The Z Files: Projecting Pitcher's BABIP

The Z Files: Projecting Pitcher's BABIP

This article is part of our The Z Files series.

I'm so old, I remember when we assumed every pitcher's BABIP (batting on balls in play) would regress to .300. It didn't matter if it was Pedro Martinez or Pedro Astacio, their BABIP should be .300.

Thankfully, we've come a long way since the advent of DIPS theory. To be fair, Voros McCracken's findings were revolutionary, and served as a foundation for a new generation of analysis.

The formula for BABIP is:

(Hits – HR)/(AB – HR- K +SF)

The original DIPS (defense-independent pitching statistics) theory noted how a large percentage of pitcher's BABIP clustered around .300. If an individual's mark was lower, he was deemed lucky, and his BABIP was expected to regress towards .300, either in-season or the following year. If it was above .300, the hurler was judged to be unlucky, with impending regression down to .300.

Over the years, it's become apparent that pitchers exert some influence on their BABIP; it isn't totally random. That said, the level of influence is often overblown which can produce flawed analysis. However, expecting everyone's BABIP to move towards league average is also egregious.

Thinking about it from a practical sense, what should affect BABIP? That is, what factors can a pitcher influence that would generate a low BABIP? The two that immediately come to mind are limiting hard contact and line drives.

Last time, I posed the question, "How Much Influence Does a Pitcher Exert?" Statcast's average exit velocity and HardHit% were reviewed, along with Baseball Info

I'm so old, I remember when we assumed every pitcher's BABIP (batting on balls in play) would regress to .300. It didn't matter if it was Pedro Martinez or Pedro Astacio, their BABIP should be .300.

Thankfully, we've come a long way since the advent of DIPS theory. To be fair, Voros McCracken's findings were revolutionary, and served as a foundation for a new generation of analysis.

The formula for BABIP is:

(Hits – HR)/(AB – HR- K +SF)

The original DIPS (defense-independent pitching statistics) theory noted how a large percentage of pitcher's BABIP clustered around .300. If an individual's mark was lower, he was deemed lucky, and his BABIP was expected to regress towards .300, either in-season or the following year. If it was above .300, the hurler was judged to be unlucky, with impending regression down to .300.

Over the years, it's become apparent that pitchers exert some influence on their BABIP; it isn't totally random. That said, the level of influence is often overblown which can produce flawed analysis. However, expecting everyone's BABIP to move towards league average is also egregious.

Thinking about it from a practical sense, what should affect BABIP? That is, what factors can a pitcher influence that would generate a low BABIP? The two that immediately come to mind are limiting hard contact and line drives.

Last time, I posed the question, "How Much Influence Does a Pitcher Exert?" Statcast's average exit velocity and HardHit% were reviewed, along with Baseball Info Solutions Hard%, Medium% and Soft%. The answer to that question is, "Not as much as you think." It is more than random, but there are other elements of pitching for which a pitcher exerts more control.

The chief area which a player can affect is groundballs and flyballs. The influence of line drives is the weakest of everything cited in this discussion.

Putting everything together, pitchers have limited influence over hard and soft contact, as well as the number of line drives they surrender. In other words, pitchers don't exert much control over two of the factors helping to maintain a low BABIP. Or at least not as much as many intuit.

On the other hand, pitchers have a great deal of influence over groundballs and flyballs. Furthermore, it is known the BABIP of grounders is higher than that of flies. As such, groundball pitchers should organically carry a higher BABIP than their flyball counterparts. Maybe we shouldn't be so quick to assume regression to the league mean.

Before going on, I am not taking credit for what follows. It was derived from independent thinking, but I am sure others do the same, or something similar. The notion is a pitcher's xBABIP (expected BABIP) can be computed solely based on their batted ball distribution. 

xBABIP = (GB% x GB BABIP) + (LD% x LD BABIP) + (FB% x FB BABIP).

