Original Research
Every piece of analysis is original, methodologically rigorous, and transparent about its data and methods. If you can't see the work, you can't trust the numbers.
Which Pitchers Can You Predict?
We trained XGBoost on 729,827 pitches to predict the next pitch. The broad model barely beat the count baseline — but five pitchers stood out. Chris Sale's next pitch is predictable 58% of the time.
Catcher Framing in the ABS Era
74 catchers, an 18.7 percentage point gap between best and worst framers, and a counterintuitive finding: in the ABS challenge era, good framing may gain value because it influences the batter's decision to challenge.
The Anatomy of a Missed Call
379,155 called pitches, a 7.2% miss rate, and the half-inch cliff where human judgment breaks down. Plus: challenge value by count, catcher framing ranges, and where umpires miss most.
The Count Tells You Everything
We built a pitch prediction model on 729,827 pitches. XGBoost beat the marginal baseline by 2.1pp — but beat the count-conditional baseline by only 0.5pp. The count already encodes nearly all predictive information.
Two Myths the Data Kills
Pitchers don't measurably lose their command late in games (r = 0.007). Plate discipline doesn't predict future hitting (r = -0.019). Two baseball beliefs that don't survive 729,827 pitches.
Coming Soon
New analysis is published throughout the season as the data reveals interesting stories.
ABS Challenge Strategy Analysis
Which counts should you challenge? How many challenges should you save for late innings? We'll model optimal strategy as challenge data accumulates.