A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football

• URL: http://arxiv.org/abs/2601.00748v1
• Date: Fri, 02 Jan 2026 17:10:36 GMT
• Title: A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football
• Authors: Sean Groom, Shuo Wang, Francisco Belo, Axl Rice, Liam Anderson,
• Abstract summary: We introduce a co-dependent Hidden Markov Model (CDHMM) tailored to corner kicks in football games.<n>Our model infers time-resolved man-marking and zonal assignments directly from player tracking data.<n>We propose a novel framework for defensive credit attribution and a role-conditioned ghosting method for counterfactual analysis of off-ball defensive performance.
• Score: 3.418921713486739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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