Decide the Next Pitch: A Pitch Prediction Model Using Attention-Based LSTM
Chih-Chang Yu, Chih-Ching Chang, Hsu-Yung Cheng · 2022
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.