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Using PITCHf/x to model the dependence of strikeout rate on the predictability of pitch sequences

We develop a model for pitch sequencing in baseball that is defined by pitch-to-pitch correlation in location, velocity, and movement. The correlations quantify the average similarity of consecutive pitches and provide a measure of the batter’s ability to predict the properties of the upcoming pitch. We examine the characteristics of the model for a set of major league pitchers using PITCHf/x data for nearly three million pitches thrown over seven major league seasons. After partitioning the data according to batter handedness, we show that a pitcher’s correlations for velocity and movement are persistent from year-to-year. We also show that pitch-to-pitch correlations are significant in a model for pitcher strikeout rate and that a higher correlation, other factors being equal, is predictive of fewer strikeouts. This finding is consistent with experiments showing that swing errors by experienced batters tend to increase as the differences between the properties of consecutive pitches increase. We provide examples that demonstrate the role of pitch-to-pitch correlation in the strikeout rate model.
© Copyright 2017 Journal of Sports Analytics. IOS Press. All rights reserved.

Subjects: baseball modelling prognosis game action
Notations: sport games technical and natural sciences
Tagging: Big Data
DOI: 10.3233/JSA-170103
Published in: Journal of Sports Analytics
Published: 2017
Volume: 3
Issue: 2
Pages: 93-101
Document types: article
Language: English
Level: advanced