Does the Amount of Jumping with Respect to Positions During Volleyball Matches Affect the Team Success at the End of the Season?
Bibliographic record
Abstract
In many sports like volleyball, jumping, balance and explosive strength which are biomotor abilities have become more important day by day to succeed. Athletes focus on games at game period so the time that they spend for improving their biomotor abilities can be less. Therefore, performance that obtained in preparation period and sustaining that performance in whole season affect significantly team rank at the end of the season. In the lights of these informations, the affect of amount of jumps with respect to different positions was investigated in major league of Turkey in 2013-2014. There were 149 female volleyball players between the ages 17-27 (age 24.19 ± 2.42) in this study. 12 teams competed with each other and 125 games that is 455 sets were evaluated. Moreover; videos were analyzed three times by experts. Number of jumps in serve, block, spike and setting were recorded. Spearman Brown Order Differential Correlation Coefficient was used for determining the relations between dependent and independent variables and descriptive statistics in analyzing datum. In addition to this, Multiple Linear Regression Analysis was used for investigating regression level between independent and dependent variables. As a result of this, middle blockers jumped the most (M=155.86, SD=39.77) while opposites jumped the least (M=73.31, SD=23.49). The amount of jumps of wing spikers (r= -.130, p<.05) and middle blockers (r= -.185, p<.01) was negatively related with team success at the end of the season in low level. The total amount of jumps in different positions was negatively related with team success at the end of the season in low level too (r= -.156, p<.01). Setters, wing spikers, middle blockers and opposites were listed with respect to regression factor (ß) and their importance level for team success at the end of the league. In conclusion, number of jumps in different positions at game time affects team success positively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".