Plyometric Training Effects on Athletic Performance in Youth Soccer Athletes
Bibliographic record
Abstract
The purpose of this systematic review was to critically analyze the literature to determine the effectiveness of plyometric training on athletic performance in youth soccer athletes. A total of 7 studies were included in this review after meeting the following criteria: (a) used plyometric training programs to assess athletic performance, (b) subjects were soccer athletes aged preadolescent up to 17 years, and (c) were published from 2000 to January 2014. Study methods were assessed using the PEDro scale with scores ranging from 4 to 6. Results showed similarities and differences in methodologies and procedures among the included studies. Athletic performance consisting of kicking distance, speed, jumping ability, and agility significantly improved because of plyometric training interventions. The current evidence suggests that plyometric training should be completed 2 days per week for 8-10 weeks during soccer practice with a 72-hour rest period between plyometric training days. The initial number of foot contacts should be 50-60 per session and increase to no more than 80-120 foot contacts per session for this age group to prevent overuse injuries. A total of 3-4 plyometric training exercises should be performed 2-4 sets for 6-15 repetitions per training session. The evidence and the literature suggest that plyometric training for this age group should only be implemented using recommended safety guidelines such as those published by the Canadian Society for Exercise Physiology and the National Strength and Conditioning Association and under appropriate supervision by trained personnel.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".