Analysis of the impact of unforced errors in tennis
Bibliographic record
Abstract
This paper investigates unforced errors in tennis which is facilitated by a rich dataset. Descriptive analyses are carried out which studies the distribution of unforced error rates across professional tennis players, the identification of players with high and low rates, the relationship between rates and match winning percentage, the relationship between rates and aggressiveness (via hitting winners), the relationship between rates and touch number, and the study of rates versus time. Methods are then developed to assess the impact of unforced errors which are applicable to any racquet sport. We demonstrate the approach in the context of professional tennis with an investigation of the longstanding rivalry between Roger Federer and Rafael Nadal. The value of the approach is that we can provide estimates of the points lost, games lost, sets lost and matches lost due to unforced errors. The methods are based on a bootstrapping procedure which also yields standard errors for the estimates. The approach is valuable in terms of player evaluation, and can also be used for training purposes where it is possible to assess the quantification of improvement based on fewer unforced errors.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".