@inproceedings{af226b93f8b343949fe9f61fe6d327ed,
title = "Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches",
abstract = "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.",
keywords = "attention mechanism, badminton language representation, shot influence, sport analytics",
author = "Wang, {Wei Yao} and Chan, {Teng Fong} and Yang, {Hui Kuo} and Wang, {Chih Chuan} and Fan, {Yao Chung} and Peng, {Wen Chih}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st IEEE International Conference on Data Mining, ICDM 2021 ; Conference date: 07-12-2021 Through 10-12-2021",
year = "2021",
doi = "10.1109/ICDM51629.2021.00178",
language = "English",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1397--1402",
editor = "James Bailey and Pauli Miettinen and Koh, {Yun Sing} and Dacheng Tao and Xindong Wu",
booktitle = "Proceedings - 21st IEEE International Conference on Data Mining, ICDM 2021",
address = "美國",
}