BABIP: What Do We Know?

This article is another in BtB's ongoing Saber Ed discussion. For previous articles, including an interview with ESPN's Mark Simon, see these links: IntroDefining SaberSimon Interview 1Simon Interview 2.

To be able to teach a concept to someone else, you first have to understand it yourself. That seems like such an obvious statement, but on numerous occasions, I've tried to explain an idea that I thought I knew well, only to find myself stumbling and backtracking my way through the conversation. To teach something well, you not only have to understand it, you have to know it forward and backward.

That brings me to today's subject: Batting Average on Balls In Play (BABIP). BABIP, as we all know, is one of the more important sabermetrics statistics. It's a deceptively simple statistic - hits divided by total balls in play, excluding homeruns - but explaining its implications gets difficult. To understand BABIP, you need to also understand sabermetrics concepts like DIPS theory and statistic concepts like random variation, and our understanding of BABIP is changing all the time. It's a nuanced statistic, and those nuances get glossed over or missed in many analyses and descriptions.

With those nuances in mind, here's my question for this week: what's our current understanding of BABIP? What are all the nuanced caveats? Or in other words, if you wanted to describe BABIP to a saber-newbie, how would you do so? There's research being done all the time on DIPS theory and BABIP, and while I feel fairly confident in my current understanding of BABIP, I'm also certain there are nuances I'm missing - nuances many of us are missing. 

After the jump, I'll get the conversation started with some of my own interpretation of BABIP. There are many people reading this out there that are far, far smarter than I am, so please, be gentle.

The Basics

  • BABIP can dramatically affect a hitter's batting average or a pitcher's ERA. If a large number of a hitter's balls in play go for hits, that can boost his batting average quite high. Also, if a pitcher lets up a large number of balls in play, he'll have a higher number of baserunners and will be more likely to let in runs.
  • That said, BABIP is an inherently fluky statistic, with little year-to-year correlation. That means that if a pitcher has a .370 BABIP one season, it's not a given that they'll post a similar rate the next season.
  • To estimate what a player's expected BABIP should be going forward, you should look at the player's career BABIP rates (assuming they've had three season or more in the majors) and use that for a baseline. League-average BABIP rates are normally around .290-.310, but certain players may have career rates slightly lower or higher than that. If a player hasn't had more than three seasons in the majors, it's best to estimate their expected BABIP as league-average.
  • EDIT: Batters have way more control over their BABIP rates than pitchers (h/t Dan Turk). I should have made this point clearer initially, but when estimating a player's future BABIP, you should be sure to regress pitcher's BABIP rates much more. They're more likely to perform close to league-average, while batters are more likely to perform closer to their career average.
Things Up For Debate
  • Why do players over- or under-perform on single-season BABIP rates? Why does BABIP fluctuate so much? We tend to simplify and call this fluctuation "luck" or "random variation", but I see it as a combination of three influences:

    a) Defense - If a pitcher has a collection of stiffs fielding behind him, then he should let up more hits on balls in play than a team with a superior defense. Also, defensive shifts can help or hurt players. As an exaggerated example, a batter that consistently hits into a shift may have a lower BABIP than a typical player.

    b) Minute Changes in Talent Level - Over the course of a season, players can go through periods of adjustment (AKA "slumps"). Maybe pitchers adjust to a weakness that a batter has, and the batter starts making less solid contact and getting fewer hits. Maybe a pitcher is off with their mechanics or batters have learned to adjust to certain pitches, and the pitcher leaves more hittable pitches over the plate than normal. The batters make more solid contact, resulting in a higher BABIP for the pitcher.

    c) Luck - Sometimes, even with a great defense, bloop hits can fall in. A batter can turn a nasty pitch into a dribbler that just sneaks past the first baseman. Hits can fall in despite the best pitches and the best defenses - that's just the game.

    d) Remember: regardless of the reason why a player's BABIP is fluky, they are still likely to regress the following season. Team defenses change, players constantly make adjustments and improve their skill levels, and luck / random variation balances out.  
  • Line drives go for hits more often than flyballs groundballs, and flyballs groundballs go for hits more often than groundballs flyballs (h/t garik16).
  • Fast hitters can sustain slightly higher-than-average BABIP rates because of their speed.
  • Groundball pitchers have a lower BABIP on groundballs than other pitchers.
  • Can certain profiles of pitchers have a better-than-average true talent BABIP?
Am I correct in suggesting that BABIP can be influenced by small adjustments in a player's talent level? This makes intuitive sense to me, but I'm not sure if I've heard it discussed elsewhere. The trick is, it's almost impossible to untangle how much of a given BABIP spike is talent-driven versus luck-driven versus least, until we have Field F/x and more advanced fielding constructs.
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