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Every offseason, there is a hitter or two who is dubbed as a poor bet for next year because of an extremely high BABIP. While the warnings are usually valid, they are often vague and don’t give us enough information. Should we avoid a hitter entirely because of a high BABIP? Or are there circumstances where a strong BABIP hitter might be a decent investment the following season?

Table 1: Top 50 BABIPs 2008-2012

Player

Year

BABIP

Next Year

Diff

$

Next Year Salary

Next Year Value

Gain/Loss

Chris Johnson

2012

.354

.394

.040

$17

7

$21

14

Dexter Fowler

2011

.354

.390

.036

$16

17

$21

4

Joe Mauer

2012

.364

.383

.019

$25

20

$18

-2

Alex Gordon

2011

.358

.356

-.002

$31

24

$24

0

Justin Upton

2009

.360

.354

-.006

$30

34

$22

-12

Mike Trout

2012

.383

.376

-.007

$47

41

$41

0

Joey Votto

2009

.372

.361

-.011

$26

31

$40

9

Joey Votto

2010

.361

.349

-.012

$40

40

$34

-6

Matt Kemp

2008

.361

.345

-.016

$31

35

$37

2

Fred Lewis

2008

.365

.348

-.017

$19

14

$8

-6

Matt Holliday

2008

.359

.341

-.018

$37

30

$30

0

Michael Bourn

2011

.369

.349

.020

$36

29

$28

-1

Andrew McCutchen

2012

.375

.353

-.022

$39

34

$38

4

Shin-Soo Choo

2009

.370

.347

-.023

$26

26

$30

4

Joe Mauer

2009

.373

.348

-.025

$32

35

$23

-12

Matt Kemp

2011

.380

.354

-.026

$53

41

$24

-17

Chris Coghlan

2009

.365

.336

-.029

$21

16

$12

-4

Ichiro Suzuki

2009

.384

.353

-.031

$31

27

$31

4

Kevin Youkilis

2009

.359

.327

-.032

$25

30

$21

-9

Miguel Cabrera

2011

.365

.331

-.034

$36

39

$40

1

Justin Upton

2010

.354

.319

-.035

$22

30

$35

5

Michael Bourn

2009

.366

.329

-.037

$33

21

$26

5

Austin Jackson

2012

.371

.333

-.038

$25

25

$18

-7

Asdrubal Cabrera

2009

.360

.318

-.042

$21

19

$9

-10

Chone Figgins

2009

.356

.314

-.042

$28

25

$19

-6

Manny Ramirez

2008

.370

.328

-.042

$35

31

$16

-15

Torii Hunter

2012

.389

.344

-.045

$26

15

$24

9

Adrian Gonzalez

2011

.380

.334

-.046

$36

35

$24

-11

Alex Gordon

2012

.356

.310

-.046

$24

24

$22

-2

Emilio Bonifacio

2011

.372

.325

-.047

$28

20

$12

-8

Brad Hawpe

2009

.356

.308

-.048

$21

18

$7

-11

Nyjer Morgan

2009

.355

.304

-.051

$27

21

$16

-5

Hanley Ramirez

2009

.379

.327

-.052

$41

44

$33

-11

Alex Avila

2011

.366

.313

-.053

$20

18

$8

-10

Austin Jackson

2010

.396

.340

-.056

$24

17

$17

0

Buster Posey

2012

.368

.312

-.056

$29

29

$20

-9

Omar Infante

2010

.355

.298

-.057

$19

15

$14

-1

Carlos Gonzalez

2010

.384

.326

-.058

$45

39

$33

-6

David Wright

2009

.394

.335

-.059

$28

35

$31

-4

Derek Jeter

2009

.368

.307

-.061

$34

27

$22

-5

Jason Bartlett

2009

.364

.299

-.065

$28

20

$12

-8

Dexter Fowler

2012

.390

.323

-.067

$21

17

$20

3

Michael Young

2011

.367

.299

-.068

$30

23

$16

-7

Hunter Pence

2011

.361

.290

-.071

$31

30

$22

-8

Josh Hamilton

2010

.390

.317

-.073

$38

32

$26

-6

Milton Bradley

2008

.388

.310

-.078

$23

16

$10

-6

Miguel Montero

2012

.362

.282

-.080

$18

17

$7

-10

Felipe Lopez

2009

.358

.273

-.085

$21

6

$8

2

Colby Rasmus

2010

.354

.267

-.087

$23

24

$12

-12

Chipper Jones

2008

.383

.287

-.096

$29

24

$17

-7

Average

-.040

$29

26

$22

-4

These are the top 50 hitters (qualifiers for the batting title) by BABIP between 2008-2012, sorted by the biggest improvement in BABIP to the largest decrease. Only three hitters improved in BABIP from one year to the next so as you might suspect, there is a good reason that fantasy analysts point to high BABIP as a harbinger of bad things to come the following season.

