Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches

Wei Yao Wang, Teng Fong Chan, Hui Kuo Yang, Chih Chuan Wang, Yao Chung Fan, Wen-Chih Peng

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)

摘要

Identifying significant shots in a rally is important for evaluating players' performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data is remained untouched. In this paper, we introduce a badminton language to fully describe the process of the shot and propose a deep learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result. Our model incorporates an attention mechanism to enable the transparency of the action sequence to the rally result, which is essential for badminton experts to gain interpretable predictions. Experimental evaluation based on a real-world dataset demonstrates that our proposed model outperforms the strong baselines. The source code is publicly available at https://github.com/wywyWang/Shot-Influence.

原文English
主出版物標題Proceedings - 21st IEEE International Conference on Data Mining, ICDM 2021
編輯James Bailey, Pauli Miettinen, Yun Sing Koh, Dacheng Tao, Xindong Wu
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1397-1402
頁數6
ISBN(電子)9781665423984
DOIs
出版狀態Published - 2021
事件21st IEEE International Conference on Data Mining, ICDM 2021 - Virtual, Online, New Zealand
持續時間: 7 12月 202110 12月 2021

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
2021-December
ISSN(列印)1550-4786

Conference

Conference21st IEEE International Conference on Data Mining, ICDM 2021
國家/地區New Zealand
城市Virtual, Online
期間7/12/2110/12/21

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