新型HPGPN模型提升足球分析中的传球接球者选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了一种分层感知拥有图指针网络（HPGPN），以改进足球分析中的传球接球者选择。该模型通过联合建模球员互动、事件背景和拥有权的时间动态，解决了球员可见性部分和候选人匿名性的挑战。在公开足球数据上的实验表明，HPGPN在预测目标接球者方面提高了性能，消融研究证实了其基于图的互动建模和双分支拥有历史方法的有效性。 AI

影响这项研究为体育分析引入了一种新颖的图神经网络架构，有可能改进球员表现分析和策略制定。

排序理由该项目是一篇在arXiv上发表的学术论文，详细介绍了一个新模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jingyi Wang, Da Li, Kaixin Wang, Zhangqin Huang · 2026-09-07 04:00

用于传球接球者选择的分层占有感知图指针网络

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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报道来源 [1]

用于传球接球者选择的分层占有感知图指针网络

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