A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions

PathCRF: 通过球员轨迹的控球路径推断实现无球足球事件检测

PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories

提出PathCRF框架，仅利用球员轨迹数据，通过将轨迹建模为动态图并采用条件随机场（CRF）推断控球路径，实现无球足球事件检测，降低对人工标注和球轨迹数据的依赖。

通往目标之路：通过路径签名理解足球控球

The path to a goal: Understanding soccer possessions via path signatures

本文提出利用路径签名编码足球控球时空结构以预测下一步动作，无需手工特征，性能超越Transformer基准且计算成本更低，并基于预测结果构建了更可靠的控球评估指标，在英超赛季数据上得到验证。

用于足球运动员技能估计的控球期望值与对抗动作

Expected Possession Value of Control and Duel Actions for Soccer Player's Skills Estimation

本文扩展了控球期望值（EPV）模型，通过引入衰减效应、控球风险和对抗能力评估，更准确地估计足球运动员技能，并预测下赛季球员表现。

足球运球任务的强化学习

Reinforcement Learning for the Soccer Dribbling Task

我们提出了一个强化学习解决方案的\emph{soccer dribbling task}，在这种情况下，一个足球代理人必须从一个区域的开始到结束保持控球，作为对手试图获得控球。当对手使用固定策略时，运球者在每个决策点学习采取的最佳行动。在定义有意义的变量来表示状态空间和高层次的宏动作，将领域知识，我们描述了我们的应用程序的强化学习算法\emph{Sarsa}与CMAC函数逼近。我们的实验表明，训练期后，运球是能够完成其任务对一个强大的对手约58%的时间。

Is it worth the effort? Understanding and contextualizing physical metrics in soccer

We present a framework that gives a deep insight into the link between physical and technical-tactical aspects of soccer and it allows associating physical performance with value generation thanks to a top-down approach. First, we estimate physical indicators from tracking data. Then, we contextualize each player's run to understand better the purpose and circumstances in which it is done, adding a new dimension to the creation of team and player profiles. Finally, we assess the value-added by off-ball high-intensity runs by linking with a possession-value model. This novel approach allows ans…

超越期望目标：足球射门发生的概率框架

Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer

本文提出xG+框架，通过联合建模射门行为与质量，改进了标准xG模型的预测准确性并增强了球员技能信号的持续性。