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New HPGPN model enhances pass receiver selection in football analytics

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Hierarchical Possession-aware Graph Pointer Network (HPGPN) to improve pass receiver selection in football analytics. This model addresses the challenge of partial player visibility and anonymous candidates by jointly modeling player interactions, event context, and temporal dynamics of possession. Experiments on public football data demonstrate that HPGPN enhances performance in predicting intended receivers, with ablation studies confirming the effectiveness of its graph-based interaction modeling and dual-branch possession-history approach. AI

IMPACT This research introduces a novel graph neural network architecture for sports analytics, potentially improving player performance analysis and strategy development.

RANK_REASON The item is an academic paper published on arXiv detailing a new model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New HPGPN model enhances pass receiver selection in football analytics

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The item is an academic paper published on arXiv detailing a new model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jingyi Wang, Da Li, Kaixin Wang, Zhangqin Huang ·

    Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection

    arXiv:2609.04803v1 Announce Type: new 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 …

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