Using historic BABIP trends to identify buy and sell targets

It’s getting to the point of your fantasy baseball season where you are looking at ways to get into a cash position – and staying there, or you are just looking forward to the NFL season.  As I wrote about last week, doing your homework before making a trade is an important aspect of the trade.  Are there players out there to target? Do you go after that hot hitter or the slumping guy who you think will heat up and scorch his way through the summer?

I looked at first and second half BABIP from the last three seasons to see if we can use the top and bottom extremes of BABIP leaders and laggards to target who we may want to acquire and who we may want to maximize our return for in a trade.  As has been covered and studied in the past we find that the league average BABIP is usually around .300, and the extremes are in the .380 and .250 range.  Players that are beyond this range will most likely regress/advance towards these extremes. So for instance, David Dahl and his current .439 BABIP should regress and be at .380 (at best) for the rest of the season.  How far will he regress?  Will it be to the league average of .300 or will it be to his career average of .381?  Dahl has a small sample size, but his high BABIP allows us to look at this question.

The following tables are the top and bottom 20 players in BABIP for the seasons 2016-2018.  I looked at their BABIP for the season, opening day (OD) to 6/30,  and from 7/1 to the end of the season (EOS).

Player 2016 OD-6/30/16 7/1/16-EOS Lifetime Player 2016 OD-6/30/16 7/1/16-EOS Lifetime
DJ LeMahieu .388 .359 .413 .344 T. Frazier .236 .186 .282 .269
S. Marte .380 .411 .344 .344 J. Panik .245 .266 .217 .289
JD Martinez .378 .345 .418 .344 C. Granderson .254 .258 .250 .292
J. Villar .373 .405 .341 .339 J. Bautista .255 .239 .278 .264
M. Trout .371 .360 .385 .351 C. Santana .258 .227 .289 .267
F. Freeman .370 .362 .378 .342 C. Carter .260 .272 .247 .272
J. Votto .366 .298 .418 .351 A. Pujols .260 .241 .278 .288
C. Hernandez .363 .338 .384 .353 D. Espinosa .261 .256 .266 .290
P. Goldschmidt .358 .364 .353 .354 B. Harper .264 .255 .273 .319
JT Realmuto .357 .354 .359 .319 Al. Ramirez .265 .278 .247 .288
C. Yelich .356 .380 .335 .356 M. Moreland .266 .272 .259 .283
C. Seager .355 .322 .389 .343 J. Heyward .266 .288 .247 .298
J. Segura .353 .349 .357 .320 J. Bruce .266 .302 .225 .280
C. Blackmon .350 .325 .371 .337 M. Semien .268 .268 .268 .296
D. Fowler .350 .360 .338 .330 A. Hechiavarria .269 .270 .268 .296
I. Desmond .350 .388 .313 .322 K. Davis .270 .271 .269 .276
O. Herrera .349 .356 .341 .339 E. Encarnacion .270 .266 .274 .271
D. Murphy .348 .359 .335 .319 M. Franco .271 .257 .283 .257
J. Altuve .347 .362 .333 .338 T. Tulowitzki .272 .229 .299 .313
B. Belt .346 .344 .349 .327 Z. Cozart .274 .271 .280 .273

Many expect that at some point BABIP regresses or advances toward the league average of .300.  So with the players in the top 20 at the halfway point, you’d expect these players to have a decline in their BABIP and the opposite for those in the bottom 20.  In 2016, 11 of the top 20 players with averages above .300 actually saw their BABIPs rise in the 2nd half.

Highlighted in red are the 13 players that had their BABIPs rise or fall in the direction of their lifetime number.  Looking at the bottom 20 players in BABIP for 2016, only 8 saw their average rise toward .300. but 10 went in the direction of the lifetime average.

