The analysis of the last shots of the top-level tennis players in open tennis tournaments
Bibliographic record
Abstract
The aims of this study is to investigate the technical strokes and the last win and lost strokes, and contribute to prepare suitable technical training programs for the desired goal. In accordance with this purpose it has been monitored the semi and quarter final matches of the elite level tennis players Nadal, Federer, Agossi, Hewitt, Coria, Davydenko, Ljubcic and Ferrero in the U.S.A., Australia, Dubai, Doha and China 2005 Open Tennis Tournaments. Video records of tennis matches in Eurosport Channel are later transferred in computer. Images in the computer are converted into an observation form by a notation method. Obtained data are transferred into Excel Program as shoot types, win or lose last shoots. Percentage distributions of the data are presented in tables. In our study 528 serving 1939 shoot technics are analyzed. The average rally durations are 3.7. 'The forehand technique' has been the most widely used (30.78%) and the best one to get scores (37.8%) of all. And 'the backhand technique' has been the worst one (48.7%).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".