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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Sep 7·5 min read
Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection
TL;DR
A Hierarchical Possession-aware Graph Pointer Network (HPGPN) was developed to enhance pass receiver selection in football analytics, significantly improving prediction accuracy. The model effectively integrates player interactions, event context, and possession dynamics to refine candidate selection.
✦ Why It Matters
Implement HPGPN in your football analytics tools to improve pass prediction accuracy today.
Key Takeaways
- Explore HPGPN for enhanced football analytics applications.
- Utilize graph models to represent player interactions effectively.
- Incorporate dynamic possession history for better context understanding.
- Conduct ablation studies to validate model components in your projects.
How It Works
HPGPN formulates pass receiver selection as a variable-size candidate prediction task. It constructs a graph representing the current game state, integrating fixed event context and dynamic possession history.
This allows the model to capture how the attacking sequence evolves, refining candidate representations hierarchically by considering spatial, contextual, and historical evidence. A glimpse pointer head is then used to score the candidates based on this comprehensive analysis.