Key result
Artificial intelligence may support football talent identification despite limitations in data quality and comparability.
Why the study?
Current evidence on the use of artificial intelligence in football talent identification needed to be synthesized.
Systematic Review (n=24)
AI-based approaches may support multidimensional football talent identification, though current evidence is limited by data quality, lack of longitudinal validation, and ethical concerns.
May assist football talent scouts now; extends AI applications yet leaves open needs for validation and standardization.
Objectives To synthesize current evidence on the use of artificial intelligence (AI) in football talent identification. Design Systematic review. Method This review followed PRISMA 2020 guidelines and was registered in PROSPERO. PubMed, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library were searched for studies published between 2016 and 2025. Eligible studies applied AI-related methods to football talent identification or player evaluation. After duplicate removal and screening, 24 studies were included in the qualitative synthesis. Results Twenty-four studies were included. Supervised machine learning was used in 19 studies (79%), deep learning in 14 (58%), ensemble learning in 11 (46%), and other specialized approaches in 5 (21%). Technical indicators were most frequently assessed, followed by physiological, psychological, and tactical variables. Although several models showed promising performance, comparability was limited by differences in samples, features, outcomes, and validation methods. Conclusions AI-based approaches may support multidimensional football talent identification, but current evidence is limited by uneven data quality, limited longitudinal validation, interpretability concerns, and ethical issues related to privacy and bias.
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Wang et al. (2026) conducted a systematic review in Football talent identification (n=24). Artificial intelligence was evaluated on Use of artificial intelligence methods and variables assessed. Artificial intelligence approaches, most commonly supervised machine learning (79% of 24 studies), may support football talent identification despite limitations in data quality and comparability.
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