Jump performance during a season in elite volleyball players
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to measure and compare jump load and dynamic performance in elite volleyball athletes under varied conditions over an entire season of practices and games. Jump load and dynamic performance were compared among best jump height, mean jump height, as well as according to the number of jumps per game or practice session and the proportion of jumps higher than 50 cm relative to the total number of jumps in a practice or game. METHODS: Every jump performed by each of 12 players, in all practices and regular games (813 player-sessions in total), was measured by a particle accelerometer in accordance with a validated protocol (Vert, Fort Lauderdale, FL, USA). Data were collected and analyzed using STATA (SataCorp, College Station, TX, USA); the significance level for definition of confidence intervals was set to 95%, unless otherwise specified. Statistical analysis and comparison of means and proportions between groups was based on standard t-tests. RESULTS: Among player positions, the middle blocker consistently presented the greatest jump loads during the season; by comparison, the smallest jump loads were observed in the setter. CONCLUSIONS: Monitoring players' jump loads and performance using a simple accelerometer provides evidence which can be used to plan individual player activity, roster composition, the season calendar and furthermore increase knowledge to reduce over-training and recurrence of injuries.
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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 | 0.000 | 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 | 0.001 |
| 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".