Analysis of shot, unforced error,number of shot and duration of thematch between Shi Yuqi and Lee ChongWei in All England 2017 / Muhamad Fitri Adnan
Bibliographic record
Abstract
This study was conducted in order to analysis of shot, unforced error, number Of shot and duration of the match between Shi Yuqi and Lee Chong Wei in All England 2017. Five match starts from quarter final to final match were selected to be observed. The indicator include for this analysis were the shot, unforced error, number of shot and duration of the match. Mann Whitney test used to see the significant of this study. For shot, first type of shot which is forehand Lee Chong Wei (Mean ± SD), (30.33±8.083) and Shi Yuqi forehand (17.67±11.7l9). Next, for backhand shot, based on the analysis for Lee Chong Wei (Mean± SD), (16.33±11.150) and Shi Yuqi (Mean ± SD), (14.33±6.658). For Lee Chong Wei drop is (Mean ± SD), (31.00±7.211) and Shi Yuqi drop (Mean ± SD), (52.67±34.962). Last for number of shot is jumping smash. Lee Chong Wei (Mean ± SD), (25.00±3.606) and Shi Yuqi (Mean ± SD), (24.33±.577). The second indicator is unforced error. Based on the results Lee Chong Wei out is (Mean ± SD), (6.33± 3.055) and Shi Yuqi out (9.67± 2.082). Next is Lee Chong Wei long (Mean ± SD), (.OO±.OOO) and Shi Yuqi long (Mean ± SD), (.OO±.OOO). For short and wrong court Lee Chong Wei and Shi Yuqi also same like long which is long (Mean ± SD), (.OO±.OOO). Lastly for unforced error is net. Lee Chong Wei is (Mean ± SD), (7.33±5.508) and Shi Yuqi (Mean ± SD), (8.67±4.04l). Next indicator is number of shot, Lee Chong Wei (Mean ± SD), (259.33±34.078) and Shi Yuqi (Mean ± SD), (2l5.67±75.745). Lastly indicator is duration of the match, Lee Chong Wei (Mean ± SD), (49.33±8.386) and Shi Yuqi (Mean ± SD), (43.33±7.024).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".