Continuing Our ‘xSeries’ By Estimating BABIP, Using StatCast and BIS Data

(Title photo courtesy of Keith Allison,

Recently, Breaking Blue has used batted ball and plate discipline data to get closer to objective performance indicators of isolated power and strikeout and walk rates. The idea of this exercise is not, as similar exercises are often interpreted as, to estimate the player’s true-talent or to project him going forward. These xSeries measures aim to inform us of how the player has played, objectively, in the past. If the metric is based on talent-oriented statistics that stabilize quickly, we should have a good idea of the player’s talent level and how he will perform going forward. The publicly available statistics are getting granular enough that the performance-based metrics we have are much more tuned to talent, and that is exciting. But the xSeries is perhaps best used as one tool of many that inform a baseball observer of how well a player is playing.

Today, we’re looking at Batting Average on Balls in Play, or BABIP. BABIP has different levels of relevance for batters and pitchers so knowing what to look for can be confusing. Pitchers generally run BABIPs that regress to league-average. Hitters for the most part cluster around the average (which is usually .300 or so) but there are many players who establish rates far above or below the average. Starling Marte and Paul Goldschmidt are perhaps real .350+ BABIP players. Hitters who hit a lot of fly balls, particularly infield flies (such as Toronto’s own Edwin Encarnacion), can have true-talent BABIPs in the .260s range.

In terms of utility, infield flies are essentially strikeouts. Some hitters who strike out infrequently but pop out a lot can have peripherals that look great on the surface (few strikeouts!) but their rate of wasted plate appearances is pedestrian all the same. Ground balls go for hits more often than fly balls, although the latter can lead to much more damaging hits, namely home runs. Speed and the velocity at which batted balls are struck are obvious important contributors to BABIP.

So, to continue the xSeries, I ran a regression involving StatCast batted ball data, the new Baseball Information Solutions data posted on Fangraphs, and basic batted ball distributions. StatCast data isn’t comprehensive but it is the important component here (other xBABIPs have been created in the past but StatCast data is brand new and there are gains to be made by knowing exit velocities and distances), so the minimum threshold for inclusion in this exercise was determined by it — 85 at-bats of StatCast data.

The metrics included in xBABIP are:

Maximum exit velocity at which a batter has struck a ball. This may seem arbitrary, but the samples are large enough that this reasonably represents the upper limit of a hitter’s strength)

Average fly ball or line drive exit velocity.

FB%, GB% and IFFB% (fly ball, ground ball and infield fly rates per batted ball)

Speed score, developed by Bill James.

Hard%, Pull% and Oppo%. The percentage of batted balls that have been hit, pulled or struck to the opposite field, classified by Baseball Info Solutions.

Here’s an illustration of how the values matched up. It’s not really the general fit that’s important to look at, since there will naturally be a great fit as the graph is the product of a regression. Note how most players tend towards the middle area and that ‘errors’ (the distance between the points and the line) appear to be normally distributed. The adjusted R^2 value (which adjusts down for the number of covariates) is.528. Fits with better adjusted R^2 values were attainable when additional covariates were considered, but the chosen combination probably provides a better glance at talent. It would have been nice to have multiple seasons of data to compare the performance and peripherals of players from season-to-season by my metric, but we only have the limited 2015 StatCast data. This would help us isolate the peripherals that are talent-based.

xBABIP Chart

Note that the axes don’t begin at zero.

And here’s the comprehensive table, with each player in the sample. Delta is BABIP – xBABIP and Quotient is BABIP/xBABIP.

