Welcome to Meatball Watch 2025

I’d like to present the meatball-iest pitch thrown so far in 2025:

I know, I know! I said that, but it’s just a foul ball. Hear me out, though, because I can put some data behind my claim. Here at FanGraphs, PitchingBot, our in-house pitch modeling system, looks at every single pitch thrown, regresses it against a huge database of past pitches, and uses some mathematical ingenuity to turn that into the expected outcomes of the pitch. That’s not the same as knowing which pitch is most likely to turn into a home run, but luckily, a good bit of mathematical wrangling can turn pitch grades into home run percentages.
Last year, I worked out the rough contours of converting PitchingBot grades into home run likelihood. This year, I’ve expanded that methodology to try to learn a little bit more about the pitchers doing the meatballing. If you’d like to skip through the how, you can head right down to the table labeled “Meatball Mongers.” If you’re here for the nitty gritty of turning pitch metrics into home run likelihood, though, here’s how I did it.
That Trent Thornton fastball had a lot of things working against it, and those things help explain how PitchingBot estimates the chances that a pitch will be hit for a home run. PitchingBot has a flowchart that explains how the model works. Here’s how the system assesses every pitch it grades:

Hey, a convenient “start here” label! How great! The “swing model” takes location, count, pitch type, movement, platoon matchups, and pretty much everything else you can imagine into account and guesses at the likelihood of a batter swinging at each pitch. That Thornton fastball was down the middle in an 0-1 count, and it’s not a particularly deceptive offering. In other words, hitters often swing at fastballs like that – 92.7% of the time, per PitchingBot’s model. Read the rest of this entry »






