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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.