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Activity Number: 107
Type: Invited
Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Sports
Abstract - #307411
Title: Using Pattern Recognition to Classify Pitch Types from MLB PITCHf/x Data
Author(s): Michael D. Schader*+
Companies: George Mason University
Keywords: baseball ; pitchfx ; pattern recognition ; machine learning ; classification
Abstract:

The volume of granular data available to baseball analysts today provides an opportunity to discover hidden relationships and non-obvious patterns. The field of Pattern Recognition supplies the tools to work with such data. In this effort, we use PITCHf/x trajectory parameters to train various types of classifiers to determine pitch type. We experiment with ways to improve accuracy by incorporating the context of each individual pitcher into the feature set, rather than resorting to building a specific classifier for every player. Our final results show excellent accuracy at duplicating the pitch classifications of the official MLB system, and could be applied to other taxonomies and other data sets.


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