Talent scouting and standardizing fitness data in football club: systematic review
DOI:
https://doi.org/10.47197/retos.v61.107766Keywords:
Football, Data Science, Big Data, Digital Technology, Athletic Performance, Machine Learning, Reference StandardsAbstract
Talent scouting and fitness data standardization in professional football clubs have become central topics in recent research. This review aims to consolidate advancements in technology, big data, and data analytics, examining their roles in optimizing talent identification and fitness evaluation within football clubs. A systematic search strategy was applied across academic databases, including PubMed, IEEE Xplore, and Scopus, using keywords like "football talent scouting," "fitness data standardization," "data analytics in sports," and "machine learning in football performance." Studies selected for review involved professional football players and interventions using digital technologies and data-driven methods within club settings, covering experimental, observational, and mixed-method designs in football environments. This review highlights the impact of integrating quantitative player statistics with advanced analytics to enhance recruitment precision and team performance, showing that data models—such as classification and regression—can predict performance scores with up to 94% accuracy for forward positions, underscoring the transformative role of data analytics in professional football.
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