用隐马尔可夫模型分析足球防守跑位，实现无标签的防守角色识别与绩效评估。
A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football
- 基于球员追踪数据，用协变量依赖隐马尔可夫模型推断防守角色
- 提出基于角色的反事实分析方法，量化防守对对手进攻成功率的影响
- 适用于战术分析师和教练团队，提升对非持球防守的评估能力
足球中非持球防守表现的评估极具挑战性，传统指标无法捕捉限制对手行动选择与成功概率的复杂协同移动。尽管广泛使用的控球价值模型在持球动作评估上表现优异，但其在防守端的应用仍有限。现有反事实方法（如幽灵模型）虽可拓展分析范围，但常依赖缺乏战术上下文的“平均”行为模拟。为此，我们引入一种针对角球这一高度结构化场景的协变量依赖隐马尔可夫模型（CDHMM）。该无标签模型直接从球员追踪数据中推断时间分辨的盯人与区域分配。基于这些分配，我们提出一种新的防守贡献归因框架，并构建一种角色条件化的幽灵模型，用于非持球防守表现的反事实分析。结果表明，这些方法能提供相对于上下文感知基线的可解释性防守评价。
原文摘要 · Abstract (English)
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.
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