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

Chih Chang Yu, Chih Ching Chang, Hsu Yung Cheng

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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.

原文???core.languages.en_GB???
主出版物標題ICMEW 2022 - IEEE International Conference on Multimedia and Expo Workshops 2022, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665472180
DOIs
出版狀態已出版 - 2022
事件2022 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2022 - Taipei City, Taiwan
持續時間: 18 7月 202222 7月 2022

出版系列

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

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???event.eventtypes.event.conference???2022 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2022
國家/地區Taiwan
城市Taipei City
期間18/07/2222/07/22

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