Evaluating off-ball defensive performance in football is challenging, as traditional metrics do not capture the nuanced coordinated movements that limit opponent action selection and success probabilities. Although widely used possession value models excel at appraising on-ball actions, their application to defense remains limited. Existing counterfactual methods, such as ghosting models, help extend these analyses but often rely on simulating "average" behavior that lacks tactical context. To address this, we introduce a covariate-dependent Hidden Markov Model (CDHMM) tailored to corner kicks, a highly structured aspect of football games. Our label-free model infers time-resolved man-marking and zonal assignments directly from player tracking data. We leverage these assignments to propose a novel framework for defensive credit attribution and a role-conditioned ghosting method for counterfactual analysis of off-ball defensive performance. We show how these contributions provide a interpretable evaluation of defensive contributions against context-aware baselines.

翻译：评估足球中的无球防守表现具有挑战性，因为传统指标无法捕捉那些限制对手行动选择和成功概率的微妙协调运动。尽管广泛使用的控球价值模型擅长评估有球行动，但其在防守方面的应用仍然有限。现有的反事实方法，如幻影模型，有助于扩展这些分析，但通常依赖于模拟缺乏战术背景的"平均"行为。为解决这一问题，我们引入了一种专为角球（足球比赛中高度结构化的环节）设计的协变量依赖隐马尔可夫模型。我们的无标签模型直接从球员追踪数据中推断出时间分辨的人盯人和区域防守任务分配。我们利用这些任务分配，提出了一种新颖的防守贡献归因框架，以及一种用于无球防守表现反事实分析的角色条件幻影方法。我们展示了这些贡献如何针对情境感知基线，提供可解释的防守贡献评估。