Using 2023 league averages, the component BABIP are

  • Line Drive: 0.628
  • Groundball: 0.248
  • Flyball: 0.095

These numbers may be different from those cited elsewhere because these don't include homers. The overall BABIP for flyballs and line drives is higher. For simplicity's sake, bunts are included with grounders while popups are lumped with flyballs. If this were a study to be submitted to a SABR conference, I may have further distilled the components.

Let's plug in some numbers to get a feel for the range of BABIP, based solely on batted ball distribution. The LD% will be kept constant. Last season, it was 25.2 percent. Again, this is just for balls in play. It is higher than what will be shown elsewhere for the league average, but that denominator includes homers (mostly all flyballs, with some line drives, depending on the data source). The top line in bold blue is the league average, encompassing all pitchers who threw at least 50 innings.

GB%FB%LD%BABIP
44.330.425.20.297
659.825.20.329
6014.825.20.321
5519.825.20.314
5024.825.20.306
4034.825.20.291
3539.825.20.283
3044.825.20.275
2549.825.20.268

After the league average, the top two and bottom two are extremes. Most pitchers induce between 35 and 55 percent ground balls, generating an xBABIP range between .283 and .314. I can't count the number of times I saw a .283 BABIP and Pavlovian assumed it was lucky, or targeted a .314 BABIP, convinced it would drop.

Here is a sortable table displaying the BABIP and xBABIP of all pitchers who compiled at least 50 frames last season.

