“Do you know what the difference between hitting .250 and .300 is? That's 25 hits...25 hits in 500 at bats is 50 points...ok. There's 6 months in a season. That's about 25 weeks.That means if you get just one extra flare a week, just one. A gork, you get a ground ball, you get a ground ball with eyes! You get a dying quail, just one more dying quail a week and you're in Yankee Stadium.” – Crash Davis (Bull Durham, 1988)

That, ladies and gentlemen, is BABIP in a nutshell. Sometimes it really is better to be lucky than good. One of the primary ways to measure the role luck plays in hitting and pitching is BABIP. That being said, there is much more to BABIP than just luck. Understanding BABIP and what goes into it can be very important for fantasy players looking for an advantage over their league mates.

What is BABIP?

BABIP is an acronym for batting average on balls in play. BABIP measures how often batted balls fall for hits. One way to think of BABIP is batting average if we take out strikeouts. BABIP is only measuring balls put in play.

For the purposes of BABIP, think of balls in play as batted balls that a fielder could conceivably make a play on. A home run is not considered a ball in play. That leads us to the formula for calculating BABIP. BABIP = (H – HR)/(AB – K – HR + SF).

So as we stated above, home runs do not count, and neither do strikeouts, so both are subtracted from the hits and at-bats. Sacrifices do count, however, so they are added to at-bats.

How do we use BABIP for fantasy?

The lazy answer is that you look for players with an extreme BABIP (either high or low), expect that players’ BABIP to normalize going forward. The league average BABIP is around .300, and if we didn’t know anything else, we could expect an extreme BABIP to move closer to .300 over time.

That approach can even work, to some degree. All else being equal, BABIP does tend to regress to the mean over time. However, there are two problems with this approach.

BABIP is not entirely a function of luck. Players, especially batters, do have some control over whether or not they have a high (or low) BABIP. Before you assume a player’s (or team’s) BABIP has changed largely due to luck, you have to make sure nothing else has changed that could account for the change in BABIP.

This obviously requires a greater understanding of BABIP and what contributes to it. Once we understand the factors that can contribute to BABIP, we can look at them to try to make sense of a player’s BABIP.

In the end, assuming a player’s circumstances are largely unchanged, we would expect that player’s BABIP to regress to his career average, not the league average. Lorenzo Cain has a .344 BABIP through 2793 career at-bats. If we are projecting his BABIP for 2018 we should start at .344, not the league average of .300. If he has consistently been better than league average in his career, the odds are he will continue to do so going forward.

Before we get into the factors that contribute to BABIP, there are a few important baselines we can establish for fantasy players as they look at BABIP. As we mentioned above, the league average for BABIP is around .300. No one has ever had a career BABIP over .380, and the highest career mark for an active player is Miguel Sano at .362. Conversely, Drew Butera has the lowest active BABIP at .247. Maikel Franco is next at .260. If you see a BABIP that falls below .260 or above .360, it should probably cause you to raise an eyebrow or even two. 

The other important rule of thumb for fantasy players with regards to BABIP is that hitters have more control over BABIP than pitchers do. Hopefully, this will become clearer once we look at the various factors that contribute to BABIP. Once the pitcher throws the ball, the result is (please forgive the pun) out of his hands. What happens next is up to the batter and the fielders and the fates.

What contributes to BABIP?

There are five main factors that contribute to BABIP. They are: the type of contact, the quality of contact, the placement of contact, ballpark and luck. Let’s take an in-depth look at each one in turn.

  1. Type of Contact

Not all batted balls are created equal. Line drives go for hits more often than groundballs, which are hits more often than flyballs. This should make sense intuitively, even if you have never really thought about it or looked at the numbers. Let’s illustrate the point with a brief thought experiment.

Picture yourself at home on your couch on a warm summer evening watching your local baseball team on television. Your favorite player comes to bat, and on the first pitch, he hits a line drive to the left side. You lose sight of the ball for a split second while the broadcast changes to a different camera shot, and the next thing you know the line drive has been caught by the shortstop. How do you react? Are you surprised? Perhaps disappointed?

Now, picture the same exact scenario, except instead of a line drive, the batter hits a ground ball, or a fly ball, that goes for an out. Are you disappointed? Are you at all surprised? That is because we know from watching hours of baseball that line drives go for hits more than other contact. That is why for years and years Little League coaches have tried to get their players to hit line drives.

What this means for our purposes is, in general, players who hit a lot of fly balls will have a lower BABIP. Players with a lot of line drives do better, especially if they combine those line drives with a good number of groundballs.

One of the reasons groundballs are better for BABIP than flyballs is groundballs give the runner an opportunity to beat out the throw for a base hit. This is one of the biggest reasons guys like Odubel Herrera, Christian Yelich, Cesar Hernandez and Austin Jackson are near the top of the career BABIP list for active players. Trea Turner, with a 48.1% groundball rate and a .352 career BABIP, could wind up being a similar player over his career, at least until he starts to slow down.

