Bibliographic record
Abstract
[[abstract]]2006年杜哈亞運前八強隊伍依照名次為韓國、中國、沙烏地阿拉伯、卡達、日本、伊朗、哈薩克及巴林，前八強十二場次比賽中快攻攻擊敘述統計中得知快攻攻擊總數為548次，A式快攻出現350次，B式快攻出現162次，C式快攻僅出現36次，可見各隊均把快攻攻勢集中在A、B式快攻，C式快攻較少，而中國與哈薩克在八強比賽中未曾出現C式快攻。本研究結果是採用2006年杜哈亞運男子排球賽，主辦單位大會技術小組資訊系統(volleyball information system; VIS)所公佈的正式比賽記錄資料，與DV錄影分析統計結果交叉比對所得之結果。並使用Microsoft Excel 2003統計快攻總攻擊共548次數據，進行資料統整計算與描述性統計分析。在八強每場次比賽中發現勝隊快攻成功率明顯高於敗隊，而勝隊快攻成功率最低有五成，最高成功率將近高達約九成左右，且在八強十二場次比賽中有九場球賽均是勝隊快攻攻擊成功率高於負隊快攻攻擊成功率，其餘三場雖負隊快攻成功率高於勝隊，但都因攔網、發球與關鍵時刻第一波攻擊及防守反攻不如對手而落敗，可見快攻的攻擊成功率在比賽中佔有相當重要地位，也是比賽中不可輕忽的一環。 The quarter finalists of 2006 Doha Asian Games was Korea, China, Saudi Arabia, Qatar, Japan, Iran, Kazakhstan and Bahrain. On the Quarter Finalists, the teams which won has higher quick attacking effectiveness then lost teams. Even the lowest team has 50% quick attacking effectiveness, and the highest team has 90%. In the twelve quarter finalists, only three teams which lost have lower quick attacking effectiveness then teams which won. Why they lost games because the attack effectiveness and defense was lower. It shows attacking player is essential for games and the coach should pay more attention on this. The data of this study was come from Asian Games volleyball information system. And used the video which recorded by DV. Excel 2003 were used to calculated and analysed. The sum of quick attacking hits was 548 times. A quick was used 350 times, B quick was used 162 times, but C quick only showed 36 times. China and Kazakhstan were never used C quick in the games. Compared the attacking hits, score and strike rate, a significant attacking effectiveness and quick attacking effectiveness in top 4 teams were higher then the others. The results was the same with others study. Although attacking technique was the key to win a game, but receive will effect the tactic operation. It is suggested that coaches should train the young players receive ability and fast spiking motion in order to improve the tactic operation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".