However, the earnings columns indicate that while there is a typically a drop off, it isn’t extreme. The market already adjusts for the possibility that high BABIP outliers are going to slip, giving these hitters a $3 pay cut (on average). It isn’t quite enough of a pay cut, as these hitters earn $22 on average, but while these hitters do take a loss it isn’t a significant one. Thirteen of these 50 hitters turn a profit for their fantasy owners while another four break even. A 34 percent chance of buying a break-even hitter isn’t great, but it’s not the shellacking you might expect given the prior year’s BABIP.

Rather than simply look at a strong BABIP and write it off as an outlier, I always look at the following factors:

1. What is the player’s track record?
Chris Johnson is the early poster child for severe BABIP regression, and his .394 BABIP is certainly an extremely high number. However, Johnson put a .317 BABIP in 2011 and a .354 BABIP in 2012. The .394 is unlikely a second year in a row, but a .340 or .350 BABIP given Johnson’s career numbers to date isn’t unrealistic.

2. What is the player’s expected BABIP (xBABIP)?
Thanks to the great work of a number of analysts over the years, we can take a player’s various contact rates and extrapolate what his BABIP should have been. Back in 2010, Tristan Cockroft of ESPN looked at batting averages for each type of batted ball and devised the following formula for calculating expected BABIP:

(GB X .237) + (FB X .138) + (LD X .724)

Using Johnson again as an example, 25.5 percent of his batted balls in 2013 were fly balls or pop ups, 28.3 percent were line drives, and 46.3 percent were ground balls. Johnson’s expected BABIP (xBABIP) was .349. Yes, Johnson should have fared more poorly in 2013, but a .349 BABIP on balls in play in 2014 would still be very good…and well in line with his career norms.

3. Don’t forget to take speed into account
This doesn’t apply to Johnson, but you’ll notice that some of the players on the chart above are players who utilize their speed not only on the base paths but also at the plate. Mike Trout turned 12 percent of his ground balls into infield hits in 2012 and 16% in 2013. His xBABIP based on the formula above is .326; however, given Trout’s speed, he is likely to generate more infield hits than Johnson and even with a lower line drive rate than Johnson is still a strong BABIP threat.

Bringing it back to fantasy
All of this theory is interesting, but I’m far more interested in what impact an artificially high BABIP might have on a player’s value as opposed to what the theoretically “correct” expected BABIP will or should be. Using Johnson as an example, what would his numbers have looked like last year had his BABIP been closer to what it “should” have been?

Table 2: Chris Johnson’s 2013: xBABIP versus BABIP

Type

At bats

Hits

Home Runs

RBI

Runs

Steals

BA

$

BABIP

514

165

12

68

54

0

.321

$21

xBABIP

514

147

12

63

49

0

.286

$16

Johnson had 514 at-bats in 2013, only 400 of those at bats resulted in an outcome that put the ball in play (home runs and sacrifice flies do not count against BABIP). Even with a sharp drop for Johnson in BABIP, he only would have lost 18 base hits. There is no way to know how many runs or RBI Johnson truly would have lost, but since Johnson had 11 percent fewer hits on the xBABIP model, I took this percentage of RBI and runs away from Johnson on plays that did not result in a home run.

This rough model tells me that Johnson is extremely unlikely to earn $21 again in 2014. However, it also tells me that Johnson is unlikely to simply disappear fall off of the map entirely. His line drive rates support a strong BABIP and there’s a good chance that he could still put up a decent batting average in 2014, even if it isn’t a great batting average. Most of Johnson’s lost value will come at the expense of his batting average; three of the five dollars he would lose in this scenario would come due to the batting average drop off. On the other hand, there is a good chance that Johnson could still be a positive batting average earner.

There is no doubting that BABIP is an important component of future player forecasting. However, BABIP often gets overstated in terms of how overvalued or undervalued a player is. Typically, a BABIP drop leads to a moderate drop in a player’s production, not a significant one. In cases of significant performance drops, a BABIP decline is merely one contributing factor, not the sole determinant of future value.

Thank you for reading

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