Brock Holt0.3890.4020.0131.03
Billy Burns0.3850.377-0.0080.98
Chris Colabello0.3840.4530.0691.18
Jorge Soler0.3810.3830.0021.01
Brandon Belt0.3740.345-0.0290.92
DJ LeMahieu0.3740.37401
George Springer0.3730.354-0.0190.95
Mike Trout0.3730.341-0.0320.91
Paul Goldschmidt0.3690.4040.0351.09
Yonder Alonso0.3640.362-0.0020.99
Jason Kipnis0.3630.3850.0221.06
Yasmany Tomas0.3610.3990.0381.11
Dee Gordon0.3590.4210.0621.17
Jose Iglesias0.3550.3580.0031.01
Anthony Gose0.3520.3720.021.06
Nick Markakis0.3510.344-0.0070.98
Ryan Braun0.3510.264-0.0870.75
Freddie Freeman0.3470.3560.0091.03
Matt Holliday0.3470.3670.021.06
Chase Headley0.3450.303-0.0420.88
Joe Mauer0.3450.295-0.050.86
Matt Carpenter0.3440.343-0.0011
Avisail Garcia0.3430.3480.0051.01
Miguel Cabrera0.3420.3860.0441.13
Cory Spangenberg0.340.307-0.0330.9
Robinson Cano0.340.284-0.0560.84
Justin Turner0.340.338-0.0020.99
Bryce Harper0.340.370.031.09
Gerardo Parra0.3390.3430.0041.01
Francisco Cervelli0.3390.3880.0491.14
David Peralta0.3380.291-0.0470.86
Brandon Phillips0.3360.313-0.0230.93
Adeiny Hechavarria0.3360.333-0.0030.99
Joey Votto0.3340.331-0.0030.99
Anthony Rizzo0.3340.309-0.0250.93
Jon Jay0.3340.264-0.070.79
Martin Prado0.3340.298-0.0360.89
Brett Gardner0.3310.3420.0111.03
Adam Eaton0.3310.28-0.0510.85
Lorenzo Cain0.3310.3390.0081.02
Eric Hosmer0.330.3370.0071.02
Yunel Escobar0.330.3720.0421.13
Cameron Maybin0.3290.340.0111.03
Ben Revere0.3280.321-0.0070.98
Josh Donaldson0.3280.324-0.0040.99
Prince Fielder0.3270.3610.0341.1
Brandon Crawford0.3270.324-0.0030.99
Ender Inciarte0.3270.315-0.0120.96
Odubel Herrera0.3270.3310.0041.01
Justin Upton0.3260.3280.0021.01
Jose Abreu0.3260.321-0.0050.98
Matt Kemp0.3250.311-0.0140.96
Jhonny Peralta0.3250.3480.0231.07
Denard Span0.3240.315-0.0090.97
Lucas Duda0.3240.32-0.0040.99
Charlie Blackmon0.3240.317-0.0070.98
Yadier Molina0.3230.330.0071.02
Andrew McCutchen0.3230.317-0.0060.98
James McCann0.3220.3250.0031.01
Chris Owings0.320.31-0.010.97
Matt Duffy0.320.3290.0091.03
Alex Rodriguez0.320.313-0.0070.98
Will Venable0.3190.3470.0281.09
Andrelton Simmons0.3190.29-0.0290.91
Jace Peterson0.3190.3390.021.06
Brett Lawrie0.3180.3810.0631.2
Christian Yelich0.3180.295-0.0230.93
Michael Cuddyer0.3170.320.0031.01
A.J. Pollock0.3170.3440.0271.09
Michael Bourn0.3160.313-0.0030.99
Kolten Wong0.3150.306-0.0090.97
Jason Castro0.3150.281-0.0340.89
Jimmy Paredes0.3150.380.0651.21
J.D. Martinez0.3150.3220.0071.02
Nori Aoki0.3140.3330.0191.06
Mike Aviles0.3130.277-0.0360.88
Kevin Kiermaier0.3130.303-0.010.97
Austin Jackson0.3130.3180.0051.02
Ryan Howard0.3130.274-0.0390.88
Jose Reyes0.3130.3150.0021.01
Troy Tulowitzki0.3130.370.0571.18
Mike Moustakas0.3130.3510.0381.12
Carlos Gonzalez0.3120.26-0.0520.83
Elvis Andrus0.3120.265-0.0470.85
Jung Ho Kang0.3120.3180.0061.02
Yasmani Grandal0.3120.298-0.0140.96
Yangervis Solarte0.3110.27-0.0410.87
Adrian Gonzalez0.3110.3170.0061.02
Manny Machado0.3110.3250.0141.05
Will Middlebrooks0.3110.244-0.0670.78
Buster Posey0.3110.289-0.0220.93
Howard Kendrick0.3110.265-0.0460.85
A.J. Pierzynski0.310.282-0.0280.91
Josh Harrison0.3090.3180.0091.03
Pedro Alvarez0.3090.259-0.050.84
Daniel Murphy0.3090.291-0.0180.94
Curtis Granderson0.3090.291-0.0180.94
Logan Morrison0.3090.275-0.0340.89
Dexter Fowler0.3080.286-0.0220.93
Marcell Ozuna0.3080.3370.0291.09
Russell Martin0.3080.3210.0131.04
David DeJesus0.3070.3310.0241.08
Jason Heyward0.3070.3170.011.03
Kris Bryant0.3070.3860.0791.26
Todd Frazier0.3070.284-0.0230.93
Kevin Pillar0.3070.304-0.0030.99
Joe Panik0.3060.3290.0231.08
Dave Freese0.3050.3-0.0050.98
Xander Bogaerts0.3050.330.0251.08
Starling Marte0.3040.3210.0171.06
