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Related Experiment Video

Updated: Aug 14, 2025

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Machine learning application in soccer: a systematic review.

Markel Rico-González1, José Pino-Ortega2,3, Amaia Méndez4

  • 1Department of Didactics of Musical, Plastic and Corporal Expression, University of the Basque Country, UPV-EHU. Leioa, Spain.

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|January 13, 2023
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Summary

Machine learning (ML) models analyze soccer data to predict injuries, performance, and talent, aiding evidence-based decision-making. Further research is needed to determine optimal data inputs for accurate soccer predictions.

Keywords:
AlgorithmBig dataComputer sciencePredictionTeam sports

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Area of Science:

  • Sports Science
  • Data Science
  • Computational Intelligence

Background:

  • Soccer's inherent unpredictability presents challenges for evidence-based decision-making.
  • Statistical predictive models are increasingly vital in modern sports analytics.
  • Machine learning (ML) offers advanced capabilities for analyzing complex sports data.

Purpose of the Study:

  • To systematically review studies applying ML to soccer data.
  • To identify current ML applications and future potential in soccer analytics.
  • To highlight ML's role in mitigating soccer's chaotic nature through data-driven insights.

Main Methods:

  • Systematic literature review adhering to PRISMA guidelines.
  • Searches conducted across PubMed, SPORTDiscus, and FECYT databases.
  • Analysis of 32 selected studies focusing on outcome measures.

Main Results:

  • Studies were categorized into three main areas: injury prediction (7 studies), performance forecasting (21 studies), and talent forecasting (5 studies).
  • Performance forecasting included match/league outcomes, physical/physiological metrics, and technical/tactical aspects.
  • ML is emerging as a key strategy for decision-making support in soccer.

Conclusions:

  • ML models leverage large datasets to enhance predictive accuracy in soccer.
  • Further investigation is required to establish the optimal data volume for effective ML predictions.
  • ML can help reduce the inherent unpredictability in soccer through data analysis and forecasting.