Prospect TalkScouting the Statline

Scouting the Stat Line – Accounting for BABIP Luck

This is the latest article in our Scouting the Stat Line series, following last week’s update on boom-bust prospects. Click here for our updated leaderboard of peak MLB wOBA equivalency for 2019 minor league performance (updated August 3rd). These leaderboards “translate” 2019 minor league statistics to peak MLB statistics.

As part of a continuous effort to understand minor league performance, I built a simple batting average on balls in play (BABIP) adjuster to better account for luck. Using a sample over 2,000 minor league performances this year, I simply regressed wOBA on BABIP, strikeout rate, walk rate, and isolated power. This model shows linear wOBA changes for a given change in BABIP. It’s a very simple model, and doesn’t account for BABIP’s relatively slight impact on isolated power*, but it offers a reasonable approximation of BABIP’s impact on wOBA anyway.

*It’s possible for high BABIPs to regress toward more home runs and consequently higher isolated powers rather than outs (since BABIP doesn’t capture home runs), but most of the regression likely should be toward outs.

The BABIP-adjuster is now accessible here, along with The Dynasty Guru’s MLB equivalency calculator for translating minor league statistics to major league statistics (see an introduction for the latter here). Simply plug in a minor league BABIP (perhaps league average or regressed 50% toward league average) to better account for luck and receive a new BABIP-adjusted minor league wOBA. In past validation tests, I’ve found minor league BABIP regressed 50% toward league average offers the most predictive validity for MLB performance. Next, plug in this new BABIP-adjusted minor league wOBA to receive a BABIP-adjusted peak MLB wOBA translation.

The rest of this article will highlight a few BABIP under and over-performers in the minors this year, and show how their peak MLB wOBA translation is impacted by BABIP.

Drew Waters has a remarkable .439 BABIP in the Double-A Southern League this year, and is thus an instructive example for thinking about BABIP. His high BABIP is, to some extent, indicative of an excellent hit tool—it’s a mistake to simply ignore his minor league BABIP. However, .439 is extreme and almost certainly driven by incredible fortune. Right now, Waters’ .384 minor league wOBA translates to a .399 peak MLB wOBA, which would make him among the top hitting prospects in baseball by this metric. See the chart below for context on peak MLB wOBA, the distribution of all minor leaguers this year (minimum 130 plate appearances, Single-A experience or higher).

*Old-for-level players excluded, maximum age 23 in Triple-A, 22 in Double-A, etc.

If you regress 50% toward league average, Waters’ minor league BABIP drops to a more reasonable but still excellent .371. His peak MLB wOBA drops from .399 to .359. A .371 BABIP gives Waters much-deserved credit for an excellent hit tool, but also a deserved penalty for excellent luck. I think this BABIP adjustment leads to a more reasonable approximation of his value—more a back-end top 30 hitting prospect than a back-end top 10 guy.

While Waters likely has the skills to sustain high BABIPs in the minors and majors, it’s instructive to consider what would happen to Waters’ wOBA if we assumed his high BABIPs were entirely luck-driven. If you assume his BABIP is purely luck and regress all the way to league average (.303 BABIP in the Double-A Southern League), Waters’ peak MLB wOBA drops further to .319—an average MLB hitter. You should not actually assume his BABIP is all luck–that would be dumb–but it’s an interesting Mccracken-ian thought experiment demonstrating the importance of BABIP.

Moving on from Waters, Brent Rooker’s peak MLB equivalent wOBA for his Triple-A performance drops from .353 to .330 with BABIP regressed 50% (from .417 to .370). Gavin Lux has miraculously sustained a .500+ BABIP in his first 130 plate appearances in the Pacific Coast League. Without adjustment, his peak MLB wOBA for 2019 is .427, making him one of the games’ best statistical prospects. Lux survives the BABIP adjustment too—with regressed BABIP, his peak MLB wOBA drops to .412, still quite elite. The same goes for Luis Robert: he’s been extraordinary in every way this year, and his peak wOBA (.444 unadjusted) also survives the BABIP-adjustment, dropping slightly to .417 (regressed 50%). One of this year’s biggest short-season breakouts, Kristian Robinson, sees his peak MLB wOBA drop from .427 (unadjusted) to a still elite .404 (regressed 50%)—further validation for his breakout.

