Is BABIP An Accurate Predictor of Batted Ball Luck?

When analyzing batters in major league baseball, a stat that has risen to prominence recently has been BABIP – or batting average on balls in play. Many writers, analysts, or media have used the statistic as a measure of how “lucky” a batter has been. With the average BABIP in the MLB around .300 every season, BABIP numbers for batters that are significant percentage points from the mean are often regarded to as lucky or unlucky. But after the ball leaves the bat, there are still factors at play besides luck that will decide if a batter records a hit. Two measurable marks that could affect BABIP are sprint speed and exit velocity. If a runner can beat out a higher percentage of groundballs, it is logical that his batting average will rise. On the same token, if a batter hits the ball at a greater velocity, there is less time for fielders to react and get the batter out.

Taking a cursory look to see if these areas really do effect BABIP, I took a look at top performers in exit velocity as well as sprint speed.

This graph shows the top 10 batters in exit velocity, and where there BABIP ranked in comparison to the league average of .300.

BABIP Avg. EV.png

Player Average Exit Velocity Average Sprint Speed          BABIP
Byron Buxton 85.3 30.2 0.339
Billy Hamilton 79.1 30.1 0.313
Bradley Zimmer 86.3 29.9 0.328
Dee Gordon 79.7 29.7 0.354
Amed Rosario 84.7 29.7 0.33
Delino Deshields 79.3 29.6 0.358
Raimel Tapia 80.9 29.5 0.361
Keon Broxton 86.5 29.5 0.323
Manuel Margot 84.7 29.3 0.309
Rajai Davis 85.3 29.3 0.297

Nine out of the top 10 players ranked by Statcast’s sprint speed finished higher than the league average BABIP of .300. The average of the 10 individuals combined to a .331 BABIP.

It seemed logical next to look at the leaders in BABIP in the 2017 season and to compare their BABIP marks to the league average in exit velocity and sprint speed. First, we take a look at the top 20 qualified BABIP performers in the MLB in comparison to their average exit velocity.

Top MLB BABIP.png13 out of 20 of the batters hit the ball with an average velocity above the league-wide median, which was calculated by eliminating all players with less than 60 batted ball events. This effectively took pitchers and short-term minor league fillers out of the equation. The average exit velocity of these 20 players was 88.07 MPH, compared to the median of 87.0 MPH.

BABIP vs. Sprint Speed.png

16 out of 20 players were above or at the league average sprint speed mark of 27.0 MPH, provided by MLB’s statcast. The average sprint speed of the top 20 BABIP performers was 27.658 MPH.

Player Average Exit Velocity Average Sprint Speed          BABIP
Avisail Garcia 90.1 28.1 0.392
Charlie Blackmon 86.8 28.1 0.371
Jose Altuve 85.6 28 0.37
Tommy Pham 89.3 28.7 0.368
Tim Beckham 88 27.5 0.365
Domingo Santana 89.4 27 0.363
Chris Taylor 86.7 28.6 0.361
Aaron Judge 94.5 27.7 0.357
Marcell Ozuna 90.7 28.2 0.355
Dee Gordon 79.7 29.7 0.354
Cesar Hernandez 84.5 28.7 0.353
Corey Seager 89.7 27.2 0.352
Trey Mancini 88.6 26.9 0.352
Eric Hosmer 89.5 27.5 0.351
DJ LeMahieu 88.6 27.2 0.351
Joe Mauer 89.9 26.7 0.349
Buster Posey 88.6 25.6 0.347
Javier Baez 86.8 28.3 0.345
Odubel Herrera 86.6 27.5 0.345
Marwin Gonzalez 87.7 26.5 0.343

Of these 20 players, each was above the league average in either sprint speed or exit velocity. Eight of 20 were above average in both categories.

Looking at the bottom 20 finishers in BABIP (of 144), the results are a bit surprising. It must also be noted that though all these players had a lower BABIP, each had enough value to be played regularly. 17 out of 20 exhibited better than average exit velocities, while only six had an above average sprint speed.

Player Average Exit Velocity Sprint Speed          BABIP
Edwin Encarnacion 89 25.5 0.271
Logan Morrison 88.5 26.9 0.268
Mookie Betts 88.4 28.1 0.268
Manny Machado 90.8 27 0.265
Jose Reyes 83.6 27.9 0.263
Mike Moustakas 87.2 25.5 0.263
Matt Joyce 87.9 26.4 0.263
Carlos Beltran 87.5 26 0.263
Kyle Seager 88.1 26.5 0.262
Alex Gordon 85.6 26.7 0.261
Yangervis Solarte 85.5 26.5 0.258
Joey Gallo 92.7 27.6 0.25
Albert Pujols 88.5 23 0.249
Scott Schebler 88.8 28.4 0.248
Ian Kinsler 86.2 25.6 0.244
Jose Bautista 88.4 25.6 0.239
Maikel Franco 88.7 26.5 0.234
Curtis Granderson 87.5 26.6 0.228
Todd Frazier 87.6 26.5 0.226
Rougned Odor 88.4 28 0.224

17 out of 20 exhibited better than average exit velocities, while only six had an above average sprint speed. Only two were below average for sprint speed and exit velocity, while five were above average in both categories.

What do these graphs and charts tell us about BABIP? None of them show a strong correlation, but the small sample sizes have the potential to be deceiving. According to the data, it appears Sprint Speed may have more influence on a batter’s BABIP than exit velocity.

However, the reason that many players with higher exit velocity ranked at the bottom of the BABIP leaderboard may be because of how they are hitting the ball.  A 90 MPH line drive would certainly be the hardest for the defense to make a play on, while a 90 ground ball has the potential to present challenges as well. However, a 90 MPH pop up will most certainly be caught, a possible reason why the bottom of the BABIP leaderboard is littered with fly ball hitting, slower individuals. Even though many of these hitters are hitting with a high exit velocity, the number of fly balls they are hitting may be leading to a lower BABIP. The exit velocity statistic may correlate more closely to BABIP if it could be combined with line drive rate or launch angle statistics.

So is BABIP an accurate predictor of batter’s luck? While it can give some clue, especially when compared to career BABIP marks, there is more that goes into a high or low BABIP than just luck. To more accurately hypothesize what can cause a high or low BABIP, bigger sample sizes will need to be taken, and other stats such as launch angle and line drive rate will also need to be taken into account.

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About Cole Schuessler

I recently finished my undergraduate coursework in sport management at Concordia University, St. Paul, also adding a minor in analytics. I am now pursuing a masters in degree in sport management at CSP while working as graduate assistant in athletics. Since discovering the stats page of as an elementary school kid, I have loved the statistical side of sports and hope to one day work in the field of sports analytics.
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