I don’t believe pitchers should go past 100 pitches. That might seem like the view of a baseball luddite, but it’s quite simple. Throwing 100 pitches means six innings. Surviving six innings equates to 27 batters. Facing 27 batters impends the fourth time through the order. And that spells doom.
As a rule of thumb—not without exceptions—a decent reliever coming out of the bullpen will be better than all but the best of starting pitchers facing the fourth time through the order. Batters make adjustments, and there’s little a pitcher can do.
So I don’t think fatigue has much to do with the 100-pitch rule. However, I do think the subject of pitcher stamina is interesting in itself. Behold:
No pitcher fades as severely as Jonathan Sanchez.
Here’s how I arrived at that conclusion. First, I tried to correct the velocity using a similar method as outlined by Mike Fast yesterday, but only using the fastest 25% of pitches by every pitcher. Then, I took the 25% fastest pitches for every pitcher at every pitch count. This would hopefully provide an unbiased measure of fastballs as the game went on. I found the difference in velocity between each of those pitches and the average pitch velocity in each game.
Here’s how it looks on a league-wide basis:
There is a phenomenon with regard to the first pitch of a ballgame. Pitchers throw fastballs at an exceedingly high rate on the first pitch, and batters refuse to swing. I’ve noticed that some smart players (such as Derek Jeter) will take advantage of such predictability. After that, pitchers will work their hardest against the most difficult batters in the batting order from pitches 10-20, and then fade a bit as the game goes on, until they hit the fourth time through, when they really gear up.
Few are able to enter another gear like Justin Verlander:
I ran a regression of velocity on pitch count, controlling for the pitcher, and found that the following starters, over the course of 100 pitchers, will either gain a half a mile per hour in velocity or lose at least one mph.
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One other notes of interest: there were reports early last season of a spike in Mike Minor’s velocity. Minor was reportedly a finesse pitcher coming out of Vanderbilt, but he debuted in the Majors throwing in the low 90s. By September, Minor complained of fatigue. As it turns out, Minor also fatigues rapidly within games:
Pitch counts aren't the only reason for fatigue; time is another potential culprit. To test for time's effect on velocity, I controlled for pitch count. At 10-15 pitches, a pitcher has generally been on the mound for about five minutes. My data showed that pitchers threw over a tenth of a mile per hour faster when they had been out there under five minutes than when they'd passed the five minute mark. In general, at the same pitch count, the more time has elapsed since a pitcher’s first delivery, the softer he throws.
Next week, I hope to look at how time between innings may impact velocity. The thing to keep in mind with all of this is that every pitcher reacts in his own way to these stresses. The data would indicate that most all pitchers should be pulled before they reach the fourth time through the order, and that there's no way of telling how most any pitcher is throwing given his pitch count without an awareness of his individual history. The 100-pitch mark isn't when pitchers tire—different athletes tire at their own rates throughout the entire course of competition. The crucial factor here isn’t fatigue, but times through the order. Limiting pitchers to 100 pitches seems to be the right rule, but for the wrong reasons.
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Whether that applies equally well within a game, I'm not sure. It's tough to separate multiple effects that are going on as pitchers pitch deeper into the games (seeing same batters multiple times, fatigue, different quality of pitchers in the sample, changing temperature, pinch hitters, etc.).
Jeremy, I think this opening should have be done without the 6 innings part, and said rather, "Throwing 100 pitches equates to roughly facing 27 batters."
Though, I really enjoyed the rest of the piece.
For example in 2010, Sanchez's K:BB ratio goes from 1.81 to 2.04 to 2.13 to 3.54 for pitches 76-100. Does that suggest Sanchez really develops pinpoint control as the game goes on ? Of course not, he throws a lot of pitches when he's throwing well and has good control.
Was the data used for this just from 2010, or does it encompass all of Sanchez's career ? Because his first two years he was largely used as a reliever, which could also bias the results for the lower pitch counts.
Data is mostly from 2009-2010. I normalized velocity by game, though, so starting vs. relieving shouldn't matter. I'm confident in saying that Sanchez fades at an exceptional rate throughout the game.
Also, what effect do you think temperature has on this? It would be nice to separate out day and night games to see whether you can tease out the temperature effect, although fatigue in day games might come into play also.
My original purpose for this article was actually to find the role of temperature on pitcher fatigue, so it's funny you ask that. I spent a day looking for something and couldn't find anything for a multitude of reasons. I did not separate out day and night games when I looked at it, which I probably should have. I do not know what the best way for testing temperature would be. Please let me know if you have any ideas on how I might go about it, because I find the topic interesting, but I'm rather stumped.
Otherwise, just run some kind of regression on pitch speed and temp controlling for the other factors of course.
As I personally hate regressions, I would simply split each pitchers games into 2 groups for day games and 2 groups for night games - below a certain temperature and above a certain temperature. You would have to do it for home games, otherwise the stadiums would be a confounding factor (the warm games would tend to be in different stadiums than the cold games).
If you did that, you would have to control for time of the year as well, otherwise your warm games would mostly be in the middle of the season and the cold games at the beginning and end, which could be a confounding factor as well.
Shouldn't be too hard to find a way to see effect of temp on overall velocity and the trends during a game.
Didn't someone have an article a while ago on pitch speed and temp? Also, someone can Alan Nathan could tell you the physical effect of changes in temp on pitch speed due to the differing air density, but that would not include any effects on the pitcher (like being looser in warmer weather or less fatigued in colder weather, etc.).
The other half of my question had to do with the 25% cutoff for identifying fastballs. I certainly understand the merits of choosing one cutoff, but unavoidably there some folks will have a lot of other pitches mixed into that group, others fewer. Therefore, if a pitcher's mix of pitches changes at all as the game moves along, this alone could possibly produce both the small deltas found within any one pitcher's data and the differences across pitchers. In other words, the findings could be an artifact of that assumption.
It's a good article and I'm glad to see such work, so I'm not trying to attack it. But it's our job as the readership to probe the science and make sure it stands up.
The coefficients are statistically significant for the guys I showed in the sense that their p-values are less than .05 or whatever. I'm not sure if that's really what you're getting at, though.
The mix of pitches thing is an issue, but check out the Sanchez graph. He's never thrown harder than 93 past 80 pitches, and he routinely throws 94-95 to start the game. And part of the point I was trying to make is that all pitchers tire at different rates and stuff, so of course I agree choosing one number like 25% as a cutoff isn't great. Wakefield's distribution was definitely messed up. Maybe I should've chosen top 50% of all pitcher's fastballs.
Hope my tone isn't somehow coming off defensive or anything. I really appreciate all comments.