PitcherBABIPxBABIPDifference
A.J. Minter0.3310.3050.026
A.J. Puk0.3190.2660.053
Aaron Bummer0.3400.3080.032
Aaron Civale0.2890.2860.003
Aaron Nola0.2860.302-0.016
Adam Ottavino0.2550.271-0.016
Adam Wainwright0.3590.3110.048
Adbert Alzolay0.2900.311-0.021
Adrian Houser0.3200.2820.038
Adrian Martinez0.3150.324-0.009
Albert Abreu0.2760.2750.001
Alec Marsh0.3210.3020.019
Alek Manoah0.3080.2890.019
Alex Cobb0.3190.3180.001
Alex Faedo0.2110.282-0.071
Alex Lange0.2450.296-0.051
Alex Wood0.3030.2830.020
Alex Young0.2790.289-0.010
Alexis Diaz0.2700.271-0.001
Andre Jackson0.2480.304-0.056
Andre Pallante0.3200.2980.022
Andres Machado0.3040.307-0.003
Andrew Abbott0.3020.2810.021
Andrew Chafin0.3100.312-0.002
Andrew Heaney0.3020.2800.022
Andrew Nardi0.2900.2850.005
Anthony DeSclafani0.3030.314-0.011
Aroldis Chapman0.3140.3080.006
Austin Gomber0.3140.2950.019
Bailey Falter0.3110.3000.011
Bailey Ober0.2760.2640.012
Ben Lively0.2970.314-0.017
Blake Snell0.2560.310-0.054
Bobby Miller0.2770.297-0.020
Brad Hand0.3450.3350.010
Brady Singer0.3300.3080.022
Brandon Bielak0.3120.2980.014
Brandon Pfaadt0.3160.2920.024
Brandon Williamson0.2800.298-0.018
Brandon Woodruff0.2040.278-0.074
Braxton Garrett0.3020.319-0.017
Brayan Bello0.3070.2910.016
Brennan Bernardino0.3380.3240.014
Brent Honeywell0.2870.296-0.009
Brent Suter0.3020.2740.028
Brock Burke0.2950.2640.031
Brooks Raley0.2880.2690.019
Brusdar Graterol0.2620.270-0.008
Bryan Abreu0.2620.298-0.036
Bryan Hoeing0.2750.297-0.022
Bryan Woo0.2740.281-0.007
Bryce Elder0.2750.305-0.030
Bryce Miller0.2900.304-0.014
Bryse Wilson0.2360.290-0.054
Buck Farmer0.2440.264-0.020
Cal Quantrill0.3000.309-0.009
Caleb Ferguson0.3640.3110.053
Camilo Doval0.3080.2820.026
Carlos Carrasco0.3360.3240.012
Carlos Estevez0.3460.2990.047
Carlos Hernandez0.2870.307-0.020
Carlos Rodon0.2870.2850.002
Charlie Morton0.3230.3080.015
Chase Anderson0.2920.2880.004
Chase Silseth0.2500.304-0.054
Chris Bassitt0.2740.292-0.018
Chris Flexen0.3330.3180.015
Chris Martin0.3010.337-0.036
Chris Sale0.2910.2840.007
Chris Stratton0.2760.2670.009
Cionel Perez0.3230.2930.030
Clarke Schmidt0.3130.2950.018
Clay Holmes0.3010.306-0.005
Clayton Kershaw0.2500.293-0.043
Cody Bradford0.2850.288-0.003
Cole Irvin0.2930.2920.001
Cole Ragans0.2750.295-0.020
Colin Holderman0.3290.2890.040
Colin Poche0.2470.268-0.021
Colin Rea0.2570.293-0.036
Collin McHugh0.3480.3240.024
Connor Seabold0.3400.3010.039
Corbin Burnes0.2440.306-0.062
Corey Kluber0.3010.2850.016
Craig Kimbrel0.2390.305-0.066
Cristian Javier0.2730.2700.003
Cristopher Sanchez0.2730.295-0.022
Dakota Hudson0.3050.312-0.007
Dane Dunning0.2880.295-0.007
Daniel Lynch0.2520.254-0.002
Danny Coulombe0.3080.2500.058
Dauri Moreta0.2650.292-0.027
David Bednar0.2990.2610.038
David Peterson0.3710.3120.059
David Robertson0.2970.2720.025
Dean Kremer0.2930.313-0.020
Derek Law0.2780.305-0.027
Devin Williams0.1980.315-0.117
Domingo German0.2330.281-0.048
Dominic Leone0.2550.294-0.039
Drew Smith0.3010.2670.034
Drew Smyly0.3070.2910.016
Drew VerHagen0.2620.275-0.013
Dylan Cease0.3310.3060.025
Dylan Floro0.4010.3300.071
Eduardo Rodriguez0.2750.309-0.034
Edward Cabrera0.2850.2680.017
Eli Morgan0.3370.2980.039
Elvis Peguero0.2740.296-0.022
Emilio Pagan0.2220.266-0.044
Emmanuel Clase0.2960.2960.000
Emmet Sheehan0.2400.255-0.015
Enyel De Los Santos0.2750.305-0.030
Erasmo Ramirez0.3500.3080.042
Erik Swanson0.2800.2510.029
Eury Perez0.2640.270-0.006
Evan Phillips0.2220.297-0.075
Felix Bautista0.2740.2600.014
