Quantitative Talent Identification Reimagined: Sequential Testing Reduces Decision Uncertainty

Background/Objectives: Quantitative approaches to talent identification in youth soccer often rely on either closed-skill assessments or small-sided games, but each carries inherent uncertainties that can reduce selection accuracy. Effective talent selection requires integrating both sources of data while accounting for their limitations. This study aimed to develop and validate a framework that combines closed-skill tests with competitive 1v1 game outcomes to optimize early-stage player selection. Methods: We assessed the dribbling and sprinting performances of 30 Brazilian youth players and used 1308 individual 1v1 bouts (70–90 bouts/individual) to estimate competitive abilities using a Bayesian ordinal regression model. Based on our empirical results, we then ran simulations to determine how many players should be selected when the aim is to reduce a player pool of 100 individuals so that the ‘true’ top 10 performers are reliably included and to determine how the weighting between data from closed-skill tests and games should change with increasing match observations. Results: Dribbling speed was a strong predictor of 1v1 success (β = –0.76, 95% CI: [–1.16, –0.40]), while sprint speed (β = 0.01, 95% CI: [–0.36, 0.40]) showed no significant association with 1v1 success. Simulations revealed that 26.0 ± 2.5 players were needed after five 1v1 contests per player to capture the true top 10% and then decreased to 18.0 ± 1.5 players after 20 contests. Optimal weighting shifted from a greater reliance on dribbling-based data (α > 0.80 at Game 0) to more match-based data after 10–20 contests per player (α = 0.16 at Game 20), but utilizing both sources of data improved selection accuracies and efficiencies. Conclusions: This study provides an uncertainty-aware protocol for talent identification that optimizes the integration of data from closed-skill tests and in-game performances within a dynamic selection framework that enhances precision and forms the basis for efficient early-stage scouting of large cohorts of players.