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In recent years, badminton analytics has drawn attention due to theadvancement of artificial intelligence and the efficiency of data collection.While there is a line of effective applications to improve and investigateplayer performance, there are only a few public badminton datasets that can beused for researchers outside the badminton domain. Existing badminton singlesdatasets focus on specific matchups; however, they cannot provide comprehensivestudies on different players and various matchups. In this paper, we provide abadminton singles dataset, ShuttleSet22, which is collected from high-rankingmatches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies inthe training set, 1,400 strokes in 450 rallies in the validation set, and 2,040strokes in 654 rallies in the testing set with detailed stroke-level metadatawithin a rally. To benchmark existing work with ShuttleSet22, we test thestate-of-the-art stroke forecasting approach, ShuttleNet, with thecorresponding stroke forecasting task, i.e., predict the future strokes basedon the given strokes of each rally. We also hold a challenge, Track 2:Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAIBadminton Challenge 2023 to boost researchers to tackle this problem. Thebaseline codes and the dataset will be made available onhttps://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.

ShuttleSet22: Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset