Batting Average on Balls in Play (BABIP) is one of the most easily understood sabermetric statistics because it can be easily calculated at home like many of the basic descriptive stats, but it is also a very powerful tool. Let’s start with the basic idea (or you can read about it at Fangraphs).
BABIP is exactly what it says it is, a player or pitcher’s batting average (or average against) on balls that are put in play, meaning that strikeouts and homeruns are subtracted from at bats in the denominator while sacrifice flies are added and homeruns are subtracted from the numerator of batting average, it looks like this:
BABIP = (H – HR) / (AB – K – HR + SF)
Sac bunts aren’t included because you’re making an out on purpose, so it doesn’t really belong given that it doesn’t reflect a hitter or pitcher’s skill.
BABIP tells you what percentage of balls hit somewhere the defense could make a play go for hits and can tell us a lot about players. For hitters, defense, luck, and skill determine your BABIP. A good defense playing against you will lower your BABIP because they will catch balls that should be hits, luck will lower or raise your BABIP because sometimes hard hit balls go right at someone, and skill will influence your BABIP because line drive hitters and speedy runners are more likely to have higher BABIPs because they hit the ball in a way that is more likely to result in hits or they leg out infield singles.
We generally think of true talent levels for hitters between .250 and .350 with average being right around .300. If you see someone deviate greatly from .300 or so, there may be a legitimate reason, but it is also very likely about luck. Hitters can influence their BABIP, but BABIP is fluky and takes a while to settle down, meaning that in small samples your BABIP can be quite different from your true talent level. This is what we mean when we say someone’s success is BABIP driven. No one can sustain a .450 BABIP for a whole season, but they can do it for two weeks and that can inflate statistics like batting average and slugging percentage in small samples.
The same is true for pitchers, but it’s even more critical. Pitchers have very little control over what happens to the baseball once it is put in play. Strikeouts, walks, and homeruns rest solely on a pitcher, but once a hitter makes contact it’s out of their hands. Most pitchers will have BABIPs close to .300 and any serious deviation from that number means there is some serious luck or defense involved. Even pitchers who are easy to hit will still have BABIPs closer to average because their defense will still get to a high percentage of balls in play.
Using BABIP is very easy. Hitters can have higher or lower BABIPs based on their skills, but they are unlikely to post very high or very low BABIPs. For example, only 14 hitters in MLB history have BABIPs above .360 for their careers and only 26 hitters since WWII have BABIPs lower than .240. What you want to do is compare a hitter’s season BABIP to their previous seasons to see if it is in line. If you’re jump from a .310 career BABIP to a .360 the next season, it’s likely due for some regression to the mean. BABIP can be predictive like this if there is no underlying change in skill.
For pitchers it’s even better. If a pitcher has a BABIP the deviates heavily from average, it’s almost certainly a function of luck or bad defense.
It’s quite straightforward. If someone’s BABIP deviates heavily from .300 and has no history of a high or low BABIP, it means you’re likely looking at something fluky. Here’s a quick demonstration to prove the point. Here is every qualifying hitter season since 1990 by BABIP:
You can see how it centers on .300 and almost never extends beyond .250 and .350. But in small samples, it can be fluky and give you weird results that can inflate your batting average or other numbers. Let’s look at the last 14 days in MLB:
You’ll notice the normalized shape, but also notice the scale across the horizontal axis. Lots of players have BABIPs in the .400 and below .200 over the last two weeks, meaning lots of players are over and underperforming their true talent thanks to luck and random variation.
The takeaway is simple. BABIP is a place to look when deciding if a player’s improved (or worse) results are coming from a real change in skill or good fortune. If the BABIP looks funky, look closer. If the BABIP looks typical, there might be something real going on.
Today is Jhonny Peralta’s 31st birthday. Most major league baseball players have their best seasons at or before they turn 30, but Peralta might be making an attempt to buck that trend. His best MLB season to date was 2011 in which he accumulated 5.0 WAR, while his best offensive season was 2005 in which he he provided 136 wRC+. I separate the two because this post is about Peralta at the plate, so his considerable improvement according to the defensive metrics over the last few years is worth separating out. Let’s take a quick peak at Peralta in 2005 and 2011:
2005: .292/.366/.520, 136 wRC+, 4.4 WAR (570 PA)
2011: .299/.345/.478, 122 wRC+, 5.0 WAR (576 PA)
His best offensive season was 2005, when he was 23, and his second best was in 2011 when he was 29. At 31, he is making a run at his best season yet. So far, he’s hitting .341/.392/.500, 139 wRC+, 2.1 WAR (195 PA). If we assume he will play 150 games based on career norms, he is set to accumulate career best 7.0 WAR.
