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Rating Player Actions in Soccer.

Uwe Dick¹, Maryam Tavakol², Ulf Brefeld¹

¹Machine Learning Group, Leuphana University of Lüneburg, Lüneburg, Germany.

Frontiers in Sports and Active Living

|August 2, 2021

Summary

This summary is machine-generated.

This study introduces a data-driven model to evaluate soccer player actions based on their contribution to ball possession. The model uses a graph recurrent neural network (GRNN) to predict player movement and game outcomes, enabling new performance metrics.

Keywords:

graph networks soccer sports analytics trajectory data trajectory prediction

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Area of Science:

Sports Analytics
Machine Learning in Sports
Football Performance Analysis

Background:

Evaluating individual player contributions in soccer is complex.
Traditional metrics often fail to capture the nuances of player actions during ball possession.
Data-driven approaches are increasingly vital for objective performance assessment.

Purpose of the Study:

To develop a novel data-driven model for rating player actions in soccer based on their contribution to ball possession phases.
To create a system that predicts both player trajectories and the outcomes of possession phases.
To establish aggregated performance indicators for comparing player contributions to team success.

Main Methods:

Utilized real-world soccer tracking data to train a trajectory prediction model.
Developed a prediction model for ball possession phase outcomes.
Employed a graph recurrent neural network (GRNN) to model player-ball interactions.
Derived aggregated performance indicators from model outputs.

Main Results:

The GRNN model reliably predicts player trajectories.
The model accurately forecasts the outcomes of ball possession phases.
Empirical validation confirms the model's predictive capabilities.
Generated a set of performance indicators to assess player contributions.

Conclusions:

The proposed data-driven model offers a robust method for evaluating player performance in soccer.
The model's ability to predict trajectories and outcomes enhances understanding of player impact.
The derived performance indicators provide a more objective measure of a player's contribution to team success.