  1. Quality of Contact

This is another factor that is obvious when you think about it. The more often you hit the ball hard, the more hits you will get in the long run. Over the course of a few months or even an entire season you may hit the ball hard with nothing to show for it, but if you keep that up long enough, you will eventually get rewarded.

It makes some degree of sense that Miguel Sano is the active leader in BABIP and also the active leader in hard hit percentage. It would be reasonable to look at young hitters with high strikeout rates like Sano, Aaron Judge and Domingo Santana and conclude their batting averages will drop significantly in 2018. That being said, if they keep hitting the ball hard consistently, they may not suffer much of a drop in batting average even if their strikeouts remain the same. Kris Bryant looked like a prime candidate for batting average regression after he had a .275 average, .378 BABIP and 30.6 K% as a rookie. He has actually batted batter than .290 in each of his two subsequent seasons, and while much of the credit for that belongs to a lower strikeout rate, it also helps that he continues to make hard contact.

In addition to hard hit percentage, another way to possibly measure quality of contact is average exit velocity. Aaron Judge led the league in average exit velocity last season, and he finished eighth in BABIP. Miguel Sano was fourth in exit velocity in 2017. Neither hard hit percentage nor average exit velocity isa perfect measure of quality of contact, but they can give you a pretty good idea, especially when you consider the type of contact as well.

  1. Placement of Contact

In our Draft Guide article Stat Splits that Matter, we discussed splits against the shift, and the way that some players, especially young ones who didn’t face a shift in the minors, will have to adjust to shifting defenses in the majors. Players like Matt Adams and Mark Teixeira saw their batting averages (and their BABIPs) fall quite a bit because teams began shifting more against them. This is just one example of the ways in which defense has a huge impact on BABIP, especially for pitchers.

As we discussed above, a pitcher’s BABIP allowed depends quite a bit on that pitcher’s defense. The pitcher can control the type and quality of contact to some degree, but they can’t control if the shortstop can get to the groundball up the middle and make a strong enough throw to get the runner at first base.

Clayton Richard is a great example of this. He allowed the highest BABIP in baseball last season among qualified starters at .351. He was also third in MLB in GB% at 59.2. That could be a recipe for success on some teams—he had a 3.83 ERA in 2015 and a 3.33 ERA in 2016—but the Padres had the second lowest UZR in baseball in 2017, and poor defense behind him almost certainly deserves some of the blame for Richard’s BABIP. If Richard winds up on a better defensive team, or even if he goes back to the Padres after they added Freddy Galvis, he could have better fantasy numbers without doing much differently on the mound.

When we consider BABIP for hitters, it is useful to look at batted ball direction statistics (Pull%, Cent%, Oppo%). Pull heavy hitters are easier to defend, and a low BABIP for those players may be a sign of things to come. The batters with the three lowest BABIPs in 2017—Roughned Odor, Todd Frazer and Curtis Granderson—were all in the top eight in Pull%. Odor especially is a great example of a player whose BABIP (and batting average) may not bounce back much in 2018.

In his first two seasons combined, Rougned Odor batted against the shift 32 times in 812 at-bats. He faced a shift 224 times in 605 at-bats in 2016 and 332 times in 607 at-bats in 2017. Odor had a .281 BABIP against the shift in 2016 and a .233 BABIP against the shift in 2017.

Prior to last year, Rougned Odor had never batted below .259 for a season. Unless he goes back to pulling the ball less (his Pull% has gone up in each of his MLB seasons), he will be lucky to get back to .259 in 2018.

  1. Ballpark

It probably isn’t a coincidence that five of the 14 active pitchers with the highest career BABIPs have called Coors Field home for significant portions of their careers. Much like fielding and positioning are important for BABIP, so are a ballpark’s dimensions. Because Coors Field has such a big outfield, a lot of flyballs and line drives drop in for hits that would probably be outs in other ballparks.

A lot is being made of Tyler Chatwood’s home/road splits now that he is trading Coors Field for Wrigley Field, and for good reason. That being said, he might still allow a lot of home runs in 2018. In 2018 he had a 1.28 HR/9 at home and a 1.16 HR/9 on the road. Where we really saw a huge difference was in his BABIP, which was .217 on the road and .350 at home. A lot of factors obviously go into that, including the one we will discuss next, but his home BABIP will almost certainly be lower than .350 in 2018.

  1. Luck

As Crash Davis so eloquently put it in the quote at the beginning of this article, luck plays a large role in both batting average and batting average on balls in play. One extra lucky hit per week can make a huge difference by the end of the season, and you probably wouldn't even notice without looking at BABIP. In Moneyball (The book by Michael Lewis, not the movie), Scott Hatteberg has a couple of quotes that illustrate why it is foolish to ignore the role luck plays in batting average.

