A Neighbor-based Approach to Pitch Ownership Models in Soccer

TL;DR

This paper introduces a flexible, KNN-based pitch ownership model for soccer that enhances tactical analysis by incorporating player positioning and allows easy tuning for different scenarios.

Contribution

It presents a novel, hyperparameter-efficient KNN approach to modeling pitch control, adaptable to various methods and uncertainty levels in soccer analysis.

Findings

The model accurately emulates multiple pitch control methods.

It offers fast inference suitable for real-time analysis.

The approach is easily tunable for different skill levels.

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.