Is Brian Bannister on to something?
January 31, 2008 9 Comments
In a recent interview with MLB Trade Rumors, Kansas City Royals pitcher Brian Bannister reveals that he does statistical studies to help improve his game. Bannister, in talking about DIPS theory, suggests that one piece of information that is rarely taken into account, when considering statistics such as DIPS, is the issue of the count. He doesn’t fully develop the argument, but he talks about the fact that on an 0-2 count, the batter doesn’t have the luxury of letting a pitch go by and so he might be forced into a bad decision and a bad swing. It’s a logical theory. And thankfully, one that can be tested, and rather easily at that.
It’s known that in general, pitchers batting average against varies by count with general batting average and OBP on a 3-0 count being much better than on an 0-2 count. I think the reasons there should be fairly obvious. The question here though is a little different. Once the ball is hit (and it doesn’t leave the park), does a pitcher have more control over what happens to the ball to an 0-2 pitch rather than a 0-1 pitch? Or more accurately, are the results at least more consistent on some counts, but not others.
I took my trusty 2003-2006 Retrosheet PBP data base out and selected out all the balls in play and coded them for whether the batter got a hit or made an out. I calculated each pitcher’s BABIP for each year, broken down by count in which the ball in play happened. I kept it to those pitchers with at least 200 batters faced total in the year in question. As per usual when I do DIPS-based analyses like this, I looked at the AR(1) intra-class correlation over those four years to see how stable BABIP was for each count. The higher the number, the more stable BABIP is at that count from year to year.
0-0 count – .066
0-1 count – .023
0-2 count – .050
1-0 count – .084
1-1 count – .103
1-2 count – .045
2-0 count – .011
2-1 count – .002
2-2 count – .016
3-0 count – .039
3-1 count – .000
3-2 count – .046
First off, those numbers are all still pathetically small. (There’s also a likely sample size issue, in that we’re looking at sample sizes as small as 20 BIP on some pitcher-year-counts) Yeah, some are bigger than others, but these are the types of numbers that spawned the original DIPS theory that on balls in play, a pitcher has very little replicable skill in keeping the ball from being a hit, at least on a year-to-year level. It looks like that conclusion still holds, and it doesn’t seem to matter much, on a year-to-year individual pitcher basis, what the count is.
But, maybe there’s something to Bannister’s theory. I ran BABIP by count on all balls in play from 2003-2006 for everyone. The results seemed to support Bannister’s basic premise that the count does make a difference. Here are the BABIP numbers for all pitchers in the four years in the dataset, in order from highest to lowest.
That’s what you might expect. The more that the count tilts toward the batter, the higher the BABIP, and the difference between an 0-2 and a 3-0 count is 26 points or so. So, overall, the average pitcher does benefit from having the count in his favor. The problem is again, at the individual level, there’s not a lot of stability, so it doesn’t look like it’s something that one pitcher is able to exploit and not another. At the individual level, the variation around the mean is random. Rather, the benefit of an 0-2 count is a general benefit that all pitchers get in the aggregate.
Consider this a tutorial in what’s known as multi-level modeling. In this case, you have individual players who are all members of an overarching group, MLB pitchers. In this case, most of the effect might descend from being a Major League pitcher rather than being a specific individual. Now, Bannister says that the best way to control what happens to a batted ball is to try to control the count. It looks like he’s right. Pitchers who pitch ahead in the count are more likely to give up balls in play that end up in someone’s glove.
Now, do pitchers generally show some skill in what counts they give up their BABIP? The answer is actually “sorta kinda yes.” Again, I went to the 2003-2006 data set and calculated the percentage of balls in play that came during each specific count (minimum 200 total BIP) relative to the total number of BIP. Then, I ran a series of ICC’s over the four years in the data set. The ICC’s were generally in the mid .30 range. Not huge, but not something that can be dismissed out of hand. There’s more. The ICC’s for percentage of time getting the BIP off of an 0-2 count was .51 and for a 1-2 count was .41. The ability to induce a pitcher’s count, and then to get the batter to hit the ball has some decent stability. And those are the counts with the lowest BABIP.
So, in a two-step process, there is a certain amount of control that a pitcher has over BABIP. A pitcher has somewhat of an individual ability to control what counts he gets into, especially two-strike counts. Then, based on that, there’s a league-wide benefit/penalty for working into specific types of count. It’s not that certain pitchers have a certain ability to leverage a 1-2 count, comparable to other pitchers. It’s just that some pitchers are better than others at getting to a 1-2 count, and everyone pitches better when the count is in his favor. So, a pitcher who is good at getting ahead in the count is likely to have a BABIP that’s particularly low, and that’s not a mistake.
I think Brian Bannister is on to something.