Badmintonvision: a deep learning framework for automated tactical analysis in elite badminton

Tactical analysis in badminton is critical for performance optimization, yet traditional manual video review is time-consuming, subjective, and difficult to scale. Existing deep learning approaches struggle with rapid stroke recognition from single frames and require extensive labelled data. We present BadmintonVision, an integrated framework combining self-supervised learning (SSL), temporal modelling, and tactical analysis for automated badminton analysis. The detection model is pretrained on 139,501 unlabelled images using ConvNeXt V2. We develop MotionFormer, a Transformer-based module processing 10-frame windows (400 ms) to capture complete stroke cycles and construct a dataset of 30,000 expert-annotated images from BWF World Championships (2019-2025) covering five action classes. BadmintonVision achieves 94.1% detection accuracy (mAP@0.5), with SSL pretraining providing a 9.8% relative gain in stroke recognition and temporal modelling providing an additional 6.3% relative gain. Movement analysis reveals that scoring rallies correlate with higher path efficiency (Cohen's d = 1.77) and shorter recovery times (d = - 1.56), though these correlational findings warrant cautious interpretation. In an exploratory, gender-stratified analysis, Markov-chain modelling further suggests preliminary, gender-associated regularities in stroke-transition sequences; given the limited number of games and players analysed, these patterns are descriptive and warrant confirmation in larger, balanced samples. BadmintonVision offers a reproducible approach for quantitative tactical analysis supporting coaching decisions and performance research in elite badminton.