Johnny Giavotella0.3040.3050.0011
Adam Jones0.3040.3180.0141.05
Carlos Gomez0.3030.3380.0351.12
Jean Segura0.3030.3110.0081.03
Wilson Ramos0.3020.291-0.0110.96
Mitch Moreland0.3020.3460.0441.15
Adam Lind0.3010.3130.0121.04
Mookie Betts0.3010.289-0.0120.96
Joc Pederson0.3010.299-0.0020.99
Kole Calhoun0.30.3130.0131.04
Addison Russell0.30.3610.0611.2
Gregory Polanco0.30.298-0.0020.99
Justin Maxwell0.30.266-0.0340.89
Alcides Escobar0.30.3040.0041.01
Evan Longoria0.2990.3190.021.07
Brayan Pena0.2990.3180.0191.06
Kendrys Morales0.2990.320.0211.07
Jake Marisnick0.2980.3080.011.03
Giancarlo Stanton0.2980.286-0.0120.96
Juan Lagares0.2980.330.0321.11
David Ortiz0.2980.241-0.0570.81
Dustin Pedroia0.2980.3250.0271.09
Sam Fuld0.2970.256-0.0410.86
Michael Brantley0.2960.3030.0071.02
Brad Miller0.2960.272-0.0240.92
Juan Uribe0.2960.3180.0221.07
Josh Reddick0.2940.292-0.0020.99
Hanley Ramirez0.2940.265-0.0290.9
Erick Aybar0.2940.293-0.0011
Nelson Cruz0.2940.3650.0711.24
Ryan Zimmerman0.2940.228-0.0660.78
Alex Gordon0.2920.3120.021.07
Logan Forsythe0.2920.3250.0331.11
J.T. Realmuto0.2910.259-0.0320.89
Andre Ethier0.2910.286-0.0050.98
Nick Ahmed0.2910.289-0.0020.99
Shin-Soo Choo0.2910.289-0.0020.99
Pablo Sandoval0.290.2970.0071.02
Billy Butler0.290.288-0.0020.99
Cody Asche0.290.3030.0131.04
Jose Altuve0.290.3030.0131.04
Danny Espinosa0.2890.3170.0281.1
Mark Trumbo0.2890.268-0.0210.93
Alexei Ramirez0.2890.249-0.040.86
Wilmer Flores0.2880.241-0.0470.84
Kyle Seager0.2870.274-0.0130.95
Ryan Goins0.2860.271-0.0150.95
Albert Pujols0.2860.23-0.0560.8
Carlos Beltran0.2850.2920.0071.02
Seth Smith0.2850.3140.0291.1
Adam LaRoche0.2850.3040.0191.07
Ian Desmond0.2850.2990.0141.05
Jay Bruce0.2840.272-0.0120.96
Alexi Amarista0.2840.252-0.0320.89
Miguel Montero0.2840.272-0.0120.96
Eric Sogard0.2840.2980.0141.05
Ian Kinsler0.2830.3090.0261.09
Neil Walker0.2820.3090.0271.1
Stephen Vogt0.2820.3180.0361.13
Didi Gregorius0.2810.262-0.0190.93
Melky Cabrera0.2810.275-0.0060.98
Chris Coghlan0.280.2830.0031.01
Yoenis Cespedes0.2790.3580.0791.28
Jordy Mercer0.2790.247-0.0320.89
Starlin Castro0.2790.3060.0271.1
Billy Hamilton0.2780.257-0.0210.92
Adrian Beltre0.2780.258-0.020.93
Steven Souza Jr.0.2780.3010.0231.08
Brian McCann0.2770.2910.0141.05
Delmon Young0.2760.3120.0361.13
Brandon Moss0.2760.2780.0021.01
Lonnie Chisenhall0.2760.231-0.0450.84
Victor Martinez0.2760.2780.0021.01
Freddy Galvis0.2750.3080.0331.12
Brian Dozier0.2740.2790.0051.02
Carlos Ruiz0.2740.268-0.0060.98
Angel Pagan0.2730.3170.0441.16
Mark Canha0.2720.264-0.0080.97
Marwin Gonzalez0.270.2830.0131.05
Salvador Perez0.270.2850.0151.06
Marcus Semien0.270.320.051.19
Jimmy Rollins0.2690.228-0.0410.85
Leonys Martin0.2680.280.0121.04
Eduardo Escobar0.2680.3010.0331.12
Evan Gattis0.2660.247-0.0190.93
Omar Infante0.2660.2680.0021.01
Nick Castellanos0.2650.2750.011.04
Nolan Arenado0.2640.2730.0091.03
Jose Bautista0.2640.255-0.0090.97
Ben Zobrist0.2620.242-0.020.92
Mark Teixeira0.260.224-0.0360.86
Mike Napoli0.2590.232-0.0270.9
Conor Gillaspie0.2590.2910.0321.12
Aramis Ramirez0.2590.22-0.0390.85
Torii Hunter0.2590.2860.0271.1
Derek Norris0.2590.2990.041.15
Trevor Plouffe0.2570.2770.021.08
Carlos Santana0.2570.227-0.030.88
Chris Carter0.2560.2710.0151.06
Zack Cozart0.2540.2580.0041.02
Marlon Byrd0.2480.233-0.0150.94
Edwin Encarnacion0.2480.247-0.0011
Rene Rivera0.2420.209-0.0330.86
Chris Davis0.2410.2830.0421.17
Asdrubal Cabrera0.2320.2510.0191.08
Matt Joyce0.230.225-0.0050.98
Luis Valbuena0.230.168-0.0620.73
Chase Utley0.2290.186-0.0430.81
Kurt Suzuki0.2280.2430.0151.07
Dustin Ackley0.220.209-0.0110.95
Stephen Drew0.1740.1780.0041.02