Player 2017 OD-6/30/17 7/1/17-EOS Lifetime Player 2017 OD-6/30/17 7/1/17-EOS Lifetime
A. Garcia .392 .384 .402 .331 R. Odor .224 .231 .216 .275
C. Blackmon .371 .357 .385 .337 T. Frazier .226 .216 .237 .269
J. Altuve .370 .355 .387 .338 C. Granderson .228 .260 .180 .292
T. Pham .368 .353 .377 .349 M. Franco .234 .224 .245 .257
T. Beckham .365 .383 .348 .322 J. Bautista .239 .270 .209 .264
D. Santana .363 .343 .384 .358 I. Kinsler .244 .256 .233 .282
C. Taylor .361 .368 .356 .340 S. Schebler .248 .257 .236 .274
A. Judge .357 .413 .294 .357 A. Pujols .249 .250 .249 .288
M. Ozuna .355 .351 .358 .319 J. Gallo .250 .228 .276 .273
D. Gordon .354 .332 .374 .337 Y. Solarte .258 .272 .242 .269
C. Hernandez .353 .335 .369 .339 A. Gordon .261 .236 .288 .311
T. Mancini .352 .386 .329 .321 K. Seager .262 .288 .232 .281
C. Seager .352 .349 .355 .343 C. Beltran .263 .258 .270 .299
E. Hosmer .351 .338 .364 .315 M. Joyce .263 .255 .271 .277
DJ LeMahieu .351 .346 .356 .344 M. Moustakas .263 .261 .266 .264
J. Mauer .349 .329 .368 .341 J. Reyes .263 .214 .321 .305
B. Posey .347 .352 .341 .323 M. Machado .265 .226 .294 .301
J. Baez .345 .305 .386 .344 M. Betts .268 .273 .263 .313
O. Herrera .345 .323 .375 .339 L. Morrison .268 .253 .287 .266
Ma. Gonzalez .343 .348 .340 .308 E. Encarnacion .271 .299 .245 .271

In 2017 we only see 6 players go towards .300 who were in the top 20, but 10 of them went towards their lifetime average.  In 2017 we see half of the bottom 20 players go towards .300 with 13 go towards their established number.

Player 2018 OD-6/30/18 7/1/18-EOS Lifetime Player 2018 OD-6/30/18 7/1/18-EOS Lifetime
JD Martinez 0.375 0.366 0.385 0.344 C. Santana 0.231 0.214 0.247 0.267
C. Yelich 0.373 0.348 0.394 0.356 Y. Solarte 0.233 0.258 0.185 0.269
M. Betts 0.368 0.332 0.402 0.313 M. Kepler 0.236 0.240 0.233 0.256
M. Smith 0.366 0.344 0.388 0.339 C. Davis 0.237 0.219 0.256 0.303
N. Castellanos 0.361 0.376 0.344 0.332 B. Dozier 0.240 0.239 0.241 0.271
P. Goldschmidt 0.359 0.332 0.386 0.354 K. Calhoun 0.241 0.196 0.278 0.291
S. Gennett 0.358 0.383 0.332 0.334 T. Shaw 0.242 0.251 0.231 0.283
F. Freeman 0.358 0.361 0.356 0.342 S. Perez 0.245 0.220 0.268 0.283
L. Cain 0.357 0.337 0.375 0.342 J. Gallo 0.249 0.238 0.264 0.273
D. Duffy 0.353 0.381 0.323 0.332 I. Kinsler 0.250 0.207 0.310 0.282
J. Wendle 0.353 0.337 0.364 0.338 K. Seager 0.251 0.252 0.249 0.281
J. Altuve 0.352 0.381 0.302 0.338 J. Ramirez 0.252 0.271 0.231 0.287
W. Merrifield 0.352 0.341 0.361 0.337 R. Healy 0.257 0.267 0.247 0.298
B. Nimmo 0.351 0.352 0.351 0.345 J. Kipnis 0.258 0.256 0.260 0.263
J. Martinez 0.351 0.317 0.391 0.353 M. Moustakas 0.259 0.261 0.256 0.264
J. Baez 0.347 0.333 0.360 0.344 D. Gregorius 0.259 0.250 0.271 0.283
M. Trout 0.346 0.347 0.345 0.351 V. Martinez 0.260 0.295 0.265 0.305
T. Story 0.345 0.332 0.361 0.343 K. Davis 0.261 0.249 0.274 0.274
C. Taylor 0.345 0.320 0.379 0.340 A. Escobar 0.263 0.216 0.331 0.293
Y. Moncada 0.344 0.329 0.358 0.347 Y. Molina 0.264 0.260 0.267 0.296

