计算机科学
定位
人工智能
定位关键字
指导
弹丸
框架(结构)
机器学习
期限(时间)
杠杆(统计)
提取器
化学
物理
管理
有机化学
结构工程
量子力学
工艺工程
工程类
经济
作者
Wei-Yao Wang,Teng-Fong Chan,Wen-Chih Peng,Hui-Kuo Yang,Chih-Chuan Wang,Yao-Chung Fan
出处
期刊:ACM Transactions on Intelligent Systems and Technology
[Association for Computing Machinery]
日期:2022-09-23
卷期号:14 (1): 1-22
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 has remained untouched. In this article, we introduce a badminton language to fully describe the process of the shot, and we 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 between the action sequence and 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. We also conducted case studies to show the ability to enhance players’ decision-making confidence and to provide advanced insights for coaching, which benefits the badminton analysis community and bridges the gap between the field of badminton and computer science.
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