Fernando Cruz0.3170.2940.023
Framber Valdez0.2840.313-0.029
Freddy Peralta0.2750.280-0.005
Gabe Speier0.3050.3030.002
Garrett Whitlock0.3420.3060.036
Gavin Williams0.2700.310-0.040
Genesis Cabrera0.2810.2770.004
George Kirby0.2930.2850.008
George Soriano0.2840.292-0.008
Gerrit Cole0.2630.296-0.033
Giovanny Gallegos0.2970.2960.001
Graham Ashcraft0.2930.308-0.015
Grayson Rodriguez0.3230.3130.010
Gregory Santos0.3370.3360.001
Gregory Soto0.2650.274-0.009
Griffin Canning0.2970.2800.017
Griffin Jax0.2990.2920.007
Hayden Wesneski0.2610.310-0.049
Hector Neris0.2220.264-0.042
Hoby Milner0.2540.291-0.037
Hogan Harris0.3080.2990.009
Huascar Brazoban0.3180.3060.012
Hunter Brown0.3300.2960.034
Hunter Greene0.3420.2780.064
Hunter Harvey0.2530.282-0.029
Hyun Jin Ryu0.2750.301-0.026
Ian Gibaut0.2950.2850.010
Ian Hamilton0.3090.312-0.003
J.P. France0.2900.298-0.008
Jack Flaherty0.3570.3180.039
Jacob Webb0.2460.293-0.047
Jaime Barria0.2860.292-0.006
Jake Bird0.3330.3020.031
Jake Diekman0.2500.277-0.027
Jake Irvin0.2810.2800.001
Jakob Junis0.3380.2950.043
James Kaprielian0.3240.2790.045
James Paxton0.2940.295-0.001
Jameson Taillon0.2920.2900.002
Jared Shuster0.2770.2770.000
Jason Adam0.2390.263-0.024
Jason Foley0.3100.319-0.009
Javier Assad0.2690.292-0.023
Jeff Hoffman0.2320.258-0.026
Jesse Scholtens0.3130.2970.016
Jesus Luzardo0.3120.2790.033
Jhoan Duran0.3010.2790.022
Jhony Brito0.2680.274-0.006
Joan Adon0.3330.3130.020
Joe Jimenez0.3040.318-0.014
Joe Musgrove0.3050.3000.005
Joe Ryan0.3050.2750.030
Joel Payamps0.2770.287-0.010
Joey Wentz0.3290.2880.041
Johan Oviedo0.2810.301-0.020
Johnny Cueto0.2360.294-0.058
Jon Gray0.2990.304-0.005
Jordan Hicks0.3230.3040.019
Jordan Lyles0.2560.278-0.022
Jordan Montgomery0.2950.311-0.016
Jordan Romano0.2960.2890.007
Jordan Weems0.2230.272-0.049
Jorge Lopez0.3160.330-0.014
Jose Berrios0.2900.308-0.018
Jose Cisnero0.3420.2930.049
Jose Cuas0.3220.3110.011
Jose Hernandez0.2990.2840.015
Jose Leclerc0.2440.250-0.006
Jose Quintana0.3070.3050.002
Jose Urquidy0.2810.287-0.006
Josh Fleming0.2800.308-0.028
Josh Hader0.2690.2650.004
Josh Sborz0.2870.289-0.002
Josh Winckowski0.3310.3000.031
Josiah Gray0.2930.2900.003
JP Sears0.2800.2670.013
Julian Merryweather0.3130.2850.028
Julio Teheran0.2660.300-0.034
Julio Urias0.2850.2790.006
Justin Lawrence0.3050.315-0.010
Justin Steele0.3200.3040.016
Justin Topa0.2970.303-0.006
Justin Verlander0.2650.281-0.016
Ken Waldichuk0.3130.2890.024
Kendall Graveman0.2560.296-0.040
Kenta Maeda0.2930.294-0.001
Kevin Gausman0.3240.2990.025
Kevin Ginkel0.2440.289-0.045
Kevin Kelly0.2640.305-0.041
Keynan Middleton0.2780.279-0.001
Kirby Yates0.2130.279-0.066
Kodai Senga0.2790.285-0.006
Kutter Crawford0.2690.2580.011
Kyle Bradish0.2710.313-0.042
Kyle Finnegan0.2940.316-0.022
Kyle Freeland0.3120.2980.014
Kyle Gibson0.3110.3000.011
Kyle Hendricks0.2840.295-0.011
Kyle Muller0.3750.2980.077
Kyle Nelson0.3310.3000.031
Lance Lynn0.2920.2900.002
Logan Allen0.3170.2950.022
Logan Gilbert0.2750.291-0.016
Logan Webb0.3030.305-0.002
Louie Varland0.2840.2780.006
Lucas Erceg0.3570.3000.057
Lucas Giolito0.2750.280-0.005
Lucas Sims0.2150.252-0.037
Luis Castillo0.2680.290-0.022
Luis Garcia0.3010.308-0.007
Luis Ortiz0.3090.3070.002
Luis Medina0.3060.311-0.005
Luis Severino0.3280.3140.014
Luke Weaver0.3310.3210.010
MacKenzie Gore0.3100.3060.004
Marco Gonzales0.3160.317-0.001
Marcus Stroman0.2830.314-0.031
Mark Leiter0.2700.315-0.045
Martin Perez0.2950.296-0.001
Mason Englert0.3070.2960.011
