift qualifier):

• Giancarlo Stanton: 78 shifts; .474 BABIP with shift on vs. .358 overall BABIP
• Jason Heyward: 57 shifts; .421 BABIP with shift on vs. .324 overall BABIP
• Nelson Cruz to my surprise: 77 shifts; .351 BABIP with shift on vs. .272 overall BABIP
• Andrew McCutchen (Really? Did the Pirates play the O’s 15 times?): 52 shifts; .404 BABIP with shift on vs. .338 overall BABIP

Here are some bigger samples:

• Kyle Seager (157 shifts): .358 vs. .298
• Pedro Alvarez surprisingly (156 shifts): .321 vs. .278
• Yoenis Cespedes (110 shifts): .336 vs. .298
• Matt Adams (200 shifts!): .375 vs. .339
• and finally we’ll end it here, but enjoy this additional confidence: Chris Carter (133 shifts): .308 vs. .273

I won’t furnish the full list (not sure if I’ll get scolded), so here are the players with the largest shift-on vs. general BABIP differentials on both sides of the spectrum using 50 shifts as the qualifier. You’re looking at BABIP differentials over 3/4 of a standard deviation from the mean, which you can find in the 8th column, “zBABIPdiff”:

Last, week I verified some xBABIP potential. Let me do that quickly again here, now that we have actual shift data. I will use only these hitters from this list with both a BABIP and xBABIP 1 standard deviation from the mean:

Paul Goldschmidt, Giancarlo Stanton, Matt Adams and even Josh Hamilton this year (.375 shift-on BABIP; 128 shifts on) are perfect examples on how this shift effect positively impacts BABIP relative to Jeff’s xBABIP formula.

On the other hand, Freddie Freeman is not a good example, so I need to evaluate that, but I’m only proving my point in this post, which I guess I should make now. I will preface it by saying that a) my goal is to present this in terms of fantasy value rather than a higher, more passionate MLB landscape-changing level, and b) there is an obvious contingency, which is that BABIP in general takes 820 Balls In Play to stabilize, but I’m going to assume that it takes less BIP for the shift to have a significant effect.

While evaluating players (and I still urge you to use xBABIP), keep shift devastation in mind. It’s a fantasy-value and career killer: imagine Chris Davis without the shift = .353 BABIP; imagine Mike Moustakas being an asset vs. his .212 BABIP. When evaluating fantasy studs like Michael Brantley (67 shifts; .235 BABIP with shift on vs. .317 BABIP in general) or Jason Kipnis (37 shifts; .217 BABIP with shift on) assume all will catch on and all will be forced to adjust – and not everyone can ignore or adjust the shift like Paul Goldschmidt and Matt Adams.