Selected Cognitive Abilities in Elite Youth Soccer Players
Bibliographic record
Abstract
The identification of talent in soccer is critical to various programs. Although many research findings have been presented, there have been only a few attempts to assess their validity. The aim of this study was to determine the relationship between talent and achievement variables in the Vienna Test System. The participants were 91 Czech soccer players, representing four youth soccer teams, who were born in the year 2000. These boys were divided into two groups according to their coaches' assessments using a TALENT questionnaire. A two-factor model (component 1: "kinetic finesse"; component 2: "mental strength") was designed to interpret the responses of the coaches on the questionnaire. The Vienna Test System was used to determine the level of players' cognitive abilities. In total, the subjects performed seven tests in the following order: Raven's Standard Progressive Matrices (SPM), a reaction test (RT), a determination test (DT), a visual pursuit test (LVT), a Corsi Block-Tapping Test (CORSI), a time/movement anticipation test (ZBA), and a peripheral perception test (PP). To analyze the relationship between talent and achievement variables within the Vienna Test System, correlation analyses were performed. The results revealed that the talented group attained significantly better results on only 1 of the 16 variables, which was ZBA2: movement anticipation - deviation of movement median (r = .217, p = .019). A comparison of the two talent components showed that component 1 ("kinetic finesse") was a more significant factor than component 2 ("mental strength"). Although we observed statistically significant correlations, their actual significance remains questionable; thus, further research is required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".