In 2018, we again see only 6 of the 20 upper tier players “regressing” towards the league average.  Now that we see a trend here, 15 players approached their career BABIP.  In the lower tier we also see 12 go towards their career number and 12 also rise towards .300.

So, when trying to evaluate a player for the rest of the season, what is it that you should be looking at when using BABIP as a tool?  The correlation is more in line with the player’s lifetime BABIP.  When looking at the 3 years of data, the players that are highlighted in red are those that the second half BABIP went in the proper direction towards the player’s lifetime number.

In 2016 13 upper tier and 10 lower tier players gravitated toward their lifetime numbers in the second half.  In 2017 it was 10 and 13, and in 2018 14 and 12.  Although not an extreme correlation, looking at the lifetime number is a better predictor than just expecting someone to regress or advance based upon the league average that season.  Yes, some of the player’s success or failure is due to the luck of a ball falling in front of an outfield or just out of the reach of an infielder, but a player will more likely perform in line with his career average rather than the overall league average.

Let’s take a look at some players on both sides of the aisle and see whether we can expect them to regress or advance their BABIP the rest of the season.

One of the bigger surprises of the year is the success of Josh Bell.  He was always touted as a high end prospect, but never seemed to be able to reach his potential.  His BABIP so far this year is .363 – his lifetime average is .303.  The strikeout rate is up from 17.8% to 21.2% and his walk rate is down from 13.2% to 9.7%.  He’s therefore putting about the same percentage of balls in play if you add in the home runs.  His line drive, ground ball and fly ball rates are all similar to last year’s.  What is up is his HR/FB ratio.  It is dramatic, going from 9.2% to 28.1%.  By hitting 19% more balls over the fence, this will reduce the number of fly ball outs and increase BABIP as his splits are the same.  Is his 28% HR/FB ratio sustainable?  Only Christian Yelich and JD Martinez reached that number over the entire season last year.  With all his peripherals the same except for the HR/FB ratio, I expect Bell will cool off the remainder of the season and will give a sell high recommendation at this point.

Another breakout player this year is Max Kepler.  As of June 11th his 15 homers are only 5 away from his career high of 20.  His BABIP is currently .251 and his career number sits at .256. Based on what we saw over the last three seasons, the most likely thing to happen is just more of the same for Kepler.  When we dig deeper the story on Kepler is almost identical to that of Bell.  His peripherals are all similar to last year except his HR/FB ratio which has risen to 17.6% from 9.9%. This explains the increase in HR, but what doesn’t make sense is that the BABIP hasn’t risen like that of Bell’s.  Kepler’s hard hit, ground ball and line drive rates are all similar.  Here is an example of that little bit of luck factor when looking at BABIP.  Expect a rise in his BABIP over the remainder of the season, and I give him a hold if you own him and a buy if you don’t.

One of the leaders this year in BABIP is Joey Gallo.  His .385 mark is over 100 points above his lifetime average.  Is this sustainable, and what has changed to improve this number? Gallo is currently raking a 43.6% HR/FB ratio which is extremely high, and as mentioned with Bell, this can raise BABIP.  What is different with Gallo this year is an increase in his line drive rate and a comparable decrease in his fly ball rate.  These both will contribute to higher BABIP rates.  I don’t expect the extreme HR/FB ratio to continue, but with the change in his line drive rate, I expect Gallo to continue with a higher than normal BABIP, but it will probably regress a bit back towards .300.  Just an aside, if Gallo was able to continue this absurd HR/FB rate he would hit 78 HRs based upon his projected ROS at bats.

