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2019 | OriginalPaper | Chapter

Semi-automated Development of a Dataset for Baseball Pitch Type Recognition

Authors : Dylan Siegler, Reed Chen, Michael Fasko Jr., Shunkun Yang, Xiong Luo, Wenbing Zhao

Published in: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health

Publisher: Springer Singapore

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Abstract

In this paper, we report our work on developing a new dataset for baseball pitch type recognition based on youtube videos of the US Major League Baseball games. The core innovation is a largely automated procedure to extract relevant clips from the full game, and automatically label the clips by aligning the infographic information included in the broadcast and the PitchF/X data. We adopted the Needleman-Wunsch algorithm to address the challenges imposed by the aligning the two streams of data based on pitch speed, i.e., minimize gaps and mismatches between the two streams. Manual inspection is used only to select games that include infographic information for clip extraction and to remove erroneous clips for improve the quality of the dataset.

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Metadata
Title
Semi-automated Development of a Dataset for Baseball Pitch Type Recognition
Authors
Dylan Siegler
Reed Chen
Michael Fasko Jr.
Shunkun Yang
Xiong Luo
Wenbing Zhao
Copyright Year
2019
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-1925-3_25

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