The Pitch Clock and Its Effects on Pitching Performance and Injuries

Troy Taormina-USA TODAY Sports

As a measure to improve baseball for the average fan — or even the decidedly non-average fans who frequent our pages — I think the pitch clock has been a resounding success. Trimming almost half an hour from the length of games hasn’t diminished baseball itself, with the cutting room floor mainly littered with the things that take place in between the action. Now, you can argue that we’ve also eliminated some of the dramatic tension from crucial situations in important games. But for every high-stakes matchup between two great players in a big moment, there were a multitude of unimportant ones stretched out endlessly by a parade of uniform readjustments and crotch reconfigurations. I enjoy having a leisurely Campari and soda with a friend while waiting for dinner, but I certainly don’t want to do that for every meal, and if I could chop down cocktail hour to get my food more quickly, I’d happily find other moments for social bonding.

Of course, game length isn’t the only consideration when assessing the pitch clock. I’m frequently asked in my chats if I think a given pitcher’s underperformance relative to expectation can be attributed to the clock. It can’t feel great to do a job for a number of years and suddenly experience such a monumental change in how you go about executing it. Steve Trachsel ain’t punching no time clock!

Another big question is whether the pitch clock, which can result in mechanical changes, could have an effect on injuries, a subject Will Sammon, Brittany Ghiroli and Eno Sarris explored for The Athletic after a high injury rate in April. While we obviously don’t have enough data to reach a verdict on the long-term effects of the clock (and things like Tommy John surgery count are still going to involve relatively small samples), as we near the halfway point of the season, we do have enough information to look at how the data are shaking out and arrive at some kind of preliminary conclusion about what’s going on.

At this point, I believe that the key to looking at these data beyond anecdotal information involves looking at changes in a pitcher’s pace of play. While this doesn’t encompass every aspect of a pitcher’s job, it doesn’t seem like a stretch that the pitchers who have had to change their pace the most will be the ones most affected by the clock. There’s quite a difference between Austin Pruitt chopping a single second off of his pace and Giovanny Gallegos paring down more than 10 seconds of the interregnum between his pitches.

For the purposes of looking at performance, I started with every pitcher who threw at least 50 innings in 2022 and at least 20 in 2023. That list included 229 pitchers, every single one of whom is pitching faster than last year. For those who are curious, here are the pitchers who have had their paces change the most and the least from last season to this one:

Pace of Play Changes, 2022 to 2023
Most Changed 2023 Pace 2022 Pace Change
Giovanny Gallegos 20.0 30.8 -10.8
Devin Williams 20.0 30.5 -10.5
Kenley Jansen 20.7 29.4 -8.7
Shohei Ohtani 18.3 26.6 -8.3
Josh Hader 18.4 26.7 -8.3
Taylor Rogers 18.6 26.7 -8.1
Jake Diekman 19.3 27.3 -8.0
Kyle Finnegan 21.1 29.0 -7.9
Emmanuel Clase 18.5 26.3 -7.8
Camilo Doval 18.8 26.6 -7.8
Ryan Helsley 20.4 28.2 -7.8
Cionel Pérez 18.7 26.4 -7.7
Gregory Soto 17.1 24.6 -7.5
Erasmo Ramírez 18.9 26.3 -7.4
Tanner Houck 17.0 24.3 -7.3
Least Changed 2023 Pace 2022 Pace Change
Mark Leiter Jr. 21.3 23.0 -1.7
Drew Smyly 18.9 20.7 -1.8
Zack Greinke 19.8 21.8 -2.0
Shane Bieber 17.8 19.9 -2.1
Max Scherzer 19.1 21.4 -2.3
Ranger Suárez 19.3 21.6 -2.3
Jesse Chavez 16.3 18.7 -2.4
Brady Singer 17.2 19.6 -2.4
David Peterson 17.9 20.4 -2.5
David Robertson 19.8 22.3 -2.5
Marcus Stroman 18.9 21.5 -2.6
Reid Detmers 18.5 21.1 -2.6
Brent Suter 15.5 18.1 -2.6
José Berríos 18.0 20.7 -2.7
Zach Davies 18.9 21.6 -2.7

To get a baseline expectation for performance, I used the preseason depth chart projections. Neither ZiPS nor Steamer penalized or rewarded pitchers for the pending pitch clock changes, so neither projection should be capturing the expected risk of pace changes. Just to be thorough, I’ll use both ERA and FIP, which has the side benefit of dealing with the slim possibility that changing the pitch clock has BABIP effects that would cause the ERA and FIP projections to diverge.

First up, we’ll look at how the divergence between 2023 projected and actual FIP interacts with pace change:

The projections assumed a slightly lower overall scoring environment than the one we’ve actually gotten. But as a whole, while there’s a very slight lean towards changes in pace having a relationship with underperformance, it hasn’t actually been a significant one, with a coefficient of determination (r-squared) of 0.012. If we change gears and look at change of pace versus the magnitude of the misses rather than their direction, nothing actually improves. The relationship between change of pace and overall accuracy in any direction is even tinier, with an r-squared of 0.0018.

