OpenCommand @open_command
Open-source command reports! By @tomdoyo (& maybe @lukeblomm) github.com/tomdoyo/open-c… Joined July 2026-
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So, if command's internal reliability is 0.5 and stuff's is 0.5, we can only observe ~30% of command's effects. Even if command 100% stabilizes at pitch 1, it still needs to wait for stuff to stabilize to be effective. 3/3
The problem is that command's influence is observable *conditional* on stuff. (Raw command is uncorrelated to ERA) It's a huge survivorship bias (good stuff guys get away with bad command) - there's a negative correlation between stuff and command. 2/3
3 things are true at the same time: - Command is worth 1/3 Stuff - Command stabilizes almost as fast as Stuff - Adding Command on top of Stuff doesn't dramatically improve ERA prediction Why? 🧵 1/3
@GoCubs49 @RTS_Owen @pitchprofiler Doesn't explain the K% hockey stick though
As @pitchprofiler & others mentioned, maybe there's some "minimum command" to be a viable pitcher (throw enough strikes). I managed to find *some* hockey stick going on but it looks like it's driven by K% dropping (not BB% going up disproportionately). It's hard to show in MLB because these pitchers already proved they could throw strikes in the minors!
@GoCubs49 @pitchprofiler Yeah idk what's going on
@GoCubs49 @pitchprofiler Oh yeah there is, worse command pitchers have better stuff, but the hockey stick with K% (might) mean worst command pitchers have worse stuff which is weird
@GoCubs49 @pitchprofiler On the surface it means the opposite: the worst command pitchers also have worst stuff. This is despite removing position players. But maybe there's something in what you said, maybe there's only so much a good stuff helps you with chase
We can adjust for tendencies by offsetting the target by the avg pitcher × pitch type miss pattern. E.g. Logan Webb's changeup averages 13in below the glove This is the current math behind "inferred" targets. But there are 2 huge flaws with it. Can anyone guess what they are?
I'll be using this account to publicly share the results of OpenCommand, a computer-vision based command tracker. Ideas in mind: - MLB command leaderboards - Target maps - Pitcher command trends - Pitcher tactics changes - Reply to this tweet if you have ideas!
Some news: I decided to open source my command project. It's called OpenCommand. This means anyone can see the code (and even the data)! I hope this gives more people a chance to pursue and share cutting edge baseball research. Anyways, here's how to measure command: 🧵 1/N
@JenMineau13 There should be but I'm not sure what the y axis should be in that case
For reference, going from worst stuff in the league to best is worth 3 ERA. So, stuff is arguably 3x as important as command (at the MLB level)!
Going from worst command in the league to best is worth 1 ERA! There's a strong correlation between xERA (unexplained by Stuff+) and command. And it gets really interesting because some pitch types are way more dependent on command than others (see below)
@rogercfr Good point but it's stuff-residualized so the only survival bias we have are pitching iq, arsenal effects etc
@pitchprofiler That's actually a good take. I should look into whether there's some sort of a command threshold to be part of the MLB
@whipstache Yeah maybe because their chase rates are way lower
Going from worst command in the league to best is worth 1 ERA! There's a strong correlation between xERA (unexplained by Stuff+) and command. And it gets really interesting because some pitch types are way more dependent on command than others (see below)
Note: This wasn't obvious before I managed to remove location leakage from Stuff+ x.com/tomdoyo/status…
The biggest remaining flaw with stuff models is that they're secretly location models. Contrary to popular belief, there's a trivial way to adjust for this. 🧵
Fastballs are almost twice as command dependent as Breaking balls. Anyone have an idea why?
Justin Martinez dotted SIX out of 9 pitches during his immaculate inning!
Thanks for reading. In the future I'll be focusing on analyzing pitcher-batter matchups!
TL;DR Measurement errors increase avg estimated miss, so lower avg estimated miss is a good indicator for small measurement error. OpenCommand's target estimation is about 4in (or less) away from true target each pitch. This is among the best in baseball, despite lacking advanced scouting and situational adjustments.
OpenCommand might actually be the most accurate command tracker in the world. It estimates intended targets with the lowest error! (TL;DR at the end) 🧵
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