Mason Thompson0.3520.2930.059
Matt Brash0.3800.2740.106
Matt Manning0.2160.276-0.060
Matt Moore0.2950.2800.015
Matt Strahm0.2750.290-0.015
Matthew Boyd0.3020.2830.019
Matthew Liberatore0.3140.3010.013
Max Fried0.3100.2910.019
Max Scherzer0.2650.279-0.014
Merrill Kelly0.2790.302-0.023
Michael Fulmer0.3040.2970.007
Michael Grove0.3640.3230.041
Michael King0.3070.3010.006
Michael Kopech0.2650.2640.001
Michael Lorenzen0.2680.290-0.022
Michael Tonkin0.2430.286-0.043
Michael Wacha0.2660.287-0.021
Miguel Castro0.2510.315-0.064
Mike Baumann0.2660.286-0.020
Mike Clevinger0.2820.2730.009
Miles Mikolas0.3090.3030.006
Mitch Keller0.3100.3020.008
Nathan Eovaldi0.2710.297-0.026
Nestor Cortes0.2910.2600.031
Nick Martinez0.2940.2910.003
Nick Pivetta0.2690.301-0.032
Nick Sandlin0.1950.256-0.061
Noah Syndergaard0.2890.316-0.027
Osvaldo Bido0.3290.3230.006
Pablo Lopez0.3130.2930.020
Patrick Corbin0.3110.3020.009
Patrick Sandoval0.3090.321-0.012
Paul Blackburn0.3510.3200.031
Paul Sewald0.2840.2580.026
Pedro Avila0.3050.310-0.005
Peter Lambert0.2960.2800.016
Phil Bickford0.2930.2740.019
Phil Maton0.2700.280-0.010
Pierce Johnson0.3630.3520.011
Quinn Priester0.2970.309-0.012
Rafael Montero0.3580.3000.058
Raisel Iglesias0.3120.2890.023
Ranger Suarez0.3270.3180.009
Reese Olson0.2550.308-0.053
Reid Detmers0.3180.3170.001
Reynaldo Lopez0.2780.2760.002
Rich Hill0.3230.3120.011
Roansy Contreras0.3180.2950.023
Robert Stephenson0.2220.274-0.052
Ronel Blanco0.2800.294-0.014
Ross Stripling0.3100.326-0.016
Ryan Brasier0.2480.293-0.045
Ryan Pressly0.2720.293-0.021
Ryan Walker0.3560.2930.063
Ryan Weathers0.3270.2870.040
Ryan Yarbrough0.3020.2890.013
Ryne Nelson0.3010.2820.019
Ryne Stanek0.2580.270-0.012
Sam Hentges0.3470.3220.025
Sam Moll0.2810.2780.003
Sandy Alcantara0.2890.299-0.010
Scott Barlow0.3260.3240.002
Scott McGough0.2720.306-0.034
Sean Manaea0.2930.307-0.014
Seranthony Dominguez0.2910.292-0.001
Seth Lugo0.2980.313-0.015
Shane Bieber0.2950.313-0.018
Shane McClanahan0.2740.296-0.022
Shawn Armstrong0.2500.293-0.043
Shintaro Fujinami0.3000.2980.002
Shohei Ohtani0.2400.286-0.046
Sonny Gray0.2950.313-0.018
Spencer Strider0.3160.2950.021
Steven Matz0.3200.2990.021
Steven Okert0.2990.2990.000
Steven Wilson0.2240.243-0.019
Taijuan Walker0.2730.296-0.023
Taj Bradley0.3120.3110.001
Tanner Banks0.2820.293-0.011
Tanner Bibee0.2870.295-0.008
Tanner Houck0.2990.2960.003
Tanner Scott0.2910.313-0.022
Tarik Skubal0.2890.2840.005
Taylor Clarke0.3690.2830.086
Taylor Rogers0.2820.2690.013
Tim Mayza0.3310.3120.019
Tom Cosgrove0.2110.244-0.033
Tommy Henry0.2780.286-0.008
Tony Gonsolin0.2350.290-0.055
Touki Toussaint0.2660.313-0.047
Trevor Gott0.3450.3020.043
Trevor Richards0.3130.2810.032
Trevor Stephan0.3180.2880.030
Trevor Williams0.3160.2980.018
Tristan Beck0.2900.2840.006
Tucker Davidson0.3670.2940.073
Ty Blach0.3460.3230.023
Tyler Anderson0.3020.2950.007
Tyler Glasnow0.2940.2940.000
Tyler Holton0.2130.305-0.092
Tyler Rogers0.2740.275-0.001
Tyler Wells0.2000.258-0.058
Tylor Megill0.3250.3000.025
Wade Miley0.2360.272-0.036
Wandy Peralta0.2200.283-0.063
Will Smith0.2620.290-0.028
Xzavion Curry0.2950.2880.007
Yennier Cano0.2840.316-0.032
Yimi Garcia0.3450.3050.040
Yonny Chirinos0.2860.316-0.030
Yu Darvish0.3190.3100.009
Yusei Kikuchi0.3150.3070.008
Zac Gallen0.3010.311-0.010
Zach Davies0.3480.3080.040
Zach Eflin0.2960.2840.012
Zack Greinke0.3000.304-0.004
Zack Littell0.2950.2920.003
Zack Thompson0.3410.3070.034
Zack Wheeler0.2920.2900.002

