Decide the Next Pitch: A Pitch Prediction Model Using Attention-Based LSTM

Chih Chang Yu, Chih Ching Chang, Hsu Yung Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Information collection and analysis have played a very important role in high-level baseball competitions. Knowing opponent's possible strategies or weakness can help own team plan adequate countermeasures. The purpose of this study is to explore how artificial intelligence technology can be applied to this domain. This study focuses on the pitching events in baseball. The goal is to predict the pitch types that a pitcher may throw in the next pitch according to the situation on the field. To achieve this, we mine discriminative features from baseball statistics and propose a stacked long-term and short-term memory model (LSTM) with attention mechanism. Experimental data come from the pitching data of 201 pitchers in Major League Baseball from 2016 to 2021. By collecting information of pitchers' pitching statistics and on-field situations, results show that the average accuracy rate reaches 76.7%, outperforming conventional machine learning prediction models.

Original languageEnglish
Title of host publicationICMEW 2022 - IEEE International Conference on Multimedia and Expo Workshops 2022, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665472180
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2022 - Taipei City, Taiwan
Duration: 18 Jul 202222 Jul 2022

Publication series

NameICMEW 2022 - IEEE International Conference on Multimedia and Expo Workshops 2022, Proceedings

Conference

Conference2022 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2022
Country/TerritoryTaiwan
CityTaipei City
Period18/07/2222/07/22

Keywords

  • attention model
  • LSTM
  • Pitch prediction
  • sport analysis

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