Pitcher fatigue, batted balls, and DIPS
June 10, 2008 6 Comments
Are you tired, Mr. Starter? I’ve been asking this question of my magic spreadsheet for a while now, and last week, during my look at fatigue factors like pitch count, days of rest, mileage on the arm for the season, and number of times through the lineup, I promised a follow up study on how fatigue affected what happens to a batted ball. Here it is.
I isolated all batted balls from 2000-2006 (isolated is a strong word: that’s still 500K+ events!) Like in my last two articles, I first calculated the batter’s and pitcher’s GB rate, LD rate, and FB rate (including pop ups), and then the expected probabilities of each for such a matchup, using the odds ratio method. Then I took the natural log of that number. I entered this into a binary logit regression as a control for batter and pitcher tendencies, and then on top of that entered my fatigue variables. Pitch count was present in all the regressions, then one of the other fatigue variables (rest, mileage, times through order, number of pitches thrown last time out), and then the interaction of pitch count and whatever fatigue measure was under consideration. (In my day job, we call this a modeator analysis.)
First, what effect does pitch count have on the batted ball profile in general. As the game wears on, there’s an effect for pitch count such that grounders go down and fly balls go up. How poetic. Line drives don’t seem to respond to pitch count.
Let’s talk about mileage. Like last week, I kept the sample to guys who went a maximum of 10 days between starts on the season to get rid of players who had “hidden mileage” in AAA or the bullpen, plus those who had been injured. The results: as the season wears on, and the pitcher has more pitches on that arm, there’s a very real effect. When the ball comes off the bat, it’s more likely to be a flyball and less likely to be a grounder. Of course, the effect is not going to overwhelm a pitcher’s own tendencies to induce ground balls, but it’s certainly not going to help. Tired pitchers get the ball up in the zone more often and that’s more likely to be hit in the air. Now, last week, I didn’t find that there was a significant increase in home runs (or anything other than walks), in terms of outcomes, but batted ball percentages are usually much more statistically stable than are outcome measures. It’s possible that what used to be ground ball outs are becoming fly ball outs, but I’m not convinced.
Next, let’s talk about rest. Turns out that days of rest didn’t have any effect whatsoever on the batted ball profile, once you control for batter and pitcher matchup. In my previous study from last week, I found that a short-rested pitcher was more likely to give up a home run. So, while he might not throw any more fly ball pitches, his pitches that do go for fly balls are more likely to leave the yard. A ground ball pitcher likely wouldn’t have the same problem, because… well a mistake on a ground ball pitch is going to just be a slightly harder hit ground ball… maybe a better chance for a single. This also brings up the old chestnut about starting sinkerballer pitchers on short rest because their ball is “heavier” and sinks better, leading to more ground balls. I decided that was worth a look. I restricted my sample to pitchers who had GB percentages over 50% for the year. That’s not an exact proxy for sinkerballers, but I’m guessing there’s a few sinkers and splitters being thrown there. The result: no effect. Looks like the sinker is sinking any more heavily because of the short rest, but it doesn’t seem to harm the pitcher either.
Finally, let’s talk about the number of times that the pitcher has seen this guy before in this game. Because I’m controlling for the expectations of this pitcher/batter matchup and for pitch count, any effects for time through the lineup are likely due to the pitcher actually gathering intelligence about the batter. Are there effects? Yes, there are. As the lineup cycles around more, the batter is more likely to hit a ground ball and less likely to hit a line drive. (That’s a good trade for the pitcher!) So, it looks as though the pitcher is actually gathering some sort of intelligence on the batter and is perhaps gaining some small advantage. Oftentimes, Sabermetric analysis has a tendency to reduce at bats to simple agglomorations of probabilities. Here’s some evidence that we need to take a look at the mental aspect of the game. Of course the batter and pitcher are trying to learn about one another. It looks like the pitcher is the one who has the advantage. Perhaps a pitcher gets the batter out with his “stuff” early and his brain late.
One more area of interest. Does fatigue affect DIPS? For a long time, it’s been assumed that balls in play went for hits at a rate that had more to do with the defense than the pitcher. That’s been based mostly on season-to-season intercorrelations. But, what about within a game? The answer is… yes, there is an effect. At lower pitch counts, a ball in play is less likely to be a hit, again, controlling for batter/pitcher rates. Additionally, there’s an effect for number of times through the lineup (already controlling for the fact that there will be a pitch count effect.) So, we would expect that starters who are efficient with their pitch count to have a lower BABIP overall. Fresher pitchers throw pitches that are better able to be turned into outs. This might explain my question concerning Troy Percival.
There are a few more factors that could be studied. I didn’t consider age (younger pitchers probably bounce back faster) nor body type (through BMI?), and I haven’t yet looked at relievers. And then there’s the work that’s sure to come from the Pitch F/X folks who can break this stuff down on a molecular level.