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

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Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health (CyberDI 2019, CyberLife 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1138))

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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Acknowledgment

This work is partially supported by the Undergraduate Summer Research Award program at Cleveland State University.

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Authors and Affiliations

  1. Georgia Institute of Technology, North Avenue, Atlanta, GA, 30332, USA

    Dylan Siegler

  2. Duke University, Durham, NC, 27708, USA

    Reed Chen

  3. Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, 44115, USA

    Michael Fasko Jr. & Wenbing Zhao

  4. School of Reliability and Systems Engineering, Beihang University, 37 Xueyuan Road, Beijing, 100191, China

    Shunkun Yang

  5. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Xiong Luo

Authors
  1. Dylan Siegler
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  2. Reed Chen
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  3. Michael Fasko Jr.
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  4. Shunkun Yang
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  5. Xiong Luo
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  6. Wenbing Zhao
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Corresponding author

Correspondence to Wenbing Zhao .

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Editors and Affiliations

  1. University of Science and Technology, Beijing, China

    Huansheng Ning

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© 2019 Springer Nature Singapore Pte Ltd.

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Siegler, D., Chen, R., Fasko, M., Yang, S., Luo, X., Zhao, W. (2019). Semi-automated Development of a Dataset for Baseball Pitch Type Recognition. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_25

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  • DOI: https://doi.org/10.1007/978-981-15-1925-3_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1924-6

  • Online ISBN: 978-981-15-1925-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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