SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines
Researchers have developed an enhanced system for the SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge. Their approach builds upon existing FOOTPASS baselines by incorporating gradient checkpointing for efficient fine-tuning, fusing graph neural network (GNN) outputs with visual features, and applying square-root frequency class weighting to balance imbalanced training data. The system achieved a Macro F1 score of 0.548 on the test set and 0.446 on the challenge set. AI
IMPACT This research advances AI capabilities in sports analytics by improving player action recognition in soccer.