Examining the Attacks After the 20th Scores in Volleyball According to Nationality and Positions
Bibliographic record
Abstract
The most efficient technique of scoring in volleyball is possible to be mentioned as attack. The numbers of efficient attacks performed after the 20th scores during the match are more important, and therefore this period should be managed more elaborately in terms of the technique and tactic. Therefore, it is considered that players’ presenting more productive performances with the drills related to the match after the trainings will be more efficient upon winning the match. The purpose of this study was to investigate the attacks performed after the 20th scores in 2017-2018 Volleyball Sultans League session according to the nationalities and positions. The study included totally 124 female players with natives (n=86) and foreigners (n=38) from 12 teams between 26 and 30 years old (28.25±1,62).  The numbers of the attacks performed after the 20th scores of 17.918 rallies in 496 sets (except from the 5th set) of 125 matches played by 12 teams during the session were evaluated. All matches were recorded with a video recorder by the researcher. The videos were analyzed by the experts in 3 repetitions. Attack errors, blocked attack, bad attacks, good attacks, numbers of attacks, coverage and totals numbers of attacks performed after the 20th scores during the match were recorded in numbers. Lilliefors Kolmogorov-Smirnov Test indicated whether the study data were appropriate for the normal distribution or not (p<.05). In data analysis, descriptive statistics such as number, percentage, arithmetic average and standard deviation were used; and Independent Samples T-Test and One-Way Variance Analysis (One-Way ANOVA) were used for determining the difference between dependent and independent variables. Tukey HSD Post-Hoc test was performed for finding among which groups there were differences between the units in one-way variance analysis (ANOVA). While interpreting the data, 0.01 and 0.05 were accepted as the level of significance. The research data were analyzed using SPSS 21 statistical software. As result of the findings obtained in the study, it was determined that there were significant differences in whole attacks performed by the players in terms of nationalities and positions (p<0.05). In conclusion, because the 20th scores are referred the critic period called as the golden scores, foreigners and opposite spiker’s that had high level conditional abilities for winning had more attacks.
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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 | 0.000 |
| 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".