T-GAN segments football matches into intention-driven in-possession phases using player interaction graphs and transformers on 25 Hz tracking data from seven Bundesliga matches, achieving frame-level F1 of 0.87 for intentions.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
An MPNN models football players as graph nodes and passes as weighted edges to predict receivers from tracking and event data, claiming competitive accuracy plus metrics for likelihood, threat, and creativity.
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Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning
T-GAN segments football matches into intention-driven in-possession phases using player interaction graphs and transformers on 25 Hz tracking data from seven Bundesliga matches, achieving frame-level F1 of 0.87 for intentions.
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Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection
An MPNN models football players as graph nodes and passes as weighted edges to predict receivers from tracking and event data, claiming competitive accuracy plus metrics for likelihood, threat, and creativity.