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Uncovering data-driven Football: Topic modeling from a data analytics perspective.

Marco Klaiber1, Manfred Rössle1

  • 1Aalen University of Applied Sciences Aalen, Germany.

Journal of Sports Sciences
|March 27, 2026
PubMed
Summary

Football analytics is rapidly evolving with data science. This study analyzes 152 articles using Latent Dirichlet Allocation (LDA) to identify key trends and define eight future research areas in sports analytics.

Keywords:
FootballLDAPRISMAdata analyticssoccer

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Area of Science:

  • Sports Science
  • Data Science
  • Football Analytics

Background:

  • Football is increasingly adopting data-driven strategies.
  • Emerging Data Science concepts are transforming the sport.
  • The growing volume of data fuels new research avenues.

Purpose of the Study:

  • To provide a comprehensive overview of developments in Football Analytics.
  • To conduct a comparative analysis of current approaches, trends, and challenges.
  • To identify future research directions in the field.

Main Methods:

  • Latent Dirichlet Allocation (LDA) analysis of 152 research articles.
  • Development of a taxonomy to categorize the research field into seven topics.
  • Analysis of generic terms and literature review to identify trends and challenges.

Main Results:

  • The research field was subdivided into seven distinct topics based on LDA.
  • Current trends and open research directions were identified.
  • Eight key open research fields were defined for future investigation.

Conclusions:

  • Football analytics is a dynamic field driven by data science advancements.
  • Systematic investigation of the eight defined open research fields is recommended.
  • This study provides a structured overview and roadmap for future research in football analytics.