Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset
arxiv(2023)
摘要
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.
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