Trouble With The Curve: Improving MLB Pitch Classification
Abstract
The PITCHf/x database has allowed the statistical analysis of of Major League Baseball (MLB) to flourish since its introduction in late 2006. Using PITCHf/x, pitches have been classified by hand, requiring considerable effort, or using neural network clustering and classification, which is often difficult to interpret. To address these issues, we use model-based clustering with a multivariate Gaussian mixture model and an appropriate adjustment factor as an alternative to current methods. Furthermore, we describe a new pitch classification algorithm based on our clustering approach to address the problems of pitch misclassification. We illustrate our methods for various pitchers from the PITCHf/x database that covers a wide variety of pitch types.
Keywords
Cite
@article{arxiv.1304.1756,
title = {Trouble With The Curve: Improving MLB Pitch Classification},
author = {Michael A. Pane and Samuel L. Ventura and Rebecca C. Steorts and A. C. Thomas},
journal= {arXiv preprint arXiv:1304.1756},
year = {2013}
}