“Luck-Neutral” Batting All-Stars

Earlier in the week, we looked at pitchers that performed, eh, just about as expected. Now it’s time to look at the batters, though we lack the same sort of luck-neutralized all-encompassing stat like FIP with which we can go to battle.

Ah, but do we? So much of our analysis of a batter’s state of ‘luck’ does stem from one statistic. All hail the mighty BABIP.

At first encountering with the mighty BABIP tool, our analysis was a bit crude. The number trends toward .300 across baseball, so we just figured it might do so for most hitters. Then we noticed that Ichiro Suzuki broke the rule, and did so year-in and year-out. BABIP was tied to line drive percentage, and speed, and our knowledge of the tool was refined. Derek Carty and Chris Sutton really revolutionized the field when they debuted their Simple xBABIP Calculator at The Hardball Times in 2009. For the purposes of this article, however, I’ll use the formula put forward by slash12 on Beyond the Box Score, as it uses percentages where the calculator used raw numbers.

So now that we’ve broken down an expected BABIP based on batted ball profiles, what does it look like in practice? The players on the edges come in all flavors – Josh Hamilton (.390 BABIP, .347 xBABIP) and Carlos Gonzalez (.384 BABIP, .328 xBABIP) were the high-BABIP, high-xBABIP ‘lucky ones;’ Carlos Pena (.222 BABIP, .301 xBABIP) and Aaron Hill (.196 BABIP, .250 xBABIP) the low-BABIP, low-xBABIP ‘unlucky ones.’ But the calculator also uncovers some players that ‘deserved’ their poor luck, like Mark Reynolds (.257 BABIP, .262 xBABIP), or high-BABIP players that ‘deserved’ their good luck, like Ichiro (.353 BABIP, .358 xBABIP).

BABIPs come in all flavors, and our luck-neutral batters follow suit. The ten most neutral dudes were surrounded by two guys that might be considered lucky – Joe Mauer (.348 BABIP, .351 xBABIP) and Dan Uggla (.330 BABIP, .327 xBABIP) actually performed about as well as expected. That isn’t to say that their batted ball profiles won’t change and then affect their BABIP/xBABIP relationship, though. That’s actually happened to both players in their past. But last year, the stars aligned.

Ten guys were closer to their xBABIPs though. Leaving the top three guys out, we’ve got Casey McGehee (.306 BABIP, .303 xBABIP), Adrian Gonzalez (.322 BABIP, .320 xBABIP), Alex Gonzalez (.275 BABIP, .273 xBABIP), Kelly Johnson (.339 BABIP, .338 BABIP), Cody Ross (.324 BABIP, .325 xBABIP), Aubrey Huff (.303 BABIP, .305 xBABIP) and Prince Fielder (.291 BABIP, .293 xBABIP). That’s a lot of different kinds of players with different skill sets. We can’t say that groundball hitters, or speedsters, or sluggers are any easier to predict or are any more likely to put up luck-neutral BABIPs. And, given the lack of a strong correlation from year to year, we can’t even say that these players are likely to put up the same numbers next year. For one, these numbers are not park-adjusted, and we know Adrian Gonzalez will be looking at a vastly different park.

Our top three guys actually show BABIPs that are equal to their xBABIPs three decimal points in. Marco Scutaro (.295 BABIP, .2954 xBABIP), B.J. Upton (.304 BABIP, .3045 xBABIP) and Marlon Byrd (.335 BABIP, .3356 xBABIP) all suffered through bland, fifty-fifty luck for the year. Their mix of infield fly balls, infield hits, fly balls, ground balls and line drives produced just as many hits as it should have.

Unfortunately, where our pitchers article produced Jake Westbrook as the turkey-swiss of his position, none of the three have shown a particular proclivity towards stable batted ball profiles or BABIPs. If it’s any player mentioned here today, it might be Ichiro who is the peanut butter and jelly of this pantry. His lifetime batted ball profile has produced a .357 BABIP… and a .355 xBABIP.