Going to ERA also doesn’t move the needle:

Next verse, same as the first. ERA is generally a noisier stat than FIP to begin with, and when looking at the relationships, the microscopic thread between the variables becomes much thinner than that.

I also added age as a variable. After all, older players have been doing their routines far longer than younger ones, and many of the youngest players already have experience with the minor league pitch clock. But age didn’t improve any of the models here by even by a micro-skosh.

At least from these data, there is no compelling reason to think of the pitch clock as having a drastic, system-wide effect on pitcher performance.

But what about injuries?

Daniel R. Epstein, with the help of Derek Rhoades, looked at the injury data a month ago for Baseball Prospectus and found a real effect of increased injuries, at least in the early going and especially during spring training. I want to take a different approach, however, and look at the change in a pitcher’s pace more than the absolute pace of working. As with performance, there’s a reasonable argument to be made that if the changes in pitch pace, as opposed to working quickly/slowly, are causing injuries, then we’d expect to see more trips to the injured list for the players whose pace has changed the most. We do not have pace data on FanGraphs for pitchers who were injured in the spring (though it would have been nice), but if the effect is significant, we should see some relationship between pace change and injuries.

Since the bounty of data we have to fill out the statistical pantry isn’t as abundant as we’d like, I’m going with a simple approach here, looking at change of pace vs. a binary dummy variable (0/1) for whether a pitcher has appeared on the IL this year. Since pace doesn’t really have much of a luck element to it, I’m setting the limits very low, to pitchers with at least five innings pitched in both seasons.

Of the 447 pitchers who pitched at least five innings in both seasons, 123, or 27.5% of have appeared on the IL. Of the 50 pitchers with the largest changes of pace, nine (18.0%) have been on the IL, while eight (16.0%) of the pitchers with the smallest changes have been on the shelf.

Limiting the look to arm injuries, not including fractures and contusions, doesn’t change this distribution. Sixty of the 447 players have been or are currently on the IL with an arm injury of this type. Of the 50 pitchers whose pace has sped up the most, only one, Chris Martin, has been on the IL with an arm injury. He’s had a 1.31 FIP in 13 games since returning, so if he had a problem with the clock, it seems to have dissipated. For the least-changed pitchers, four have been on the IL with an arm injury.

For both injuries generally and arm injuries specifically, I did a logistic regression between change of pace and an appearance on the IL. The seven Tommy John surgeries for players with at least 5 IP in both seasons don’t provide enough data to do anything useful, but to satisfy any curiosity, Luis Garcia was the pitcher with the greatest change in pace. He trimmed 6.2 seconds, 82nd out of 447.

Again, there was no meaningful relationship. While logistic regressions don’t have an easy r-squared the way linear ones do, due to the nature of the data, whatever flavor of pseudo r-squared you use, whether likelihood or the more complex mathematical palette teasers, I found no relationship between these numbers. The very best model was only confident enough to range the modeled probability of a pitcher having appeared on the IL so far from 27.60% (for the most changed pitchers) to 27.63% (for the least changed pitchers). A simple likelihood chart demonstrates just how loose the relationship is. For each class of pitcher, the likelihood ratio reflects the increased injury probability related to all the pitchers:

Increase in Pace vs. Likelihood of Injury, Opening Day Through June 11
Increase in Pace Likelihood Ratio for Injury
>8.0 0.9989
7.0-7.9 0.9993
6.0-6.9 0.9996
5.0-5.9 0.9998
4.0-4.9 1.0001
3.0-3.9 1.0003
2.0-2.9 1.0006
<2.0 1.0009

When looking at changes in pace vs. injuries or performance, there isn’t much there yet, at least by this methodology. And if there are any meaningful effects hidden in the noise, we might expect them to dissipate over time as veteran pitchers become more accustomed to the game’s new cadence and young pitchers who never know professional baseball without a pitch clock are promoted. As with the minors, we’ve already seen violations decreased rapidly, from 203 pitcher violations in April to 165 in May to 42 in almost half of June.

Now, there are certainly limitations here. That these changes don’t seem to have affected the group as a whole does not mean that individual pitchers haven’t been impacted. Routines and bodies vary. None of this changes the frustration that some pitchers have expressed over the new rules (though many seem to have adapted fine). It also doesn’t mean that there isn’t a real effect that our data is simply too limited to capture at this point. There will certainly be other ways to look at these data once we have a longer period of time to examine. We should continue to examine the potential effects of the pitch clock on pitcher health and performance, and be open to adjustments as necessary; improvements to pace that come at the expense of pitcher’s arms don’t serve fans or players. Still, when it comes to the pitch clock as the potential cause of injuries or underperformance, the early data suggests a rather muted effect.

All statistics are through June 11.

Dan Szymborski is a senior writer for FanGraphs and the developer of the ZiPS projection system. He was a writer for from 2010-2018, a regular guest on a number of radio shows and podcasts, and a voting BBWAA member. He also maintains a terrible Twitter account at @DSzymborski.

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11 months ago

I never had you down as a Campari and soda guy, Dan. People really do contain multitudes.