提出首个足球传球价值评估基准，提升预测准确率与决策分析能力
Revisiting Expected Possession Value in Football: Introducing a Benchmark, U-Net Architecture, and Reward and Risk for Passes
- 采用U-Net结构的卷积神经网络改进传球价值模型
- 在基准测试中正确识别78%的高价值局面
- 新增奖励与风险双维度评估，助于优化传球策略
本文首次提出期望控球价值（EPV）基准OJN-Pass-EPV，并构建新改进的EPV模型。通过该基准，我们以成对比赛状态及其相对EPV值为依据，定量评估模型性能。尝试复现Fernández等（2021）结果时未能成功，遂基于U-Net型卷积神经网络设计新架构，在模型损失和期望校准误差上表现良好。最终提出的传球模型引入球体高度信息，采用双组件结构同时分析传球的潜在收益与风险。该模型在OJN-Pass-EPV基准中正确判断78%的比赛状态对中更高价值状态，验证了其对进球潜力评估的准确性。研究成果可助力评估模型质量、提升预测精度、辅助传球决策分析，进而改善球员与球队表现。
原文摘要 · Abstract (English)
This paper introduces the first Expected Possession Value (EPV) benchmark and a new and improved EPV model for football. Through the introduction of the OJN-Pass-EPV benchmark, we present a novel method to quantitatively assess the quality of EPV models by using pairs of game states with given relative EPVs. Next, we attempt to replicate the results of Fernández et al. (2021) using a dataset containing Dutch Eredivisie and World Cup matches. Following our failure to do so, we propose a new architecture based on U-net-type convolutional neural networks, achieving good results in model loss and Expected Calibration Error. Finally, we present an improved pass model that incorporates ball height and contains a new dual-component pass value model that analyzes reward and risk. The resulting EPV model correctly identifies the higher value state in 78% of the game state pairs in the OJN-Pass-EPV benchmark, demonstrating its ability to accurately assess goal-scoring potential. Our findings can help assess the quality of EPV models, improve EPV predictions, help assess potential reward and risk of passing decisions, and improve player and team performance.
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