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Right after FanGraphs published my piece on the Kirby Index, the metric’s namesake lost his touch. George Kirby’s trademark command — so reliable that I felt comfortable naming a statistic after him — fell off a cliff. While the walk rate remained under control, the home run rate spiked; he allowed seven home runs in May, all on pitches where he missed his target by a significant margin.
Watching the namesake of my new metric turn mediocre immediately following publication was among the many humbling experiences of publishing this story. Nevertheless, I wanted to revisit the piece. For one, it’s December. And writing the story led me down a fascinating rabbit hole: While I learned that the Kirby Index has its flaws, I also learned a ton about contemporary efforts to quantify pitcher command.
But first, what is the Kirby Index? I found that release angles, in concert with release height and width, almost perfectly predicted the location of a pitch. If these two variables told you almost everything about the location of a pitch, then a measurement of their variation for individual pitchers could theoretically provide novel information about pitcher command.
This got a few people mad on Twitter, including baseball’s eminent physicist Alan Nathan and Greg Rybarczyk, the creator of the “Hit Tracker” and a former member of the Red Sox front office. These two — particularly Rybarczyk — took issue with my use of machine learning to make these predictions, arguing that my use of machine learning suggested I didn’t understand the actual mechanics of why a pitch goes where it goes.
“You’re spot on, Alan,” wrote Rybarczyk. “The amazement that trajectory and launch parameters are strongly associated with where the ball ends up can only come from people who see tracking data as columns of digits rather than measurements of reality that reflect the underlying physics.”
While the tone was a bit much, Rybarczyk had a point. My “amazement” would have been tempered with a more thorough understanding of how Statcast calculates the location where a pitch crosses home plate. After publication, I learned that the nine-parameter fit explains why pitch location could be so powerfully predicted by release angles.
The location of a pitch is derived from the initial velocity, initial release point, and initial acceleration of the pitch in three dimensions. (These are the nine parameters.) Release angles are calculated using initial velocity and initial release point. Because the location of the pitch and the release angle are both derived from the 9P fit, it makes sense that they’d be almost perfectly correlated.
This led to a reasonable critique: If release angles are location information in a different form, why not just apply the same technique of measuring variation on the pitch locations themselves? This is a fair question. But using locations would have undermined the conclusion of that Kirby Index piece — that biomechanical data like release angles could improve the precision of command measurements.
Teams, with their access to KinaTrax data, could create their own version of the Kirby Index, not with implied release angles derived from the nine-parameter fit, but with the position of wrists and arms captured at the moment of release. The Kirby Index piece wasn’t just about creating a new way to measure command; I wanted it to point toward one specific way that the new data revolution in baseball would unfold.
But enough about that. It’s time for the leaderboards. I removed all pitchers with fewer than 500 fastballs. Here are the top 20 in the Kirby Index for the 2024 season:
2024 Kirby Index Leaders
SOURCE: Baseball Savant
Minimum 500 fastballs thrown.
And here are the bottom 20:
2024 Kirby Index Laggards
SOURCE: Baseball Savant
Minimum 500 fastballs thrown.
A few takeaways for me: First, I am so grateful Kirby got it together and finished in the top three. Death, taxes, and George Kirby throwing fastballs where he wants. Second, the top and bottom of the leaderboards are satisfying. Cody Bradford throws 89 and lives off his elite command, and Joe Boyle — well, there’s a reason the A’s threw him in as a piece in the Jeffrey Springs trade despite his otherworldly stuff. Third, there are guys on the laggard list — Seth Lugo and Miles Mikolas, in particular — who look out of place.
Mikolas lingered around the bottom of the leaderboards all year, which I found curious. Mikolas, after all, averages just 93 mph on his four-seam fastball; one would imagine such a guy would need to have elite command to remain a viable major league starter, and that league-worst command effectively would be a death sentence. Confusing this further, Mikolas avoided walks better than almost anyone.
Why Mikolas ranked so poorly in the Kirby Index while walking so few hitters could probably be the subject of its own article, but for the purposes of this story, it’s probably enough to say that the Kirby Index misses some things.
An example: Mikolas ranked second among all pitchers in arm angle variation on four-seam fastballs, suggesting that Mikolas is intentionally altering his arm angle from pitch to pitch, likely depending on whether the hitter is left-handed or right-handed. This is just one reason why someone might rank low in the Kirby Index. Another, as I mentioned in the original article, is that a pitcher like Lugo might be aiming at so many different targets that it fools a metric like the Kirby Index.
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