Mike Trout, Yasmani Grandal, and Other Early BABIP Outliers
When it comes to early-season dominance or struggles, BABIP tends to be a featured player in many of the odder-looking lines. At the top of the league, you have the already amazing Mike Trout sporting a .519 BABIP, fueling a video game-like 236 wRC+ and a 1.224 OPS. On the flip side, quality players are still looking way up at the Mendoza line, such as Yasmani Grandal (.121 BA, .125 BABIP) or Kyle Tucker (.179 BA, .173 BABIP). Even though the evidence suggests that there’s more variability in BABIP ability among hitters than pitchers, a month of a season is a pitifully small amount of time to establish a new baseline expectation for BABIP. So, who is “earning” their BABIP and who isn’t so far?
Similar to the “x” Statcast stats, the ZiPS calculates “z” stats — I’ll let you guess what the z stands for — as part of its year-end projection model. These aren’t yet used in the simpler in-season model, though that’s in the works. Similar to Statcast, ZiPS estimates BABIP from the component parts: launch angle, exit velocity, speed data (for grounders), and so on. ZiPS also considers the direction a ball is hit, as a player’s pull tendency is a repeatable skill. This last data matters quite a bit. For example, grounders hit up the middle end up as singles about half the time, but grounders hit 15 degrees to the left or right of the second base bag are hits about a tenth of the time.

How does it work? The numbers are still volatile, but if all you have is zBABIP and actual BABIP, zBABIP is historically the better predictor. For all players with 50 PA in both 2020 and ’21, 2020 zBABIP is closer to 2021 BABIP than 2020 BABIP for 65% of players. Historically, the best predictor of actual BABIP, again using only these two stats, is a linear combination of 0.9 zBABIP and 0.1 actual BABIP.