Statistical Analysis on Technical Index of Women's Basketball Teams in the 30th Olympic Games Based on RSR
Bibliographic record
Abstract
By using the method of Rank-sum ratio,this paper makes quantitative evaluation about technical Index of women's basketball teams in the 30th Olympic Games.The results show that RSR value of technical index of the United States,France and Australia is 1,0.83 and 0.84,respectively.They belong to Grade A;RSR value of Turkey and the Czech Republic is 0.71 and 0.75,respectively.They belong to Grade B;RSR value of China,Brazil,the United Kingdom is ranging from 0.40 to 0.59,they belong to Grade C;RSR value of Croatia is 0.29,it belongs to Grade D;RSR value of Canada and Angola is 0.17 and 0.08,respectively.They belong to Grade E;The Correlation coefficient between Olympics ranks and RSR value is 0.895(P0.01),it means they are both significantly associated;Compared with the top 8 teams of the Olympic Games,except two-point field goal percentage and assists,Chinese women's basketball team lags behind other teams in other 7 offensive index,it has significant difference(P0.05) in offensive rebound and it has significant difference(P0.01) in manufacturing foul.Chinese women's basketball team lags behind other teams in other 4 offensive index except foul,it has significant difference(P0.01) in blocked shots;Compared with the top 4 teams of the Olympic Games,except assists,Chinese women's basketball team lags behind other teams in other 8 offensive index,it has significant difference(P0.05) in offensive rebound and it has significant difference(P0.01) in manufacturing foul.Chinese women's basketball team lags behind other teams in other 4 offensive index except foul,it has significant difference(P0.01) in blocked shots.Finally,this paper put forwards some suggestions for Chinese women's basketball team.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".