Fun with DIPS: Not all balls in play are created equal
June 27, 2007 14 Comments
DIPS. The idea that a pitcher doesn’t have any say in what happens to the ball once it is hit, short of fielding a ground ball back to him. The now-famous original study found that pitchers showed very little year-to-year correlation in the percentage of balls in play that became hits. However, there is a large amount of year-to-year correlation with events that the pitcher does have control over, specifically walks, strike outs, home runs allowed, and hit batsmen. The natural corollary of the theory was that once the ball was hit, just about every pitcher becomes a league average pitcher.
Critics of DIPS theory often point to such counter-examples as Greg Maddux, who “pitches to contact”, yet in the 1990s, was anything but league average. Perhaps, they contend, ground ball pitchers or fly ball pitchers have better luck than others. There has been some discussion of GB/FB rates and DIPS, but to my knowledge (and I could be mistaken), no one has ever broken down DIPS theory by the type of ball in play.
I take as my data set Retrosheet play-by-play files from 2003-2006. I eliminated all home runs, and calculated each pitcher’s yearly BABIP on each type of ball in play (grounders, liners, pop ups, and fly balls that are not home runs). I restricted the sample to those who had at least 25 of that type of ball in play in the year in question. (So, a pitcher with 22 grounders and 28 liners would have an entry for BABIP for liners, but not for grounders.) I transformed the variables using a log-odds ratio method, as is proper for rate/probability variables. Then, as per my favorite statistical trick, I took the intraclass correlation for each type of ball in play.
Ground balls, .114
Line drives, .174
Pop ups, .075
Fly balls (non-HR), .194
You can read those ICC’s much like year-to-year correlations. The pitcher has the least control over whether pop-ups go for outs and the most for fly balls. Even the fly ball number works out to an R-squared value of 3.8%, which isn’t all that thrilling (it means that 96.2% of the variance is due to other factors), so the DIPS theory still seems pretty sound. On the other hand, the R-squared value for ground balls is 1.2%, so pitchers have a little bit more control over their fly balls than they do their ground balls. Still, those values are pretty tiny, so I wouldn’t make anything of it. I’m not saying anything new here, but the assumption that the pitcher is totally out of control of what happens is errant, although not all that far off from the truth. However, some pitchers, especially those who live on fly balls are a little bit more in control than others.
There’s one other issue that irks me. While doing some work for something else I’m in the process of writing, I found that the ICC for stolen base success rate (SB / (SB+CS)) was about .30. That’s an R-squared value of 9%, which is, in perspective, a lot higher than the general BABIP ICC of .182 that I found here, but with correlation you end up on a slippery slope. When does the ICC (or if you want to do year-to-year) become high enough that it’s a “skill” and not luck? Is success at stealing bases a skill? This isn’t an issue with an easy resolution in Sabermetrics or science in general, I realize, but it’s something to consider.