SynthesisFrontiers in sports and active living2026
Artificial intelligence in football talent identification: a systematic review.
Synthesis in Frontiers in sports and active living, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: The rapid development of artificial intelligence (AI) in sports has driven a shift in football talent identification from traditional experience-based judgment to data-driven decision-making. However, existing evidence on the application of AI in talent identification is dispersed, and a systematic synthesis of its application methods and effectiveness across different stages of the identification process is lacking. Objective: To systematically review the application of AI in football talent identification, compare sample characteristics, model types, data sources, and application contexts across different AI techniques, and summarize their advantages, limitations, and future directions. Methods: This systematic review followed PRISMA guidelines and was registered with PROSPERO. Searches were conducted in Web of Science, Scopus, SportDiscus, and PubMed to identify eligible studies, which were assessed for methodological quality. Information regarding sample characteristics, AI technique type, input features, model performance, and application context was extracted and synthesized. Results: A total of 20 studies were included, covering youth and professional male players. Traditional machine learning models were the most frequently used, whereas deep learning and hybrid models were less common. AI demonstrated potential in three main areas: developmental potential identification, performance and role identification, and player value and recruitment decision-making. Although most studies adopted multidimensional feature sets, limitations remain regarding data quality, model interpretability, methodological rigor, and external validity. Conclusion: AI has shown substantial value in football talent identification, but current research is still transitioning from methodological exploration to systematic application. Future work should focus on developing more representative datasets, promoting cross-institutional data sharing, enhancing model transparency and interpretability, and validating AI models in real-world football contexts to ensure their safe, reliable, and effective application in talent identification practice. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251196817, PROSPERO: CRD420251196817.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.