Bridging the Relationship between Anthropometrics, Physical Performance, and Specific Soccer Skills in Young Male Soccer Players
Bibliographic record
Abstract
Objective. The aim of this study was to investigate the association between anthropometrics, physical performance, and specific soccer skills in young male soccer players. Method. 132 male soccer players aged 13-15 years old were recruited and categorized according to three distinct playing positions: defenders, midfielders, and forwards. Anthropometric profiles, including height, weight, body mass index, body fat, muscle mass, and 2D:4D finger length ratio were evaluated. Furthermore, acceleration ability (10m, 20m, and 30m velocity), countermovement jump (CMJ) test, drop jump (DJ) test; sit and reach test (SRT), and Y-balance test (YBT) were assessed. Soccer-specific performance was measured by the lofted passing accuracy over 35 meters protocol and the modified Illinois change-of-direction test with ball dribbling speed. Results. There was a significant strong positive correlation between the right and left digits’ ratios (r = .644, p < 0.001). However, the 2D:4D ratio of both hands demonstrated no significant differences between playing positions. Notably, body weight and muscle mass showed large positive correlations with long passing accuracy (r = .378, r = .418, respectively), while there was a moderate inverse relationship between dribbling time and both CMJ with arm swing (r = -.396) and drop jump height (r = -.305). Additionally, the YBT on both legs was negatively associated with dribbling time. Conclusion. Our results provide strong evidence that higher muscle mass and weight are associated with greater long passing accuracy, while better performance in countermovement and drop jumps serve as a key predictor of faster dribbling times. This information is useful for talent identification and performance optimization in young male soccer players.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".