Talent scouting and standardizing fitness data in football club: systematic review
DOI:
https://doi.org/10.47197/retos.v60.107767Keywords:
Football, Data Science, Big Data, Digital Technology, Athletic Performance, Machine Learning, Reference StandardsAbstract
Talent scouting and fitness data standardization in professional football clubs have been the focus of research in recent years. The role of technology, big data, and data analytics is crucial in reshaping sports performance and bolstering the competitiveness of professional football teams. This review aims to consolidate current research on these technological advancements, examining how they can be effectively harnessed to revolutionize talent sourcing and elevate the competitive standards within football clubs. The review will focus on participants, including professional football players, and intervention interventions such as the implementation of digital technologies, data mining, and machine learning techniques. The study designs will include experimental, observational, and mixed-method studies conducted in football club settings. The analysis highlights the transformative potential of integrating quantitative player statistics with advanced data science and data-driven approaches in revolutionizing sports performance, enabling clubs to make informed recruitment decisions and enhance team performance. In addition, the review focuses on the impact of data analytics on transforming sports performance. The research has demonstrated that these models, specifically classification and regression models, can forecast a football player's performance score with up to 94% accuracy for forward positions.
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