Effects of Maturation on Physical Fitness Adaptations to Plyometric Drop Jump Training in Male Youth Soccer Players
Bibliographic record
Abstract
Vera-Assaoka, T, Ramirez-Campillo, R, Alvarez, C, Garcia-Pinillos, F, Moran, J, Gentil, P, and Behm, D. Effects of maturation on physical fitness adaptations to plyometric drop jump training in male youth soccer players. J Strength Cond Res 34(10): 2760-2768, 2020-The objective of this study was to compare the effects of maturation on physical fitness adaptations to a twice weekly, 7-week plyometric drop jump training program. Seventy-six young male soccer players (aged 10-16 years) participated in this randomized controlled trial. Before and after the intervention, a physical fitness test battery was applied (countermovement jump; drop jump from 20 to 40 cm; 5 multiple bounds test; 20-m sprint time; change of direction speed; 2.4-km running time-trial; 5 repetition maximum [RM] squat; and maximal kicking distance). Participants were randomly divided into an active soccer-control group (CG) with Tanner stage maturation of 1-3 (CG-early; n = 16) or Tanner stage 4-5 (CG-late; n = 22), and to plyometric drop jump training groups with Tanner stage 1-3 (plyometric jump training [PJT]-early; n = 16) or 4-5 (PJT-late; n = 22). The analysis of variance and effect size (ES) measures revealed that when compared with their age-matched controls, the PJT-early (ES = 0.39-1.58) and PJT-late (ES = 0.21-0.65) groups showed greater improvements (p < 0.05) in sprint time, 2.4-km running time-trial, change of direction speed, 5RM squat, jumping, and kicking distance. The PJT-early exceeded the PJT-late group with greater (p < 0.05) improvements in drop jump from 20 cm (ES = 1.58 vs. 0.51) and 40 cm (ES = 0.71 vs. 0.4) and kicking distance (ES = 0.95 vs. 0.65). Therefore, a 7-week plyometric drop jump training program was effective in improving physical fitness traits in both younger and older male youth soccer players, with greater jumping and kicking adaptations in the less-mature athletes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".