Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection

2026-09-04

AI Summary

本文针对足球分析中传球目标选择问题，提出了一种基于层次化持球感知图指针网络的方法，在部分可见球员的情况下提高预测准确性。

Abstract

Pass receiver selection is a fundamental task in football analytics, aiming to predict the intended receiver under a given game state. This task is challenging with event-centered freeze-frame observations, a broadcast-like setting that provides only partial and variable player visibility without complete trajectories or stable player identities. The model must therefore reason over anonymous visible candidates, opponent pressure, and recent context under partial observation. To address this setting, we propose a Hierarchical Possession-aware Graph Pointer Network (HPGPN), which formulates pass receiver selection as variable-size candidate prediction over visible teammates. HPGPN jointly models current player interactions, local event context, and possession-level temporal dynamics. It represents the current pass situation with a graph, incorporates fixed event context, and uses dynamic possession history to capture how the attacking sequence evolves. Candidate representations are refined hierarchically by integrating spatial, contextual, and historical evidence, and a glimpse pointer head scores the receiver candidates. Experiments on public football event and freeze-frame data show that HPGPN improves pass receiver selection performance. Ablation studies demonstrate the effectiveness of graph-based interaction modeling, fixed event context, and dual-branch dynamic possession-history modeling.

Problem

• Pass Receiver Selection
• Event-centered Freeze-frame Observations
• Partial Observation

Innovation

• Hierarchical Possession-aware Graph Pointer Network
• pass receiver selection
• event-centered freeze-frame observations
• dynamic possession history
• graph-based interaction modeling