2013/04/05 by Michael A. Pane, Pane, Michael A., Samuel Ventura +5 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Engineering · #Applications (stat.AP) #FOS: Computer and information sciences #Multidisciplinary Science and Engineering Research #Sports Analytics and Performance #Sports Dynamics and Biomechanics
paper · pdf · doi:10.48550/arxiv.1304.1756
openalex publication_date 2013/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.