The Effect of an In-Season 8-Week Plyometric Training Programme Followed By a Detraining Period on Explosive Skills in Competitive Junior Soccer Players
Bibliographic record
Abstract
The aim of this study was to examine the effect of an eight-week plyometric training and resistance programme, on different motor abilities, kicking speeds, and individual techniques in competitive junior soccer players.A training programme was applied for eight weeks and was composed of countermovement jump, countermovement onto a box, sprints, and sprints with change of direction.The players were evaluated in three moments: before the start of the training programme, after the application of the training programme, and after four weeks of detraining.The applied tests were: countermovement jump, speed dribbling, 30-meter sprint, kicking speed, and a Yo-Yo recovery intermittent recovery test Level 2 (Yo-Yo IE2).The results showed statistically significant differences (p<0.05) in sprint performance, countermovement jump, Yo-yo IE 2, and speed dribbling.The maximum velocity of the ball did not present statistically significant differences (p > 0.05).In addition, these results indicate that general performance may not be sensitive to four weeks of detraining in the trained soccer players.These results demonstrate that the combination of soccer drills and specific power training with no additional training time in-season optimizes some general and soccer-specific performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".