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Revisiting xBABIP: Different Equation, Similar Results

Last week we used an xBABIP calculator based on batted ball types to determine what the Indians should have done on balls in play last year. This week, we use a different approach, incorporating more data, and revisit the same question: how lucky (or unlucky) were the 2014 Indians on balls in play?

By Chad Young
David Richard-USA TODAY Sports

In the comments of my post last week, there was some discussion of how the xBABIP calculator I used compares to work done by Jeff Zimmerman at FanGraphs to improve xBABIP calculations.

That link will give you more detail on what Jeff did, but the basics are this: He incorporated two new pieces of informatoin into the calculation. One is "hard contact" data from Inside Edge. They categorize every ball in play as Weak, Medium or Hard hit and Jeff found the not-so-surprising connection between hard contact and balls finding holes. He also incorporated Bill James' Speed Score, since being fast also leads to more hits on balls in play.

The chart below combines what I shared last week with the same data based on Jeff's new equation.

Player BABIP BBType BBDiff HH/Spd HH/SpdDiff xDiff
Nick Swisher 0.273 0.342 0.069 0.3 0.027 0.042
Michael Bourn 0.337 0.351 0.014 0.312 -0.025 0.039
Jason Kipnis 0.288 0.335 0.047 0.304 0.016 0.031
Ryan Raburn 0.245 0.307 0.062 0.276 0.031 0.031
Mike Aviles 0.271 0.305 0.034 0.28 0.009 0.025
David Murphy 0.285 0.296 0.011 0.275 -0.01 0.021
Michael Brantley 0.333 0.34 0.007 0.32 -0.013 0.02
Lonnie Chisenhall 0.328 0.314 -0.014 0.294 -0.034 0.02
Jose Ramirez 0.297 0.32 0.023 0.303 0.006 0.017
Yan Gomes 0.326 0.329 0.003 0.313 -0.013 0.016
Carlos Santana 0.249 0.302 0.053 0.291 0.042 0.011
Brandon Moss 0.283 0.284 0.001 0.301 0.018 -0.017

Some quick definitions - BBType is xBABIP based on batted ball type while HH/Spd is xBABIP including hard hit and speed score data. They each have a diff column showing how much higher the xBABIP is than the actual (so positive means the hitter had bad luck). xDiff is the difference between the two formulas for each player.

The first thing to note is that 11 of the 12 player we looked at had lower xBABIPs once you account for hard hit and speed. There are three possible explanations for this:

1. The new formula just plain returns lower numbers for all players. This, in fact, turns out to be true. Using the batted ball type formula, I found a 2014 league average xBABIP of .305, while based on the chart in this article, 2014 league average xBABIP was .296. That, though, does not explain everything. Even if we decrease all the Indians xBABIPs from the old calculation by .009, we have 11 of 12 seeing their xBABIPs go down even further. So what other explanations are there?

2. Indians players are all slow and therefore, once we account for speed, we expect lower BABIPs. This seems a bit unlikely. First of all, the calculation for speed score includes things like stolen bases (Cleveland was 10th in MLB last year) and triples (Indians were 23rd). Stolen base percentage also factors in, and the Indians were very strong in that department. This certainly helps to explain why Nick Swisher had such a big gap, though. It probably is not super informative on Michael Bourn, though.

3. Indians players hit the ball hard rarely. Maybe this is the Bourn explanation? The one player who had a higher xBABIP with the new formula is Brandon Moss, who definitely hits hard. Carlos Santana and Yan Gomes also tend to strike the ball solidly, and they were the next closest in xBABIP scores.

The chances are, there is some truth to this. There is also a possibility that Inside Edge simply has skewed data for Progressive Field or that there is something about Progressive Field that leads to fewer hard hit balls, which would help explain why Moss isn't afflicted by the same malaise as the rest of the crew.

The other big take away here is that 8 of the 12 players here had below average BABIPs in 2014, while either seven or eight (depending which equation you prefer) had above average xBABIPs. Even with the new formula, seven of the 12 players show up as unlucky. Some, particularly Carlos Santana, were extremely unlucky.

No matter how you cut the data, the Indians appear to have a crew of players who should post above-average BABIP figures, but have instead been below average. Maybe 2015 is the year their luck balances out.

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