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Learning to Rate Player Positioning in Soccer.

Uwe Dick¹, Ulf Brefeld¹

¹Institute of Information Systems, Leuphana University, Lüneburg, Germany.

|January 24, 2019

Summary

This summary is machine-generated.

This study uses deep reinforcement learning to rate soccer game situations based on player positions. The data-driven approach successfully predicts dangerous attacking opportunities without needing expert input.

Keywords:

deep learning reinforcement learning scoring function spatiotemporal data

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Area of Science:

Sports Science
Data Science
Artificial Intelligence

Background:

Evaluating soccer game situations is crucial for tactical analysis.
Traditional methods often rely on subjective expert evaluation.
Objective, data-driven approaches are needed for consistent performance assessment.

Purpose of the Study:

To develop a data-driven method for evaluating soccer game situations.
To assess the potential of player positioning for successful attacks.
To explore the use of deep reinforcement learning in sports analytics.

Main Methods:

Utilized deep reinforcement learning techniques.
Employed a purely data-driven approach.
Valuated multiplayer positionings using positional data.

Main Results:

Developed functions to rate game situations by their attacking potential.
Predicted scores showed a high correlation with the dangerousness of actual situations.
Demonstrated the feasibility of rating player positioning without expert knowledge.

Conclusions:

A data-driven deep reinforcement learning approach can effectively evaluate soccer game situations.
Player positioning can be objectively rated based on its potential to lead to attacks.
This methodology offers a scalable and objective alternative to expert-based assessments.