Brock Holt is our xBABIP king!

The top under-performers (players whose batted ball peripherals suggest they should be carrying higher BABIPs) by Delta are Ryan Braun, Jon Jay, Will Middlebrooks, Ryan Zimmerman and Luis Valbuena.

Braun has been a different player since being suspended for using performance enhancers a couple years ago. A chronic thumb injury has clouded his health situation since last year and he has taken a significant step back on offense. One of the very best hitters in baseball from 2007-2012, Braun now projects as a first-division player who isn’t a star. This is in large part due to BABIP. Braun was consistently producing BABIPs in the .330-.360 range but he’s at .264 right now and Fangraphs Depth Charts has him at .308 going forward. Interestingly, Braun’s batted ball peripherals suggest he has been hitting the ball well enough to get him back in that elite zone, at a .351 mark.

Luis Valbuena was obviously going to feature highly on this list as his ridiculous batting line is well-publicized, but his xBABIP still isn’t very palatable at .230. He hits an utter ton of fly balls, with an average-ish infield fly rate. He’ll keep swatting home runs but the batting average (and on-base percentage) may not quite normalize to the point that he’s an above-average hitter. Valbuena’s career path has been strange to say the least.

The top over-performers are Kris Bryant, Yoenis Cespedes, Nelson Cruz, Chris Colabello and Jimmy Paredes.

These players have BABIPs that any observer would say are obvious to regress. Cespedes has the lowest actual BABIP of the five at .358. That’s a figure that some hitters can support. But Cespedes has been slightly below-average in terms of how well his batted ball peripherals suggest he is hitting the ball. Chris Colabello’s bizarre season definitely warrants its own article and I’d like to break down his likely offensive and overall values going forward in a lengthy article sometime soon.

Next up in the xSeries, Breaking Blue will bring it all together by presenting xwOBA and xSlashLine.

How Well Have Hitters Been Hitting, Objectively? | Expected wOBA and Slash Lines
Down On the Farm - Daniel Norris With an Encouraging Start

Author: Spencer Estey

Spencer has been a baseball fan since a young age and, being from Toronto, he has always been partial to the Blue Jays. He is a statistics major at the University of Waterloo and is intensely interested in the analytic aspect of the game. Spencer follows baseball by watching countless games each season, reading various advanced analysis sites, playing in deep dynasty fantasy leagues and discussing the game with fellow fans.

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