In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used by researchers outside the badminton domain. Existing badminton singles datasets focus on specific matchups; however, they cannot provide comprehensive studies on different players and various matchups. In this paper, we provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040 strokes in 654 rallies in the testing set, with detailed stroke-level metadata within a rally. To benchmark existing work with ShuttleSet22, we hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge @ IJCAI 2023, to encourage researchers to tackle this real-world problem through innovative approaches and to summarize insights between the state-of-the-art baseline and improved techniques, exchanging inspiring ideas. The baseline codes and the dataset are made available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.

翻译：近年来，随着人工智能技术的发展和数据采集效率的提升，羽毛球数据分析引起了广泛关注。尽管已有诸多有效应用用于提升和分析运动员表现，但可供羽毛球领域外研究者使用的公开数据集仍然有限。现有的羽毛球单打数据集集中于特定对决，无法对不同运动员及多种对阵类型进行综合性研究。本文提供了羽毛球单打数据集ShuttleSet22，该数据集收集自2022年高级别赛事。ShuttleSet22包含训练集2888个回合中的30172次击球、验证集450个回合中的1400次击球、测试集654个回合中的2040次击球，并附带回合内详细的击球级元数据。为在ShuttleSet22上对现有工作进行基准测试，我们在CoachAI羽毛球挑战赛@IJCAI 2023中设立了第二赛道："预测羽毛球回合中基于回合制的未来击球"，旨在激励研究者通过创新方法解决这一现实问题，并总结最先进基线技术与改进技术之间的见解，交流启发性思路。基线代码及数据集已公开于https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023。