11 Hitter BABIP Surgers For 2018 — A Review

In mid-February, I identified and discussed 11 hitters whose xBABIP marks stood significantly above their actual BABIP marks. I dubbed the group the potential BABIP surgers for 2018. There are two important caveats to remember:

1) This equation, while the best currently out there for estimating what BABIP should have been given underlying skills, is still not great, leaving lots of room for batters skills not captured in the equation, and of course, lady luck
2) xBABIP is not predictive/forward looking/a projection. It’s backwards looking like SIERA, but it correlates with next year BABIP better than BABIP itself, which is quite meaningful.

So now that we got those caveats out of the way, let’s see how the hitters who most underperformed their 2017 xBABIP marks ended up performing in 2018.

2018 BABIP Surgers
Name 2017 xBABIP 2017 BABIP 2018 BABIP Diff
Rhys Hoskins 0.335 0.241 0.272 0.031
Miguel Cabrera 0.354 0.292 0.352 0.060
Ian Kinsler 0.301 0.244 0.250 0.006
A.J. Pollock 0.343 0.291 0.284 -0.007
James McCann 0.349 0.300 0.282 -0.018
Russell Martin 0.310 0.261 0.234 -0.027
Greg Bird 0.241 0.194 0.230 0.036
Brad Miller 0.307 0.265 0.350 0.085
Ryan Braun 0.330 0.292 0.274 -0.018
Nicholas Castellanos 0.351 0.313 0.361 0.048
Unweighted Avg 0.322 0.269 0.289 0.020

Note that Yasmany Tomas was originally included in the group, but removed from the table as he spent the entire season in the minors.

These results were far worse than I expected. Only six of the 10 improved their BABIP marks, despite the group significantly underperforming their xBABIPs in 2017. Overall, from an unweighted perspective, the group boosted their BABIP by 0.020 points, still remaining well below its 2017 xBABIP mark in aggregate.

Oddly, the biggest BABIP improver was Brad Miller, a guy who was DFA’d by the Rays in June, picked up by the Brewers, and then released by them at the end of July. Even though he posted an acceptable .312 wOBA and has appeared at literally every non-pitcher or catcher defensive position, his services were no longer requested by a Major League team.

Did you realize that Miguel Cabrera’s BABIP rebounded tremendously, after dipping below .300 for the first time in his career? His power remained down though and he forgot how to hit fly balls, so you would be forgiven if the BABIP rebound escaped your memory. His batted ball distribution certainly don’t suggest a big BABIP spike, as you don’t want one of the slower men in baseball suddenly hitting grounders more than 50% of the time.

Nicholas Castellanos also made good on his xBABIP promise, as his pristine batted ball profile led to a career best BABIP. He was already a strong line drive hitter, but he took that skill to a new level, just inching past his previous career best posted during his rookie 2014 campaign. He also continued to avoid hitting pop-ups. One of these years, he’s going to explode for a 20% HR/FB rate. If and when that happens, he’s Joey Votto without the plate discipline.

A pair of catchers brought up the rear, as Russell Martin and James McCann both suffered BABIP declines, despite massively underperforming their xBABIP marks in 2017. You never know how a catcher is going to age and it looks like Martin may be done offensively. His LD% fell to a career low, while his pop-up rate rose to a career high. It’s impossible to contribute positive value offensively when you fail to hit liners and hit too many pop-ups.

I really liked McCann as a sleeper second catcher, but he was a massive disappointment. Aside from the BABIP decline, his power disappeared. I didn’t see that coming.





Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.

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scotman144member
5 years ago

I wonder if efforts to predict / analyze / normalize babip’s would be better off for just omitting catchers from the population. They’re being selected for a very different sort of athleticism and skillset than every other position player and I’d wager that a great majority of them are outliers on the low end of sprint speeds.

scotman144member
5 years ago
Reply to  Mike Podhorzer

Hi Mike, thanks for the reply. This could be anecdotal but I see some slow-footed catchers on babip regression lists every year and always make a note to doubt those candidates.

Including spd is great but I suspect there’s a threshold of slow-footedness below which the impact on babip is dramatically magnified / nearly exponential rather than anything like linear. I also suspect catchers age more poorly in speed and generally behave very differently than the rest of the player population with regards to babip.

I doubt it’s a huge skew on the data but it would be interesting to see if a formula with constants derived from a catcher excluded population (or even better a population with all extreme slow speed outliers excluded and JT Realmuto types kept in) would better fit the “normal” position player population.

Thanks for the thought provoking content!