Bridging the Gap: Doubles Badminton Analysis with Singles-Trained Models
This work addresses the understudied problem of doubles badminton analytics for sports researchers, though it is incremental as it adapts existing methods to a new format.
The paper tackled the lack of analysis for doubles badminton by transferring singles-trained models to doubles, demonstrating feasibility with a pose-based shot recognition system that uses keypoints and a Transformer classifier.
Badminton is known as one of the fastest racket sports in the world. Despite doubles matches being more prevalent in international tournaments than singles, previous research has mainly focused on singles due to the challenges in data availability and multi-person tracking. To address this gap, we designed an approach that transfers singles-trained models to doubles analysis. We extracted keypoints from the ShuttleSet single matches dataset using ViT-Pose and embedded them through a contrastive learning framework based on ST-GCN. To improve tracking stability, we incorporated a custom multi-object tracking algorithm that resolves ID switching issues from fast and overlapping player movements. A Transformer-based classifier then determines shot occurrences based on the learned embeddings. Our findings demonstrate the feasibility of extending pose-based shot recognition to doubles badminton, broadening analytics capabilities. This work establishes a foundation for doubles-specific datasets to enhance understanding of this predominant yet understudied format of the fast racket sport.