Breaking Down BABIP: What Impacts Flyball BABIP for Hitters?

In a pair of recent columns, I looked into what factors have impacted flyball BABIP (or FB BABIP) and ground ball batting average for pitchers, and those analyses were linked by a common finding. Whether pitchers are allowing balls that are in play in the air or on the ground, the launch angles of those batted balls go a long way towards explaining whether they become base hits. Now I am turning my attention to flyball BABIP for hitters, and the trend continues. While flyball pull rate, average flyball distance and average exit velocity, both on flyballs and line drives combined and on flyballs alone, did not have significant relationships with FB BABIP for hitters, average flyball launch angle (FB LA) turned out to be a statistically significant factor yet again (p < .0006, r = .19)

As with the previous analyses, I looked at players over the last five seasons — in other words, every season during the Statcast era. I only included hitters who launched at least 100 flyballs in a season. The relationship between FB BABIP and FB LA for these hitters is depicted below, with a few notable outliers labeled, which I will explore in some detail just a bit ahead.

Before getting on with the analysis of this particular relationship, I should note that there was one other factor that turned out to be statistically significant, though not to the same degree as FB LA. Average spin rate of the pitches hit by a batter was the only other factor to have a relationship with FB BABIP for hitters at the p < .05 level, and it actually cleared that hurdle by a good distance (p < .003). It’s not clear to me why that relationship exists, and that will be a subject for a separate analysis. Oddly enough, there was no significant relationship between FB BABIP and average spin rate for pitchers.

The relationship between FB BABIP and FB LA for hitters is not overwhelmingly strong, but it still could have predictive power. The correlation of FB LA from a season with 100-plus flyballs to the next season with 100-plus flyballs was significant at the p < .0001 level with a Pearson’s r of .40, so it’s sticky. There has also been a lot of FB BABIP regression when a hitter’s expected FB BABIP (that is, what it would be if it were determined by FB LA) has been far higher or lower than their actual FB BABIP. Year-to-year change in FB BABIP (i.e., counting only seasons with 100-plus flyballs) correlates with the difference between FB BABIP and expected FB BABIP in the first of those seasons at the p <.0001 level with a Pearson’s r of .56.

FB LA seems to produce FB BABIP estimates that may be too low across the board, given that the differences between actual FB BABIP and expected FB BABIP, as shown in the table below, skew heavily towards the positive side. Still, if we look at the largest outliers at each end of the continuum, we get a sense of which hitters have the greatest chance of regressing.

2019 BABIP and xBABIP on Flyballs
Player FB BABIP xFB BABIP Difference
Andrew Benintendi 0.265 0.099 0.166
Kris Bryant 0.200 0.087 0.113
Rafael Devers 0.194 0.094 0.100
Michael Brantley 0.205 0.106 0.099
Ketel Marte 0.172 0.091 0.081
Xander Bogaerts 0.180 0.101 0.080
Adam Frazier 0.168 0.089 0.079
Christian Yelich 0.179 0.101 0.078
Carlos Santana 0.167 0.089 0.078
Joey Votto 0.167 0.091 0.076
Eugenio Suárez 0.175 0.101 0.074
Charlie Blackmon 0.162 0.096 0.066
Randal Grichuk 0.159 0.097 0.062
Jorge Polanco 0.152 0.091 0.061
Jose Abreu 0.081 0.102 -0.021
Josh Bell 0.075 0.098 -0.023
Mike Trout 0.069 0.095 -0.026
Rougned Odor 0.053 0.093 -0.040
Pete Alonso 0.055 0.105 -0.050
SOURCE: Baseball Savant
Min. 100 flyballs. xBABIP on flyballs calculated from regression equation where x = average flyball launch angle.

We can probably disregard three names among the first six in the table to some extent. Andrew Benintendi, Rafael Devers and Xander Bogaerts all benefit from playing home games at Fenway Park, which has consistently been one of the best parks in the majors for getting hits on flyballs in play. All three Red Sox had home splits for FB BABIP that were far higher than their road splits last season. Perhaps only Benintendi, whose differential between actual and expected FB BABIP was miles ahead of every other hitters’ differential, should be considered due for BABIP regression. However, he could make up for it by rebounding from last season’s jump of 6.8 percentage points in his strikeout rate.

We should be more concerned about regression from Kris Bryant, Michael Brantley and Ketel Marte. Granted, Bryant has a history of posting higher FB BABIPs than his FB LA would predict, but last year’s differential was 40 points higher than any of his differentials from his previous three seasons with 100-plus flyballs. As for Brantley, we can see from the graph above that he was in the sweet spot for FB LA, but he also stood far out above the crowd of other hitters who had a similar launch angle. Marte was due to improve his FB BABIP after getting two hits on 74 flyballs in play in 2018 (.027), but it’s hard to buy into last year’s .172 FB BABIP, especially since he increased his FB LA by 1.4 degrees to 36.9 degrees. I’m confident that Marte can approach another 30-homer season, but this time around, it is likely to come with a batting average in the .290s.

Pete Alonso and Rougned Odor stand out as hitters who appeared to have notched fewer flyball hits than they should have. I never gave a second though to Alonso being a below-average BABIP hitter (.280) as a rookie, as I had a notion that lumbering sluggers were more inclined to get fewer hits on balls in play. Given what the results of this analysis tell us, I will need to change that assumption. Because Alonso was among the best hitters in the majors at keeping his flies relatively low, he could have easily collected 10 or 11 hits on flyballs in play instead of going 4 for 73 (.055). That might not sound like a big deal, but projections may be cheating Alonso by 10 points or more on his batting average. Both in terms of projected value and ADP, Alonso is trailing behind Freddie Freeman by a good distance, but if you pass on taking Freeman in the second round, Alonso may represent a minimal dropoff in production a round later.

Odor’s .244 overall BABIP was also easy to ignore, as he was two years removed from recording a BABIP that was 20 points lower. However, he shaved 1.3 degrees off his FB LA from that season. Odor is still a risky second base option, but if he bounces back from last season’s 30.6 percent strikeout rate while continuing to hit flies at a more moderate angle, he just might hit .240 rather than straddle the Mendoza Line.

Al Melchior has been writing about Fantasy baseball and sim games since 2000, and his work has appeared at, BaseballHQ, Ron Shandler's Baseball Forecaster and FanRagSports. He has also participated in Tout Wars' mixed auction league since 2013. You can follow Al on Twitter @almelchiorbb and find more of his work at

Newest Most Voted
Inline Feedbacks
View all comments
2 years ago

Interesting! Somewhat validates my theory about Eduardo Rodriguez. But, in a way, does not. I am surprised that two of the top three are Red Sox lefties rather than righties. Part of my theory was that E-Rod, as a lefty, faces tons of righties, who can float stuff into the monster. But why would the lefties do that? Now I am confused. Brantley would make some intuitive sense, too, because of the Crawford Boxes, but he’s a lefty, too. No idea.

2 years ago
Reply to  cavebird

Lefties playing in Fenway learn to go the other way. Right field is very deep in Fenway and a bad place for lefty hitters. So to compensate for a deep right field the lefties learn to just go the other way and play wall ball