The Influence of Growth and Maturation on Technical Skill Development in Youth Soccer
Bibliographic record
Abstract
Background: Youth soccer teams typically use chronological age-banded tryouts to select final rosters; therefore, youth players are more likely to be selected for the short-term advantages of early maturation rather than skill. Because of this, later maturing individuals may be overlooked or deselected, even if they have the greatest potential for the sport. Although it has been shown that growth and maturational influence soccer performance, less is known about its effects on soccer skill development. Skill is the learned ability to bring maximum certainty about pre-determined results. Traditionally, skill has been assessed subjectively; however, objective measures, such as the Loughborough Soccer Passing Test (LSPT), have recently been developed. The LSPT can distinguish various components of skill performance between players of different abilities. This thesis investigated how growth and maturation influence youth soccer skills development as assessed by the LSPT. A secondary aim was to compare skill development between the sexes. The third aim was to investigate the role of fat mass on skill development. Methods: A convenience sample of youth soccer players aged 9 to 15 were recruited from ASTRA Soccer Academy, Saskatoon. Soccer descriptives were collected by questionnaire. Anthropometric measures included height, sitting height, weight, and fat composition (Tanita DC-13C). Chronological age (CA) was determined from birth and measurement date. Biological age (BA) was estimated using an anthropometric equation to predict years from attainment of peak height velocity (PHV). The LSPT assessed soccer-specific technical skills. Skills were assessed in a designated area, and time to complete with penalties was recorded. The lower the time, the higher the technical skills rating. Descriptive statistics, correlations and ANCOVA were used to analyze the data. Results: 42 players were recruited (12 males and 30 females). The CA age range was 9.6 to 15.2 years and BA was -2.9 to 2.9 years from PHV. Males had significantly lower times than females (p <0.05), and scores decreased with increasing BA (p < 0.05). No relationship was found between BMI (r = 0.22) and body fat percentage (r =0.02) with LSPT score. Conclusion: The results suggest skill acquisition is associated with children’s growth and maturation. This suggests that coaches should consider a player’s growth and maturational status when objectively identifying a soccer player’s skill.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".