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題名 Performance prediction in major league baseball by long short-term memory networks
作者 蔡炎龍
Tsai, Yen-Lung
Sun, Hsuan-Cheng;Lin, Tse-Yu
貢獻者 應數系
關鍵詞 Deep learning; Long short-term memory; Player performance prediction; Baseball projection
日期 2022-02
上傳時間 20-十月-2022 16:35:06 (UTC+8)
摘要 Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction systems and many types of researches that attempt to provide accurate predictions and help domain users. However, it is a lack of studies about the predicting method or systems based on deep learning. Deep learning models had proven to be the greatest solutions in different fields nowadays, so we believe they could be tried and applied to the prediction problem in baseball. Hence, the predicting abilities of deep learning models are set to be our research problem in this paper. As a beginning, we select numbers of home runs as the target because it is one of the most critical indexes to understand the power and the talent of baseball hitters. Moreover, we use the sequential model Long Short-Term Memory as our main method to solve the home run prediction problem in Major League Baseball. We compare models’ ability with several machine learning models and a widely used baseball projection system, sZymborski Projection System. Our results show that Long Short-Term Memory has better performance than others and has the ability to make more exact predictions. We conclude that Long Short-Term Memory is a feasible way for performance prediction problems in baseball and could bring valuable information to fit users’ needs.
關聯 International Journal of Data Science and Analytics
資料類型 article
DOI https://doi.org/10.1007/s41060-022-00313-4
dc.contributor 應數系
dc.creator (作者) 蔡炎龍
dc.creator (作者) Tsai, Yen-Lung
dc.creator (作者) Sun, Hsuan-Cheng;Lin, Tse-Yu
dc.date (日期) 2022-02
dc.date.accessioned 20-十月-2022 16:35:06 (UTC+8)-
dc.date.available 20-十月-2022 16:35:06 (UTC+8)-
dc.date.issued (上傳時間) 20-十月-2022 16:35:06 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142474-
dc.description.abstract (摘要) Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction systems and many types of researches that attempt to provide accurate predictions and help domain users. However, it is a lack of studies about the predicting method or systems based on deep learning. Deep learning models had proven to be the greatest solutions in different fields nowadays, so we believe they could be tried and applied to the prediction problem in baseball. Hence, the predicting abilities of deep learning models are set to be our research problem in this paper. As a beginning, we select numbers of home runs as the target because it is one of the most critical indexes to understand the power and the talent of baseball hitters. Moreover, we use the sequential model Long Short-Term Memory as our main method to solve the home run prediction problem in Major League Baseball. We compare models’ ability with several machine learning models and a widely used baseball projection system, sZymborski Projection System. Our results show that Long Short-Term Memory has better performance than others and has the ability to make more exact predictions. We conclude that Long Short-Term Memory is a feasible way for performance prediction problems in baseball and could bring valuable information to fit users’ needs.
dc.format.extent 106 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) International Journal of Data Science and Analytics
dc.subject (關鍵詞) Deep learning; Long short-term memory; Player performance prediction; Baseball projection
dc.title (題名) Performance prediction in major league baseball by long short-term memory networks
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1007/s41060-022-00313-4
dc.doi.uri (DOI) https://doi.org/10.1007/s41060-022-00313-4