Sequencing Effects of Balance and Plyometric Training on Physical Performance in Youth Soccer Athletes
Bibliographic record
Abstract
Hammami, R, Granacher, U, Makhlouf, I, Behm, DG, and Chaouachi, A. Sequencing effects of balance and plyometric training on physical performance in youth soccer athletes. J Strength Cond Res 30(12): 3278-3289, 2016-Balance training may have a preconditioning effect on subsequent power training with youth. There are no studies examining whether the sequencing of balance and plyometric training has additional training benefits. The objective was to examine the effect of sequencing balance and plyometric training on the performance of 12- to 13-year-old athletes. Twenty-four young elite soccer players trained twice per week for 8 weeks either with an initial 4 weeks of balance training followed by 4 weeks of plyometric training (BPT) or 4 weeks of plyometric training proceeded by 4 weeks of balance training (PBT). Testing was conducted pre- and posttraining and included medicine ball throw; horizontal and vertical jumps; reactive strength; leg stiffness; agility; 10-, 20-, and 30-m sprints; Standing Stork balance test; and Y-Balance test. Results indicated that BPT provided significantly greater improvements with reactive strength index, absolute and relative leg stiffness, triple hop test, and a trend for the Y-Balance test (p = 0.054) compared with PBT. Although all other measures had similar changes for both groups, the average relative improvement for the BPT was 22.4% (d = 1.5) vs. 15.0% (d = 1.1) for the PBT. BPT effect sizes were greater with 8 of 13 measures. In conclusion, although either sequence of BPT or PBT improved jumping, hopping, sprint acceleration, and Standing Stork and Y-Balance, BPT initiated greater training improvements in reactive strength index, absolute and relative leg stiffness, triple hop test, and the Y-Balance test. BPT may provide either similar or superior performance enhancements compared with PBT.
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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 | 0.000 | 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".