What Edwin Jackson’s Pitch Count Hath Wrought

Edwin Jackson threw a bit of a lame no-hitter on Friday. I’m sorry if it offends you when I call such a hallowed feat lame, but eight walks in a game for a major-league pitcher is bad (see Pulling a Homer). But aside from this, one aspect of his performance has gotten a lot of attention: 149 pitches thrown. This is the highest pitch count allowed in a game since 2005 (see my previous post on how pitch counts have changed over the past two decades).

I have been conducting a study of pitch counts with Sean Forman, and we will be presenting our findings at the upcoming SABR convention in Atlanta. But since it’s applicable to Jackson’s situation, I’ll reveal some of the findings. Our study uses past pitching performances to estimate the impact of pitch counts on future performance, controlling for numerous factors, using fractional polynomial regression analysis to capture potential non-linear relationships. The results indicate that the impact of the pitch count in a single game on the following game is real but small; and, the impact is linear, not increasing as some analysts have theorized.

On average, every pitch thrown raises a pitcher’s ERA by 0.007 in the following game. Jackson’s ERA was 5.05 going into Friday’s game averaging 104 pitches per game; thus, based on the historical response of pitchers to pitch counts Jackson’s expected performance in his next start is about 5.37. So, Jackson can be expected to pitch worse, but not that much worse. Really, it’s not that big of a deal as a one-time event. Should Jackson continue to average around 150 pitches a game, the impact will grow, but I doubt that is going to happen. As for the impact on injuries, we didn’t look into it in this study. However, I have previously found little correlation between pitching loads and injury.

My take: if you have a pitcher going for a no-hitter—not matter how bad he’s pitching—the benefit of the excitement and media coverage of letting a pitcher throw more pitches is probably worth the cost. Let’s stop freaking out about pitch counts until we understand their influence a little better.

Update: In response to Jackson’s high pitch count, the Diamondbacks will push back his next start a day or two. How much will this help him recover? No much. On average, each day of rest lowers a pitcher’s ERA by approximately 0.015. Thus, his expected ERA drops from 5.37 to 5.34 (with two days of extra rest). Why rest days matter so little is an interesting question. A few years ago, I saw an presentation on muscle recovery from exercise, and one of the interesting findings was that most of the healing happens within the first few days. Whether this explains the finding or not, I don’t know.

5 Responses “What Edwin Jackson’s Pitch Count Hath Wrought”

  1. Tim Duffy says:

    Nothing for nothing, but Jackson retired 20 of the last 23 batters (1 walk, 1 hbp, and 1 safe on error,) while clinging to a 1 – 0 lead, against a pretty good team, while pitching a no-hitter. To reach this point he had to pitch through some self-induced early trouble.

    Also consider that TB is 3rd in MLB in walks but only 22nd in BA. TB’s offensive walks per hit ratio in the highest in baseball.

    It’s true that Jackson walked 8 (and hit 1) but I gotta tell ya that the word “lame” never enters my mind when considering this effort (but I’m not “offended” in any way that you consider it lame.) Just offering a different opinion on the subject – take care.

  2. Marc Schneider says:

    It’s not a matter of being offended; it’s a matter of being ungenerous. However he did it, Jackson gave up no hits and no runs. I’m not saying it’s the greatest game ever pitched, but give the guy credit for something. If you hit a ball that bounces off Jose Canseco’s head over the fence, it’s still a home run. Enjoy the feat rather than try to denigrate it.

  3. Israel Malkin says:

    Im not familiar with Fractional Polynomial Regression, but just a thought.
    Did you control for the fact that some pitchers can pitch more without getting as tired?
    Something like setting a baseline for each pitchers (average number of pitches throughout the sample), and regressing ERA on the percentage difference from that baseline.

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