Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection

The paper introduces the Hierarchical Possession-aware Graph Pointer Network (HPGPN) for selecting pass receivers in football analytics. HPGPN treats the task as a variable-size candidate prediction problem, modeling current player interactions, local event context, and possession-level temporal dynamics using a graph representation. Experiments on public football event and freeze-frame datasets show that HPGPN improves pass receiver selection performance, with ablation studies confirming the value of graph-based interaction modeling, fixed event context, and dual-branch dynamic possession-history modeling.

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