I saw a question on twitter tonight from @_miggy22 asking what value to expect from Jose Ramirez the rest of the season.  My reply was simply if he steals 40 bases he has value, but let’s look at what his BABIP numbers show us is possible.  As of June 11 Ramirez ranked 154th out of 162 qualified hitters in BABIP.  From July 1st of last year to June 11th, he is last, dead last, with a BABIP of .229.  This year it currently sits at .227.  There have been many articles written about his struggles, and one of the biggest differences is he just isn’t hitting fastballs the way he did in the first half of 2018 and prior.  His lifetime BABIP is .286.  Do I think he’ll “advance” towards his lifetime average?  He has sustained this low BABIP for almost a year now and there is no end in sight.  There have been no hitting streaks, multi-hit games, etc.., to signal a breakout.  I’m expecting more of the same so I give Ramirez a buy only if you need SBs and you can afford the hit in batting average. If your team doesn’t need steals, sell him for anyone with a decent batting average who will hit 10-15 homers the rest of the season – that could be most players this year.

What follows are the current leaders and laggards for 2019.  Notice every player in the top 20 is exceeding his lifetime average, and all laggards are below.  Based upon what we saw from 2016-2018, roughly 60% of them will either rise or fall towards their lifetime average.

Player 2019 YTD Lifetime Player 2019 YTD Lifetime
D. Dahl 0.430 0.380 M. Franco 0.197 0.256
J. Gallo 0.385 0.273 Y. Alonso 0.201 0.293
A. Meadows 0.383 0.360 J. Profar 0.204 0.261
B. Lowe 0.383 0.341 J. Pederson 0.216 0.259
J. Baez 0.378 0.342 J. Bruce 0.216 0.28
Y. Moncada 0.378 0.352 A. Pujols 0.219 0.288
J. McNeil 0.373 0.365 E. Encarnacion 0.220 0.270
J. Polanco 0.371 0.322 J. Ramirez 0.221 0.286
A. Mondesi 0.368 0.333 R. O’Hearn 0.225 0.253
S. Choo 0.366 0.338 K. Pillar 0.227 0.288
D. Santana 0.363 0.361 B. Gardner 0.227 0.306
D. Peralta 0.362 0.339 R. Odor 0.228 0.276
T. Anderson 0.361 0.331 J. Smoak 0.235 0.269
A. Garcia 0.358 0.332 E. Hernandez 0.235 0.270
J. Bell 0.358 0.302 J. Winker 0.238 0.302
M. Cabrera 0.355 0.345 Y. Puig 0.239 0.311
B. Goodwin 0.352 0.328 F. Reyes 0.244 0.301
C. Bellinger 0.352 0.316 M. Kepler 0.247 0.255
J. Soto 0.351 0.342 A. Bregman 0.247 0.294
L. Garcia 0.351 0.323 E. Rosario 0.247 0.313

When looking at who to buy, sell and hold as we enter trading season, make sure you look at career BABIP as that is the most likely trend for a player to follow.  The juiced ball has played a role in changing what we are seeing in BABIP by increasing HR/FB ratios, but as demonstrated with Kepler and Bell, the results can be different.  Continue to churn and look for trades that will move you up in the standings, which is more important than “winning” the trade.  Always available on twitter to answer questions @gasdoc_spit

 

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Andy Spiteri

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Playing fantasy sports since 1991 when you got your stats in the mail on Thursdays...Husband and father of two. I put people to sleep for a living. Mets, NY Rangers and Eagles/Jets (a product of being born in NYC but living in Bucks County PA for 20 years) fan. Home league baseball auction is top 5 day of the year!

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