Nationwide Subjective and Objective Assessments of Potential Talent Predictors in Elite Youth Soccer: An Investigation of Prognostic Validity in a Prospective Study

Abstract

Recent studies have provided empirical evidence on the prognostic relevance of objective performance diagnostics in the soccer talent identification and development process. However, little is known about the prognostic validity of coaches' subjective evaluations of performance. This study evaluated objective and subjective assessments within a nationwide talent development program and addressed motor, perceptual skill, and personality-related performance factors. Male players (N = 13,869; Mage = 12.59 ± 1.07 years) from the age groups U12 to U15 of the German soccer talent development program participated in this study. Participants completed an objective motor diagnostic (sprint, agility, dribbling, ball control, juggling) and were subjectively rated by their coaches (kicking skills, endurance, individual tactical skills, psychosocial skills). All nine predictors were assessed with sufficient psychometric properties (α ≥ 0.72; except dribbling and ball control: α ≥ 0.53). Players' success three seasons later was operationalized by achieving professional youth academy level or not (success rate, 9%). Independent-samples t-tests analyzed univariate mean group comparisons between future selected and non-selected players. Logistic regression models examined the multivariate prognostic validity of all assessments by predicting success with subjective (model 1), objective (model 2), and both groups of predictors (model 3). Confirming the univariate prognostic validity, future selected outperformed non-selected players regarding all predictors (each p < 0.001, except for agility in U15: p < 0.01). Tactical skills, kicking skills, and sprint were of highest predictive value (d ≥ 0.61 in each age group). Multivariate results provided empirical evidence for the subjective (7% ≤ Nagelkerke's R2 ≤ 11%; each p < 0.001) and objective (8% ≤ Nagelkerke's R2 ≤ 13%; each p < 0.001) assessments' prognostic validity. However, model 3 revealed the best statistical explanatory power in each age group (0.15 ≤ Nagelkerke's R2 ≤ 0.20; p < 0.001). In this combined assessment model, sprint, tactical skills, and dribbling were found to be the most predictive variables. In conclusion, this study reinforces the call for multidimensional diagnostics integrating objective and subjective assessments. Future research is needed to address the demands for longitudinal analyses of subjective ratings, the integration of biological maturation, and empirical evidence for female soccer.

Methodological Approaches to Talent Identification in Team Sports: A Narrative Review

“…The lack of multidimensional designs is perhaps due to the fact that, in reality, the identification of talented individuals is difficult to objectively explain [6]. This is accompanied with the associated methodological challenges of needing to measure variables from each discipline in their entirety [46], combine these into a tool for TID purposes, and implement this across large samples (e.g., nationwide TID processes [55,56]).…”

“…Given the complexity of sampling performance in its entirety, one method utilised in order to provide a samples-based assessment is the inclusion of a subjective expert opinion (SEO), where a coach or practitioner can provide a holistic rating of player performance (e.g., a score from 1 to 4, [84]. Research has shown that inclusion of subjective ratings from coaches improves predictive models within TID in comparison to objective data alone [55,74]. However, the basis of and validity of such ratings is yet to be established with research showing a lack of agreement between coaches [85], an inability for coaches to accurately rate performance within specific disciplines (e.g., physical, [86,87]) and suggestions that ratings are potentially biased [82] and could be based on a coaches' perceived ability to influence and develop a player rather than solely on athlete ability alone [88,89].…”

Predictive Value of Repeated Jump Testing on Nomination Status in Professional and under 19 Soccer Players

“…A similar trend, with lower magnitude, was presented for the BMI ( OR = 2.57 [1.21, 5.44]), highlighting that, as BMI increases by one unit, the odds of PRO change positively (increase) by a factor of 2.57. Both of these predictors show the significance of anthropometrics and the dependence on the biological maturation, even though there is great heterogeneity in body size among elite soccer teams [ 4 , 28 , 29 , 40 , 90 , 91 ]. This is partly due to the playing position, as goalkeepers, central defenders, and central attackers tend to be the tallest and the heaviest players of a soccer team [ 5 , 71 , 92 , 93 , 94 , 95 ].…”

“…A recent study, which examined technical, tactical, physical, and psychological parameters on Finnish soccer players and their ability at the age of 15 years to predict professional status at age of 19 years, presented a multidimensional model with 86% discriminative validity [ 110 ]. In general, achieving professional status in soccer is dependent on the likelihood of having higher cardiorespiratory endurance, faster sprinting times, greater sprinting abilities, greater vertical jumping capacities, higher one repetition back squat values, higher lean mass and lower body fat percentage, being taller and heavier as youth players, and lastly being born in the first semester of the year [ 10 , 17 , 19 , 39 , 40 , 43 , 51 , 60 , 90 , 91 , 110 ].…”

“…Therefore, since there is a complex non-linear environment present that influences players’ future development and success, statistical modeling through BLR and ROC have been deemed as appropriate [ 120 ]. BLR examined the probability of PRO status based on U19 selected anthropometric and physiological predictors [ 77 , 82 , 86 , 90 , 110 , 121 ], while ROC was used to show the tradeoff between sensitivity and specificity and to present the accuracy of the predicting model [ 83 , 99 , 122 , 123 ]. Such statistical modeling provides better evaluation of the variance in the performance, balancing the advantages and disadvantages of the selected predictors [ 39 , 40 , 78 , 99 , 110 , 111 , 124 , 125 ].…”