Authors: Glenn Healey
The deployment of sensors that characterize the trajectory of pitches and batted balls in three dimensions provides the opportunity to assign an intrinsic value to a pitch that depends on its physical properties and not on its observed outcome. We exploit this opportunity by utilizing a Bayesian framework to map five-dimensional PITCHf/x velocity, movement, and location vectors to pitch intrinsic values. HITf/x data is used by the model to obtain intrinsic quality-of-contact values for batted balls that are invariant to the defense, ballpark, and atmospheric conditions. Separate mappings are built to accommodate the effects of count and batter/pitcher handedness. A kernel method is used to generate nonparametric estimates for the component probability density functions in Bayes theorem while cross-validation enables the model to adapt to the size and structure of the data.
Comments: 24 Pages.
[v1] 2017-03-16 12:28:32
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