Investigating BABIP
"The batters are what we thought they were" (for the most part), but use cases abound on the mound
My last post on FIP got me interested in something I generally pay little attention to: BABIP.
As a curious personality, it’s always been an intriguing concept, but with so many appealing options like xBA or xSLG, I never got around to it.
The story of the 2024 Astros BABIP, at least the portion of the roster that’s returning, was not one of outliers and exceptions, but one of “they’ve been doing that their entire career” and as a team the Astros were slightly above average, registering a .299 BABIP vs .291 for the league.
First, let’s step back and provide the definition and some editorial content from FanGraphs.
Batting Average on Balls In Play (BABIP) measures how often a ball in play goes for a hit. A ball is “in play” when the plate appearance ends in something other than a strikeout, walk, hit batter, catcher’s interference, sacrifice bunt, or home run. In other words, the batter put the ball in play and it didn’t clear the outfield fence. Typically around 30% of all balls in play fall for hits, but there are several variables that can affect BABIP rates for individual players, such as defense, luck, and talent level. Hitters have more control over their BABIP than pitchers do and that lack of control for pitchers has lead to the creation of Defense Independent Pitching Statistics (DIPS).
BABIP is one of the simplest and more important sabermetric statistics, but it is also one of the most misunderstood. Understanding the factors that lead to a higher or lower BABIP is important for analyzing player performance and knowledge about the principle itself will lead you to a more nuanced appreciation of the game.
Calculation:
The BABIP equation is:
BABIP = (H – HR)/(AB – K – HR + SF)
They go on and on from there and if you’re interested you can just search “BABIP Fangraphs” and read a novel.
Does anyone else find it funny that “one of the simplest” statistics has one of the longest explanations, but shortest calculations?
That’s the kind of thing I notice.
In general, a BABIP that is significantly above the league average (.291 in 2024) or below the league average would indicate that regression is likely.
What we’ll see shortly is the parameters of “significantly above the league average or below the league average” are so wide that few batters are included, at least in 2024.
Certain players are able to outperform the league average over time, and that’s the case with the majority of Astros returning from 2024.
Unfortunately, or fortunately, depending on your point of view, that means we have to think about BABIP in the context of the individual batter.
For example, a .350 BABIP would be crazy high for Jon Singleton, but not Ben Gamel.
First the numbers:
At first glance, you would think players like Gamel, Zach Dezenzo, Yainer Diaz and Jose Altuve would be ripe for regression, but this is where the nuance comes in.
Remember above where we said certain players outperform the league BABIP average over their careers?
While the 2024 rookie Dezenzo could very well face a regression, Gamel has a .329 lifetime BABIP, Diaz .320 and Altuve .329.
They did what they have typically done - outperformed the league in BABIP.
This also is a positive for Chas and Jake, who have lifetime BABIPs of .315 and .291 respectively, but were well below those marks in 2024.
Meyers and McCormick are the only two Astros below the league average on the graph above, which means positive regression is possible, if not likely.
The ones to be concerned about are Singleton and Victor Caratini.
Singleton is a .263 lifetime BABIP hitter and bested that by 36 points last season, while Caratini outperformed his .287 career BABIP by 23 in 2024.
Considering their relative pecking order on the 2025 team, the Astros, in general, are in good shape as far as BABIP goes.
The newer bats also fall within this range, Christian Walker very easily and Isaac Paredes on the lower end.
Uses of BABIP Moving Forward
FangGraphs frames BABIP as one of the most important sabermetrics in the game, which may be a little outdated in 2025.
Still, it makes intuitive sense. We know luck is involved in many aspects of baseball. 110-mile-an-hour line drives are caught for outs, while 65 MPH dribblers go for hits.
That’s what we’ve been taught and what we hear ad nauseam on telecasts and broadcasts.
That may be true in the longer term, but not necessarily in the shorter term.
Yet there is another axiom of baseball, one that’s less tried and true, that says these screaming liners that are caught and bloops that find some grass even out. This simply isn’t true, and it especially isn’t true over the course of a few months or even an entire season. Sure, over the course of six years, the number of hard hit balls that get caught and weak grounders that find a hole will balance out, but we don’t judge players in 3,500 plate appearance samples. We judge players based on individual seasons and those lucky moments can really swing the number of hits you rack up or allow.
Things even out over a career, perhaps, but not necessarily over a week, month or even a season. Outlier seasons should be viewed as just that - something that’s not likely to be repeated.
That same article sums up why I rarely use the statistic:
For hitters, we use BABIP as a sanity test of sorts that tells us if their overall batting line is sustainable or not. Virtually no hitter is capable of producing a BABIP of .380 or higher on a regular basis and anything in the .230 range is also very atypical for a major league hitter. In other words, BABIP allows us to see if a hitter seems to be getting a boost from poor defense or good luck or getting docked for facing good defenses and having bad luck.
The span is so wide - 150 points - that no Astro fell outside of it, despite the team ranging from .263 to .350.
For me, it’s not an everyday tool, but a “sanity check” as described.
If we look up in May and Singleton is batting .321 we may want to investigate using BABIP.
The article references pitchers having less control over BABIP than batters do and that’s the angle where BABIP has already proven useful.
We identified Ronel Blanco’s abnormally low BABIP from 2024 as a hint of regression ahead and the same could be said for Tayler Scott (.230).
Thanks for reading!