PRELUDE: ANTICIPATING LANDING POINTS AT RACKET-SHUTTLE CONTACT VIA CONTEXT- ADAPTIVE MULTIMODAL GATING

We study pre-intervention anticipation under partial observability—predicting an outcome before the action completes. This setting requires synthesizing heterogeneous spatio-temporal signals and resolving inherent ambiguities in ground-truth labeling. We introduce PRELUDE, a conditional fusion framework for multimodal anticipation under causal partial observability. PRELUDE comprises three key elements: (i) a causal pre-boundary multimodal encoder, (ii) a dual gating mechanism that dynamically reconciles cross-modal and spatio-temporal dependencies, and (iii) a physics-informed label generation protocol that enforces causal consistency. On standard benchmarks, PRELUDE sets a new state-of-the-art, achieving 92.6% accuracy while delivering an 85% latency reduction (224 ms end-to-end). By providing strong evidence that pre-stroke multimodal cues alone are sufficient for precise anticipation, this work opens a new direction for proactive an-alytics in competitive sports. Code is available at: https://anonymous.4open.science/r/AI-Sport-28D6/README.md.