MuLMINet: Multi-Layer Multi-Input Transformer Network with Weighted Loss
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 17 Jul 2023.
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5 of 11 references.
References · 5
- 2021ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badmintoncited by 21
- 2021Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matchescited by 10
- 2022Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badmintoncited by 10
- 2023Benchmarking Stroke Forecasting with Stroke-Level Badminton Datasetcited by 8
- 2021Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environmentscited by 6
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