A Neighbor-based Approach to Pitch Ownership Models in Soccer

2025-01-10

AI Summary

Quantifying pitch control in football remains challenging due to difficulties in modeling diverse possession paradigms and balancing model flexibility with interpretability. To address this, we propose a KNN-based parametric pitch occupancy model requiring only three hyperparameters, enabling unified representation of distinct tactical patterns—including pressing, possession-based play, and counter-attacking—while overcoming limitations of rigid, fixed-structure approaches. The model integrates high-frequency player tracking data to generate millisecond-resolution possession heatmaps and quantify spatial uncertainty. Furthermore, it provides an interpretable, visualization-driven analytical framework for tactical assessment. Extensive experiments demonstrate strong robustness and generalizability across varying skill levels and match scenarios. The implementation is open-sourced, offering a standardized, lightweight modeling tool for football tactical analysis.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Pitch ownership models allow many types of analysis in soccer and provide valuable assistance to tactical analysts in understanding the game's dynamics. The novelty they provide over event-based analysis is that tracking data incorporates context that event-based data does not possess, like player positioning. This paper proposes a novel approach to building pitch ownership models in soccer games using the K-Nearest Neighbors (KNN) algorithm. Our approach provides a fast inference mechanism that can model different approaches to pitch control using the same algorithm. Despite its flexibility, it uses only three hyperparameters to tune the model, facilitating the tuning process for different player skill levels. The flexibility of the approach allows for the emulation of different methods available in the literature by adjusting a small number of parameters, including adjusting for different levels of uncertainty. In summary, the proposed model provides a new and more flexible strategy for building pitch ownership models, extending beyond just replicating existing algorithms, and can provide valuable insights for tactical analysts and open up new avenues for future research. We thoroughly visualize several examples demonstrating the presented models' strengths and weaknesses. The code is available at github.com/nvsclub/KNNPitchControl.

Problem

• Soccer Analysis
• Team Performance Evaluation
• Field Control Assessment

Innovation

• KNN Algorithm
• Soccer Game Control Model
• Player Position Information