Batting average on balls in play is one of the simplest sabermetric statistics, yet it’s also one of the most useful. BABIP measures how often a ball in play — defined generally as any batted ball that did not clear the outfield fence — goes for a hit.
BABIP is often used to determine which hitters are “lucky” versus “unlucky.” Generally speaking, about 30 percent of balls in play go for hits, making the league-average BABIP around .300, with a point or two fluctuation possible in either direction. (In 2017, the league-average BABIP was actually .300, but I would have used that as the general benchmark anyway.)
BABIP often tells us whether sample size is helping or hurting a player. If Player X is hitting .350/.450/.550 in the first two months of the season, but his BABIP is north of .400, we can expect him to regress at some point during the rest of the season. Still, depending on how long luck is going in Player X’s favor, he may still be in the midst of a career year. It works in the opposite direction, too, with underperforming players and low BABIPs, but that should be pretty self-explanatory.
The thing I’ve always wondered about BABIP is what factors help a player “control” it. Many of the best hitters in baseball are able to maintain relatively high BABIPs, and in this article, I hope to break down the factors that I believe lead to these out-of-the-ordinary BABIPs, as well as predict who is likely to maintain a high BABIP going forward.
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