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Is Jose Altuve due for a BABIP regression?

BABIP Files: Regression and Rebounds at Second Base


BaseballFantasyMLB

Hopefully you know the drill by now. The deal here is that I take a player’s 2014 Batting Average on Balls in Play (BABIP) and compare it to their BABIP over the previous five seasons. If thier BABIP in 2014 was significantly higher, then it may signal a back-slide for 2015. If it was lower, then things may bounce back this season. If you didn’t catch them, here are the BABIP Files for Catchers and First Basemen with a little more about BABIP.

Of course this is very simplistic. BABIP is not just about luck. There is other batted ball data we need to look at before we lay it all on the whims of the baseball gods.

Below is the raw data for all second baseman with over 200 at-bats in 2014. I’ve highlighted players with a BABIP Differential of .030 plus or minus. This is an entirely subjective point with no solid rationale. If you want to set the bar lower or higher, by all means here’s the data. Below the table are some notes on the Fantasy relevant players whose batted ball data may give further insight.

 

2014 BABIP Differential for Second Basemen

 

Name
2014 AVG
2014 BABIP
5-Year BABIP
BABIP Diff
Kolten Wong
.249
.275
.191
.084
Munenori Kawasaki
.258
.323
.258
.065
Rickie Weeks
.274
.355
.303
.052
Jose Altuve
.341
.360
.317
.043
Dee Gordon
.289
.346
.307
.039
Danny Espinosa
.219
.319
.295
.024
Ian Kinsler
.275
.288
.268
.020
Ramon Santiago
.246
.300
.284
.016
Howie Kendrick
.293
.347
.334
.013
Robinson Cano
.314
.335
.324
.011
Cliff Pennington
.254
.309
.298
.011
Chase Utley
.270
.295
.287
.008
Ryan Flaherty
.221
.266
.258
.008
Brandon Hicks
.162
.208
.200
.008
Daniel Murphy
.289
.322
.317
.005
Brandon Phillips
.266
.298
.295
.003
Wilmer Flores
.251
.265
.264
.001
Ben Zobrist
.272
.301
.301
.000
Aaron Hill
.244
.276
.276
.000
Jeff Baker
.264
.331
.333
-.002
Dustin Pedroia
.278
.307
.310
-.003
Darwin Barney
.241
.267
.270
-.003
Brian Dozier
.242
.269
.274
-.005
Mike Aviles
.247
.271
.279
-.008
Emilio Bonifacio
.259
.324
.334
-.010
DJ LeMahieu
.267
.322
.335
-.013
Donovan Solano
.252
.304
.317
-.013
Nick Punto
.207
.279
.296
-.017
Logan Forsythe
.223
.268
.287
-.019
Eric Sogard
.223
.251
.272
-.021
Brian Roberts
.237
.269
.293
-.024
Neil Walker
.271
.288
.312
-.024
Jonathan Schoop
.209
.249
.273
-.024
Gordon Beckham
.226
.255
.281
-.026
Jason Kipnis
.240
.288
.316
-.028
Jedd Gyorko
.210
.253
.287
-.034
Skip Schumaker
.235
.284
.320
-.036
Marcus Semien
.234
.310
.348
-.038
Brett Lawrie
.247
.260
.300
-.040
Asdrubal Cabrera
.241
.272
.313
-.041
Omar Infante
.252
.275
.319
-.044
Alberto Callaspo
.223
.242
.286
-.044
Sean Rodriguez
.211
.235
.287
-.052
Scooter Gennett
.289
.321
.380
-.059
Mark Ellis
.180
.225
.298
-.073
Charlie Culberson
.195
.259
.333
-.074
Stephen Drew
.162
.194
.305
-.111

 

Positive BABIP Differential

Let’s take a deeper look at a few of the players BABIP Differential seems to indicate are due for a fall.

  • Kolten Wong – The .084 differential is the largest on the board, but his previous “five-year data” is based on 62 plate appearances in 2013. Even his .275 BABIP last season seems a little low for a left-handed hitter with good speed. Also take into account that a higher BABIP means he’s on base more which will also increase his opportunities to run. I’m expecting a pretty big step forward for Wong this season.
  • Jose Altuve – Unlike Wong, Altuve’s data is based on over 1500 plate appearances, so we need to take it seriously. Is there anything else in his peripherals that could explain his much higher BABIP? Well, he did cut his strikeout rate down by 5.1%, but fewer strikeouts don’t explain the balls in play. His line drive rate was a career-best, but just barely over 2013 when he hit .283. His FB% was also a career-high which typically would contradict a higher BABIP. There’s just really not enough change in his batted ball data to explain the elevated BABIP Differential. I like Altuve and he may still be a decent buy with plenty of others expecting big regression. Just be ready for a ,290-.300 batting average and a few less SBs as a result.
  • Dee Gordon – I have to be honest. I was very surprised when I looked through Dee Gordon’s season splits. I had assumed that his first half numbers were much better than his second half. Actually, other than stolen bases, he had a pretty balanced season, hitting over .290 in five separate months. Unlike Altuve it’s very easy to see why Gordon’s BABIP had the big increase, as 81 percent of his batted balls were either line drives or ground balls. He’d done this in the past in spurts, but 2014 was the first time he was able to maintain it for an entire season. We may see a bit of regression here, but as long as he maintains that grounder-centric approach, he’s going to hit for a pretty solid average.

 

Negative BABIP Differential

And now a look at the second basemen that seemed to be on the unfortunate side of things in 2014.

  • Jason Kipnis – Yes, I cheated by including Kipnis and his .-028 BABIP Differential, but he’s too important of a player not to talk about. I was expecting to find something in his peripherals that might explain his terrible season. I looked through his batted ball numbers and his plate discipline stats and the only thing that sticks out is a 4.8 HR/FB%, which is almost a third of what it was in 2013. The cause? Well according to Baseball Heat Maps, Kipnis lost 20 feet from his average fly ball distance from 2013 to 2014. While I’m not sure average fly ball distance is predictive in its nature, this does explain what happened to the home runs. Was it a product of the oblique injury Kipnis dealt with? It’s all speculation. I’m not targeting Kipnis, but I won’t be hesitant to grab him if he falls far enough. I see enough of a bounce-back to put him in my Top 10 second base rankings.
  • Jedd Gyorko – With Gyorko it’s more about halves of 2013 than his previous numbers. In the first half his .192 BABIP was joined by a 25.3 K% and a measly 16.6 LD%. In the second half his BABIP climbed to .313, his K rate dropped to 19.8% and his LD% climbed to 27.0.  Those second half numbers more closely resemble what Gyorko did in 2013. It’s hard to know how the changes to Petco Park will affect Padre hitters, but I feel safe projecting Gyorko to match his 2013 season with upside for more.
  • Brett Lawrie – Talk about an enigma. Lawrie’s 2014 numbers are a mess. The 12 HRs in 70 games seems promising, but his LD% sank to an anemic 13.7. making it the second straight year of a big drop. Coincidently (or not) it’s also the third straight year his BABIP has gone down. The move to Oakland is another strike against Lawrie. While the second base eligibility is nice, it’s hard to see a breakout on the horizon for Lawrie with so many trends headed the wrong way.
  • Omar Infante – If it matters to you, Infante will likely return to the empty 2.80 batting averages that we’ve grown to not hate.
  • Scooter Gennett – Much like Kolten Wong, Gennetts previous BABIP is built around a very limited sample size and his 2013 BABIP of .380 is unsustainable. With that said, Gennett did up his LD% and seems safe to hit .280+. He’s quickly turning into a rich man’s version of Omar Infante. He won’t hurt, but he also won’t really help.


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