Turning to BABIP underperformers, Jazz Chisholm gets a nice bump in peak MLB wOBA when luck-adjusting his .258 Double-A BABIP. He sees a .338 peak MLB wOBA go to .351 with a 50% regressed BABIP, and further to .363 with a league-average BABIP. It’s a similar story for Keibert Ruiz, whose peak MLB wOBA jumps from .322 to .338 (50% regressed BABIP) to .353 (league average BABIP)—still quite strong for a catching prospect. Kyle Tucker has had a down season compared to last year, but it doesn’t look quite as bad once his BABIP is normalized—his peak MLB wOBA goes from .336 to .363 with a league-average BABIP. Miguel Amaya has shown good plate discipline and power while being hurt by a .262 BABIP in the High-A Carolina League. His peak MLB wOBA rises from .339 to .365 with a league average BABIP—among the best performances in the minors for a catching prospect.

It’s clear that BABIP has a big impact on overall performance. Analysts should consider scouting reports on hit tool and past minor league performance to determine how much of a player’s BABIP is driven by skill rather than luck. A player like Waters checks both of these boxes, earning rave remarks for an elite hit tool, and never posting a BABIP below .360 at any level in his minor league career. Even so, it’s prudent to consider a large luck factor in abnormal BABIPs.

The Author

Jordan Rosenblum

Jordan Rosenblum

Jordan is an American living in Finland. In addition to writing for The Dynasty Guru, he's a doctoral candidate at Åbo Akademi researching explanations of income inequality, and a Workforce Strategist at OnWork Oy. His favorite baseball area is quantitative analysis of prospects.

Fun fact about Finland: they play pesäpallo here, which is like a soft-toss version of American baseball, except home runs are somehow outs.


  1. August 6, 2019 at 8:18 am

    For players like Chisholm, who has hit nearly 50% FBs this year, BABIP will remain lower than league average. Fly balls simply are easier to field and more often become outs than ground balls. In fact, of qualified players with 45%+ fly balls in the majors this year, only 3 of 17 have BABIP over .300 (Polanco .322, Rendon .321, Escobar .303). Even Mike Trout (48% FB, .296 BABIP) has a BABIP under .300. For most hitters, BABIP often hovers around .250 with such extreme FB rates (Encarnacion 49.8%/.238, Vogelbach 49.6%/.241, Ramirez 47.9%/.250, Renfroe 47.7%/.242, Odor 47.6%/.248, Bregman 45.6%/.252, Kepler 45.6%/.252). For Chisholm, his .261 BABIP is quite reasonable consider his 49.8% FB.

    • August 6, 2019 at 8:29 am

      This is a good caveat that I could have highlighted more. It supports the notion that home run hitters with lots of fly balls have lower BABIPs, bc BABIP only considers balls in play and not home runs, and bc fly balls tend to have lower BABIPs. I think it’s reasonable to conjecture Jazz’s extreme fly ball rate also may regress a bit toward average — and his BABIP may rebound but at the expense of a few homers.

      • August 6, 2019 at 2:44 pm

        It’s indeed wise to consider batted ball mix when setting expectations for BABIP and fly ball hitters do generally have lower BABIPs. I looked more into it. If you build a model of BABIP based on fly ball percentage, here are predicted BABIPS for each fly ball percentage (all non-rookie level 2019 minor leaguers included, sample size 1,250):

        10% 0.368
        20% 0.347
        30% 0.325
        40% 0.304
        50% 0.282
        60% 0.261
        70% 0.240
        80% 0.218
        90% 0.197

        Feel free to consider this table when setting a custom babip in the calculator!

        FB% was also the most significant predictor of BABIP at the MLB level (tested using 1st half of 2018 MLB FB% to predict 2nd half 2018 BABIP) besides BABIP itself.

        Further, there are 6 other hitters* in Double-A this year with a fb% within 1 percentage point of Jazz Chisholm’s fly ball rate. Their average BABIP is .280 (.284 excluding Jazz). Jazz’s BABIP regressed 50% is .281. Using the table above, a player with Jazz’s 50% fly ball rate is predicted to have a .282 BABIP. It appears the 50% regressed BABIP adjustment works OK for Jazz (also supported by BABIPs consistently above .300 before 2019).

        *here’s the list of the other hitters:
        Michael A. Taylor
        Chris Parmelee
        Nellie Rodriguez
        Jake Gatewood
        Arden Pabst
        Luke Williams

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