In this paper, our objective is to improve the performance of the existing framework ShuttleNet in predicting badminton shot types and locations by leveraging past strokes. We participated in the CoachAI Badminton Challenge at IJCAI 2023 and achieved significantly better results compared to the baseline. Ultimately, our team achieved the first position in the competition and we made our code available.

翻译：本文旨在通过利用历史击球数据，提升现有框架ShuttleNet在预测羽毛球击球类型与落点位置方面的性能。我们参加了IJCAI 2023举办的CoachAI羽毛球挑战赛，相较于基线方法取得了显著更优的结果。最终，本团队在竞赛中荣获第一名，并公开了我们的代码。