Analysts toss around terms such as BAbip without explaining how to interpret them or why they’re significant. Similarly, the websites that provide the statistics, such as Baseball Reference and FanGraphs, define the metrics but do little to deconstruct them for readers. This is a tutorial for anyone who is not familiar with advanced metrics and wants to learn more about them.
BAbip, or batting average on balls in play, is a metric that quantifies how often a ball put into play by a batter turns into a hit. Because it concerns only balls in play, it excludes home runs and walks and includes sacrifice flies.
A player’s BAbip for a single season can be understood by comparing it to his career rate, or what I have referred to as the “norm.” These comparisons can help you predict if a player is over-performing or under-performing, the key word being “predict.” BAbip that significantly deviates from the norm does not guarantee it will regress toward the norm; a deviation across a large sample size (in this context, an entire season) is much less likely to happen but is not impossible.
Some basics: The benchmark BAbip is around .300, although each player creates his own norm. Speedy guys tend to have higher BAbips (Mike Trout‘s is .370) because they can leg out ground balls. Power guys tend to have lower BAbips (Edwin Encarnacion‘s is .275). Although a player exerts some influence, BAbip is largely a function of the defense handling the balls the player puts into play. Some ground balls escape the gloves of clunkier infielders; some line drives find the gloves of roving outfielders. So it is important to note that a player’s BAbip involves some random deviation (luck). Take a look the Arizona Diamondbacks’ Martin Prado‘s splits circa the 2013 season:
The abbreviated table above was generated near the end of the 2013 season. Prado, a career .292 hitter, struggled through the first half of the season, hitting only .253 with a .668 OPS. His BAbip was a lowly .260 at the time, much lower than his career .311 mark. That’s a large deviation; if I were a fantasy owner looking to capitalize, I would bank on Prado bouncing back. As the table shows, Prado paid dividends to the owners who stuck with him (or the ones who capitalized via trade), batting .322 with a .321 BAbip with 13 games to go in the season. The second-half BAbip is high, but combining it with his first-half mark produces a .284 BAbip — not quite the norm, but much closer than how he performed before the All-Star Break.
Davis had hit 50 home runs with 13 games to play, with 37 of coming in the first half of the season (you can view his 2013 splits here). This is relevant because Davis’ HR/FB rate before the All-Star Break was, if I’m not mistaken, around 28 percent, significantly higher than his current mark of 22.8 percent. Twenty-eight percent was awfully high, even for Davis; a savvy statistician (aka fantasy baseball nerd) would have expected his home run rate to regress toward the norm, around 16 percent.
Because Davis didn’t break out until 2012, his career HR/FB may be a bit deflated. But even the large difference between 2013 and his career rate indicates Davis is a candidate to regress in 2014. Had his HR/FB rate in 2013 been closer to something like 18 percent, Davis would have been closer to 40 home runs than 50.
In short, compare a player’s HR/FB to his career mark, which is what is normal for him, to try to determine whether he has been getting lucky (or unlucky, or neither) on home runs. Strong deviation from the norm is a likely predictor of regression, for better or for worse. HR/FB is not the end-all, be-all to explaining a player’s performance, but it can greatly benefit the owner willing to exploit its predictive attributes.