Due to the tremendous popularity of youth football, practitioners in this domain face the ongoing question of the most effective solutions in early talent selection. Although the scientific community has suggested multidimensional models for some time, coach assessments and motor performance tests remain common. Earlier research has determined the strengths and weaknesses within these different approaches. The current investigation directly compared the effectiveness of each approach in talent selection (coach assessment vs. motor performance tests vs. multidimensional data). A sample of 117 youth football players, their parents, and coaches participated in multidimensional measurements in the U14 age category (coach assessments, motor performance tests, psychological characteristics, familial support, training history, and biological maturation). The area under the curve (AUC [95% CI]) from receiver operating characteristic indicated the prognostic validity of each approach in predicting U19 player status five years after the assessments (professional vs. non-professional). Motor performance tests (0.71 [0.58; 0.84]) showed a lower AUC than the multidimensional data (0.85 [0.76; 0.94], p = 0.02), whilst coach assessments did not differ from the two others (.82 [.74; .90]). Further, combined talent selection approaches, especially the use of coach assessments and multidimensional data together, were significantly better at predicting U19 player status (0.93 [0.87; 0.98], p = 0.02 vs. multidimensional data only). Although certain limitations may impede further insights (summation of data, skipped use of non-linear statistics), scientific claims for using multidimensionality within talent selection were confirmed to be fruitful. In particular, the combination of the subjective coaches’ eye with scientific data may buffer the mutual weaknesses of these different approaches. Future research should focus on optimizing the output of promising multidimensional models. Knowledge of detailed values relating to specific dimensions within these models and the implementation of enhanced non-linear statistics may enable further improvements in the field of talent selection.