But he likely won’t keep up this pace because this is a borderline MVP pace and he’s never done that before and players generally don’t get significantly better after age 30. A player’s performance is also not uniform over an entire season and it would be wrong to assume he will play at this pace for the rest of the year simply because that would be unlikely even if he did get tangibly better.
One of the reasons Peralta isn’t going to keep this up is because he has a very high, unsustainable Batting Average on Balls in Play (BABIP), which is a statistic we associate with luck. The standard BABIP rule of thumb is that .300 is where you expect most players to converge toward, with better hitters maintaining numbers in the middle .300s. The idea here is that when you put the ball in play, you only have control over how hard you hit it and the precise location is outside of your control. Sometimes you’ll smoke a baseball and it will be caught and sometimes a bloop hit will fall. In general, these take a a couple thousand plate appearances to balance out.
This is not to say that hitters can’t influence their BABIPs with their approach and talent level, but rather that BABIP will regress toward a player’s career norm and that small sample BABIPs can lead you to make mistaken predictions.
Jhonny Peralta’s BABIP in 2013 is .414. That’s very high. His career BABIP entering 2013 was .310, meaning it is unlikely that Peralta will be able to maintain his high BABIP, and with it, his current level of production. It’s possible that he got better, but it is not possible his true talent level is now a .414 BABIP.
The highest BABIP among active players is .367, with a number of the games’ best hitters in the .330 to .360 range. The highest modern day BABIP is Ty Cobb, coming in at .378. League average BABIP for non-pitchers over the last 10 years hovers between .294 and .305.
This is all by way of saying that Peralta’s early season success isn’t around to stay. He’s still very capable of having a great year, but it isn’t going to look like this, don’t fool yourself.
But as I gathered my thoughts last week and discovered his high BABIP, I thought, “Meh, a high BABIP post isn’t interesting. He’ll regress back toward career norms and will have a solid, 2011 type season. Nothing wrong with that, but not super interesting to write about.”
Well, then I thought to myself, perhaps I’m missing something. Perhaps Peralta’s good BABIP luck is hiding an actual improvement in his skills. Maybe he’s gotten better and luckier in his 31st year on Earth.
Peralta is walking and striking out at rates almost identical to his career rates and his Isolated Slugging Percentage (ISO) is even more identical to his career line. His triple slash line is equally buoyed this year by about 70 points all the way around (74/62/75):
If you erase the BABIP increase, he’s pretty much the Jhonny Peralta you knew. So how much, if at all, is the BABIP increase a change in skill?
Wait a second! Peralta is doing something different if you look at the results:
Peralta is hitting more line drives and more groundballs at the expense of hitting the ball in the air. This is important because line drives and groundballs are more likely to go for hits than flyballs, which could actually make his BABIP shift reasonable in direction if not in magnitude. In other words, the balls are coming off Peralta’s bat this season on a different trajectory that they did in the past. This could be something.
If we look at his spray charts from April and May from 2012 and 2013 we notice he’s using the lines more effectively this year, but the comparison I want to show you is the one between 2010 and 2013 because it shows the difference between a flyball heavy approach and a groundball heavy approach as you can divine from the graph above:
What we see here is that as Peralta has changed as a hitter, he has started to get hits to right field. Everyone knows that. He’s definitely learned to go the other way, but what is also striking to me is that he is also making fewer outs in the air to left field. He’s making fewer outs in the outfield period. He’s getting a band of hits in front of the outfielders in a way that didn’t happen in 2010.
So while Peralta’s numbers this year are great, his high BABIP means he’s not going to keep up this pace. But if you look at the batted ball data, you can see that he changing the way he makes contact to some degree and is inducing different trajectories off the bat. He’s not a 7.0 WAR player like the pace indicates, but there is reason to believe that if he continues to impress the defensive metrics, he may hit well enough to approach another 5.0 win season.