CFSR: A tactics-formation coupling recognition framework based on efficient spatio-temporal modelling

Abstract Volleyball tactical analysis depends on match statistics, yet how specific formations shape offensive execution remains underexplored. In this paper, we introduce CFSR, motivated by the observation that coaching practice and computational models are often integrated rather than implemented as separate pipelines. We further observe that the setter acts as the spatial and temporal fulcrum of all offensive sequences. Accordingly, we replace a fully connected six-player interaction graph with a setter-anchored star topology, reducing per-frame relational edges by 3 and focusing representational capacity where tactical decisions originate. We formulate hit-event detection as a kinematic deflection problem, which lowers the need for dense frame-level supervision. A hierarchical expert-rule layer then maps detected events to fine-grained tactical categories by encoding FIVB regulations as hard structural constraints. This approach yields robust classification even when labeled data are scarce. On a purpose-built dataset of 402 high-quality offensive rallies from the 2023-2024 Chinese Men’s Volleyball Super League Finals, CFSR achieves a mean tactical F1 score of 0.846 across eight fine-grained categories and a formation F1 score of 0.953 for two professional formation classes, running at 15.6 FPS on commodity hardware. Notably, the framework attains F1 = 0.79 using only 20% of the labeled training data—a performance that sequence-learning baselines require the full dataset to match—highlighting the practical value of physics-informed inductive bias in annotation-scarce sports domains. Cross-league evaluation on a collegiate dataset (135 rallies) further shows that the spatial priors transfer seamlessly (formation F1 = 0.850), while temporal parameters require only minimal statistical recalibration, confirming the framework’s adaptability beyond professional play.