This is a lot of data to digest, but keep in mind it's backwards looking, or as the kids say, it's descriptive, not predictive. Not to mention, the assumption is the individual has no control over the authority of contact, or the number of line drives. It was never stated the pitcher has zero control, only that it's limited, at least compared to how able they are to induce grounders and flies.

The next step is incorporating xBABIP into formulaic projections. This obviously entails projecting batted ball distribution. Data presented last time (and linked above) illustrates that groundball and flyball rates correlate well from year to year. One approach could be to use a weighted average for groundball and flyball rate, similar to that utilized for other statistics. The line drive rate can be what's remaining after the others are computed.

This also requires projecting the component BABIP. Using the previous season's levels could suffice, as could a weighted average of a few previous seasons. For the purpose of projections, the precision isn't important since everyone gets the same treatment, so when they are compared on a relative basis, if everyone is a little too high, or a little too low, the rankings for drafting will be the same.

Some may find the above flawed, since pitchers exhibit little control over line drives. Perhaps the line drive rate should be the projected league average for everyone, then the ground ball and fly ball rates are derived from a projected GB/FB ratio.

An argument can be tendered for either method, especially when considered in light of the following: pitchers do exert some measure of influence. Determining how much is going to be subjective, just as it is with other statistics. A good projection system should allow for overriding, especially if it's a level of regression. As a simple example, if a pitcher yields 20 homers, but his xHR is 24, plugging anything between 20 and 24 into the little black box can be justified. If the prognosticator has a reason the pitcher should have surrendered 24 homers, plugging 24 in is defensible. Personally, I set regressions of this nature to 50 percent, then season to taste.

A more complex version of xBABIP can regress everything, including the pitcher's component BABIP, to league norm. This helps account for the pitcher's ability to influence their own numbers. In the case of xBABIP, my starting point will probably be a stronger regression to league mean for all of the involved factors.

The objective of this presentation is not to spur everyone to fire up Excel and crunch xBABIP for 750 pitchers expect to appear in MLB this season. By all means, if you so desire, go for it. The primary goal is to illustrate BABIP has a wider acceptable range than many assume. You don't need to set up formulas. After checking out a pitcher's BABIP, look at their batted ball distribution. Maybe you shouldn't hang onto a perceived unlucky guy or fade an assumed lucky one. Just don't give the pitcher too much credit (or blame) for the delta from expected.

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ABOUT THE AUTHOR
Todd Zola
Todd has been writing about fantasy baseball since 1997. He won NL Tout Wars and Mixed LABR in 2016 as well as a multi-time league winner in the National Fantasy Baseball Championship. Todd is now setting his sights even higher: The Rotowire Staff League. Lord Zola, as he's known in the industry, won the 2013 FSWA Fantasy Baseball Article of the Year award and was named the 2017 FSWA Fantasy Baseball Writer of the Year. Todd is a five-time FSWA awards finalist.
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