In this week's podcast, I sit down again with Dr. Adam Maloof, Princeton assistant professor of Geosciences and athletics fellow for the baseball team.

This time he’s tackling one of the most overlooked problems in baseball analytics: pitch classification.

If you’ve ever looked at a scouting report and thought, “This guy doesn’t throw seven pitches,” you’re not alone. Dr. Maloof explains why existing pitch tagging data from popular systems (TrackMan, Synergy, and MLB’s StatCast) are often inconsistent, inaccurate, and difficult to use for data analysis.

Together, we discuss:

• Why MLB’s pitch classifier is trained on what pitchers call their own pitches (and why that’s a problem for hitters and analysts)
• How Dr. Maloof analyzed nearly 5 million college pitches using a “blank slate” approach inspired by geology
• The “color wheel” analogy: why pitches within a category (fastballs, breaking balls, changeups) exist on a continuous spectrum with no natural boundaries
• How Princeton’s staff uses reclassified data for scouting, pitch design, and pitching machine programming
• Pitch tunneling: what it is, how they measure it, and how they’re teaching it to pitchers
• The role of arm slot, biometrics, and what’s next for this research

Read Dr. Maloof's article on pitch tagging: How to Classify a Pitch

https://medium.com/sabr-tooth-tigers/how-to-classify-a-pitch-a4805ce35082

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