Effects of volume-based overload plyometric training on maximal-intensity exercise adaptations in young basketball players
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to compare maximal-intensity exercise adaptations in young basketball players (who were strong individuals at baseline) participating in regular basketball training versus regular plus a volume-based plyometric training program in the pre-season period. METHODS: Young basketball players were recruited and assigned either to a plyometric with regular basketball training group (experimental group [EG]; N.=8), or a basketball training only group (control group [CG]; N.=8). The athletes in EG performed periodized (i.e., from 117 to 183 jumps per session) plyometric training for eight weeks. Before and after the intervention, players were assessed in vertical and broad jump, change of direction, maximal strength and a 60-meter sprint test. RESULTS: No significant improvements were found in the CG, while the EG improved vertical jump (effect size [ES] 2.8), broad jump (ES=2.4), agility T test (ES=2.2), Illinois agility test (ES=1.4), maximal strength (ES=1.8), and 60-m sprint (ES=1.6) (P<0.05) after intervention, and the improvements were greater compared to the CG (P<0.05). CONCLUSIONS: Plyometric training in addition to regular basketball practice can lead to meaningful improvements in maximal-intensity exercise adaptations among young basketball players during the pre-season.
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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 | 0.000 |
| 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".