The increasing use of artificial intelligence (AI) technology in turn-based sports, such as badminton, has sparked significant interest in evaluating strategies through the analysis of match video data. Predicting future shots based on past ones plays a vital role in coaching and strategic planning. In this study, we present a Multi-Layer Multi-Input Transformer Network (MuLMINet) that leverages professional badminton player match data to accurately predict future shot types and area coordinates. Our approach resulted in achieving the runner-up (2nd place) in the IJCAI CoachAI Badminton Challenge 2023, Track 2. To facilitate further research, we have made our code publicly accessible online, contributing to the broader research community's knowledge and advancements in the field of AI-assisted sports analysis.

翻译：随着人工智能技术在羽毛球等回合制体育项目中的日益广泛应用，通过分析比赛视频数据来评估策略引起了研究者的极大兴趣。基于历史击球预测未来击球在战术指导与战略规划中发挥着关键作用。本研究提出了一种多层多输入Transformer网络（MuLMINet），利用职业羽毛球运动员的比赛数据准确预测未来击球类型及区域坐标。该方案在IJCAI CoachAI 2023羽毛球挑战赛的Track 2中荣获亚军（第二名）。为促进后续研究，我们已将代码公开至互联网，为AI辅助体育分析领域的知识共享与技术进步贡献力量。