Talking about his time with the Red Sox, Hatteberg said ”I’d have games when I’d have two hits and I didn’t take a good swing the whole game,” he said, “and it was like ‘Great game, Hatty.’” As he told it, his experience was different in Oakland. “Here I go 0 for 3 with two lineouts and a walk and the general manager comes by my locker and says, ‘Hey, great at bats.’”

As fantasy players, you want to target the batters who will have great at-bats. If you have enough great at-bats, you will eventually get good results, even if you can go an entire season without seeing them. With that in mind, let’s look at a couple of blind resumes.

  PA AVG OBP SLG BB% K% BABIP LD% GB% FB% HR/FB Soft% Med% Hard%
Player A 730 .318 .363 .534 6.7% 11.0% .322 19.3% 41.4% 39.4% 13.2% 17.4% 49.2% 33.4%
Player B 712 .264 .344 .459 10.8% 11.1% .268 16.8% 40.4% 42.8% 10.1% 18.2% 46.0% 35.7%

Player A is Mookie Betts in 2016.Player B is Mookie Betts in 2017.Hopefully, we can all agree that the only thing to change for Mookie Betts in 2017 was luck. Unless he gets unlucky again—or something else changes—he will almost certainly put up much better numbers in 2018.

We have mentioned a few times already that pitchers’ BABIPs tend to be influenced heavily by the defense behind them. They are also influenced quite a bit by luck. So often, a ball will sneak through the infield or split the outfielders that would have been caught, perhaps rather easily, if it was a foot or two in either direction. Those simple twists of fate can add up over time.

Now that we have discussed BABIP, how to think about it for fantasy and the factors that contribute to it, it is time to take a closer look at four notable BABIPs from 2017. These are players who could be difficult to evaluate for 2018, but you get a more complete picture once you dive into their 2017 numbers.

Avisail Garcia

Garcia’s .392 BABIP was the highest in baseball last season. He had a .320 BABIP in 2015 and a .309 BABIP in 2016, his only two full big league seasons. Garcia cut down on his strikeouts in 2016 to a career-low 19.8%, which helped contribute to his .330 batting average. He also had the lowest flyball rate of his career and his highest hard contact rate since his rookie season. Those things suggest he will not regress all the way back to batting .257 or .245 as he did in 2015 and 2016, but only if they continue.

Maikel Franco

Franco had the fourth lowest BABIP in baseball at .234 in 2017, but unlike Rougned Odor, this is not really anything new. As we mentioned above, Franco’s career BABIP is better than only Drew Butera among active players. His .271 BABIP was the 13th lowest in baseball in 2016. Franco’s 30.9 Hard% in 2017 was the highest of his career, and he is actually batting better against the shift than against no shift. He has a 45.2 Pull%, which ranks 27th among active batters. It would be easy to give up on Franco, especially compared to someone like Rougned Odor, but Franco might be the better bounce back candidate.

Jeremy Hellickson

Hellickson had the fourth lowest BABIP in baseball at .246 and still finished with a 5.43 ERA. It just goes to show no amount of BABIP luck can help when you have a 2.04 K/BB (only seven qualified starters were worse last season).

Rick Porcello

Porcello can be a bit of a polarizing figure. It took six seasons as a Tiger for Porcello to produce anything noteworthy for fantasy, and in that year, 2014, he had just 129 strikeouts in 204.2 innings. He has struck out more batters in Boston, but with varying degrees of success. He had a 4.92 ERA and 1.36 WHIP in 2015, a 3.15 ERA and 1.01 WHIP in 2016 and a 4.65 ERA and 1.40 WHIP in 2017. As you might expect from his inclusion in this article, Porcello’s BABIPs fluctuated wildly over this time: .331 in 2015, .269 in 2016 and .324 in 2017. Porcello has had at least 7.63 K/9 in each of his three seasons in Boston, so if he can keep that up and get a BABIP around his career average of .309, he could probably be a solid streaming option in mixed leagues. We just haven’t seen it yet.

Robbie Ray

He allowed a .267 BABIP last season, which isn’t surprising considering he is a good pitcher. It is surprising when you consider no qualified pitcher allowed more hard contact last season. Ray is a fly ball pitcher, which certainly helps, but there is at least a chance his 2018 BABIP regresses to his .319 career average, which would basically make him a streamer in mixed leagues (but still better than Rick Porcello).

Predicting how a player’s BABIP will change from one year to the next is inexact at best, but the ability to do so can be a great asset for fantasy players. Remember, before you draw a conclusion regarding a player’s BABIP, you have to look at how he got there so that you can hopefully figure out where he might be going.