BABIP stands for Batting Average on Balls in Play. Balls in play include anything that is hit into the field of play, so strikeouts, walks, and home runs are excluded. This statistic is one of the key building blocks of modern baseball analysis. It can be used to evaluate the performance of both hitters and pitchers, and it provides insight into which players may be currently performing above or below their true talent level.
The Concept of BABIP
The rate at which balls in play turn into hits tends to hover right around the same mark from year to year. The general benchmark is .300 which means the league as a whole tends to bat around .300 on balls in play each season. That number will move up and down a few points each year, of course, but it’s incredibly consistent.
So, when evaluating a single player, one useful tool is to look at his BABIP as compared to the league average. If a player is hitting .350 on his balls in play, why is that happening? Is he just getting lucky, or does he have a notable skill that helps him turn more fair balls into hits?
Over a small sample, it’s more likely that the player has been getting lucky, with some bloop hits falling in to boost his average. Over the long run, however, it becomes more likely that a player running a high BABIP has skills that allow them to perform above average, such as the ability to hit hard line drives, or speed to beat out infield hits.
The career of Ichiro Suzuki is a great example of how BABIP works. For his career, Ichiro had a BABIP of .338, which is well above the league average. And, in fact, that number was pulled down by some of his later seasons, as early in his career he was regularly above .350. Was he just getting lucky all those years? Of course not. Instead, Ichiro had a perfect combination of skills which allowed him to reach base so frequently. With great speed to beat throws to first base, and a fly ball rate that was well below average, Ichiro was destined to be a BABIP leader.
Why is BABIP Important?
While the league-wide averages are a good place to start, BABIP is even more useful once a player has established a baseline for his own performance.
Going back to Ichiro, it might have been tempting to think early in his career that his BABIP was going to come ‘back to earth.’ After a couple seasons, however, it should have been clear that this was his normal level, and he simply had the skills necessary to beat the averages.
This is also an important statistic because it can be used for pitchers in addition to hitters. That league-wide .300 BABIP baseline can be used when taking a look at pitcher performance, to see which pitchers are dramatically above or below the norm.
A pitcher giving up a .400 BABIP early in the season has probably just run into some bad luck, so there is reason to expect better results moving forward. Likewise, a pitcher running a .200 BABIP after a few starts is likely benefitting from some pretty good luck, and rougher games may be coming.
Again, just like with hitting, it’s not all about luck. Since fly balls turn into hits at a far lower rate than line drives and grounders, a flyball pitcher will usually be able to sustain a lower-than-normal BABIP.
How is BABIP Calculated?
Calculating BABIP is actually quite simple. If you would like to do the math for yourself, you can use the equation below.
BABIP = (H – HR)/(AB – K – HR + SF)
- H = Hits
- HR = Home runs
- AB = At-bats
- K = Strikeouts
- HR = Home runs
- SF = Sacrifice flies
What is a Good BABIP?
It’s tricky to identify a ‘good’ BABIP, as having a high number in this statistical category may represent a particular skill, or it may simply represent good luck (especially over a single season).
To highlight players who had the skills to maintain a high BABIP, it’s best to look at large sample sizes. The table below includes the best career BABIP numbers from 1900 to 2018 among players with at least 5,000 plate appearances, or 3,000 innings pitched.
Best BABIP Careers for Hitters, 1900 – 2018, min. 5,000 plate appearances
Best BABIP Careers for Pitchers, 1900 – 2018, min. 3,000 innings pitched
What are the Problems with BABIP?
The biggest risk with BABIP is underestimating the role of sample size. Over a short sample, there are bound to be wild deviations in BABIP, meaning those numbers aren’t going to say much about a player’s true talent level. You will want to allow for bigger sample sizes in order to draw meaningful conclusions about who has the actual talent to outperform BABIP norms, and who is just getting lucky.