TY - GEN
T1 - Decide the Next Pitch
T2 - 2022 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2022
AU - Yu, Chih Chang
AU - Chang, Chih Ching
AU - Cheng, Hsu Yung
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - LSTM
KW - Pitch prediction
KW - attention model
KW - sport analysis
UR - http://www.scopus.com/inward/record.url?scp=85138064401&partnerID=8YFLogxK
U2 - 10.1109/ICMEW56448.2022.9859411
DO - 10.1109/ICMEW56448.2022.9859411
M3 - 會議論文篇章
AN - SCOPUS:85138064401
T3 - ICMEW 2022 - IEEE International Conference on Multimedia and Expo Workshops 2022, Proceedings
BT - ICMEW 2022 - IEEE International Conference on Multimedia and Expo Workshops 2022, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 July 2022 through 22 July 2022
ER -