Spin Doctor BB
@spindoctorbb.bsky.social
Bluesky account for spindoctorbb.substack.com.
sneak peek 2 from tunnelling article:
in order to quantify tunnelling, you need some kind of model to know just based on plain singular pitch data how good a pitch is. and after some headbashing, i threw together a xgbclassifier pitch quality model (here's 2023's best pitches)
in order to quantify tunnelling, you need some kind of model to know just based on plain singular pitch data how good a pitch is. and after some headbashing, i threw together a xgbclassifier pitch quality model (here's 2023's best pitches)
November 19, 2024 at 6:13 AM
sneak peek 2 from tunnelling article:
in order to quantify tunnelling, you need some kind of model to know just based on plain singular pitch data how good a pitch is. and after some headbashing, i threw together a xgbclassifier pitch quality model (here's 2023's best pitches)
in order to quantify tunnelling, you need some kind of model to know just based on plain singular pitch data how good a pitch is. and after some headbashing, i threw together a xgbclassifier pitch quality model (here's 2023's best pitches)
Here are the feature importances of these models, key note is that Xs/Zs close to the beginning/end of the 15 ft path are important, as well as player_name (introduced as a categorical variable, so within pitcher effects do exist)
November 17, 2024 at 10:26 PM
Here are the feature importances of these models, key note is that Xs/Zs close to the beginning/end of the 15 ft path are important, as well as player_name (introduced as a categorical variable, so within pitcher effects do exist)
sneak peeks from tunnelling article:
a hitter (or theoretical machine learning powered hitter) can predict final plate x (r2 = .78) and final plate z (r2 = .94) just from ball flight positions within the first 15 feet decently well (30 feet was too well)
a hitter (or theoretical machine learning powered hitter) can predict final plate x (r2 = .78) and final plate z (r2 = .94) just from ball flight positions within the first 15 feet decently well (30 feet was too well)
November 17, 2024 at 6:38 AM
sneak peeks from tunnelling article:
a hitter (or theoretical machine learning powered hitter) can predict final plate x (r2 = .78) and final plate z (r2 = .94) just from ball flight positions within the first 15 feet decently well (30 feet was too well)
a hitter (or theoretical machine learning powered hitter) can predict final plate x (r2 = .78) and final plate z (r2 = .94) just from ball flight positions within the first 15 feet decently well (30 feet was too well)