Research on Tennis Players' Momentum Calculation Model Based on Entropy Weight Method
Bibliographic record
Abstract
Tennis, a globally revered sport, leverages the concept of momentum from physics, defined as "the force or strength gained through motion or a series of events." In the context of sports, momentum serves as a metric to delineate the performance trajectory of players over a given time frame, thereby reflecting the competitive edge of the athletes involved. This study presents the formulation of a computational model designed to estimate the momentum of tennis players during matches. The methodology commences with data preprocessing, encompassing the rectification of anomalous data points and the imputation of missing values. Subsequently, the paper elucidates the construction of a momentum estimation model, which encompasses the selection of pertinent indicators and a meticulous weight analysis. The authors have employed the entropy weight method to ascertain the relative importance of each indicator, subsequently devising a formula for momentum calculation grounded in these metrics. The paper culminates with a visual representation of the momentum dynamics, utilizing momentum change graphs and scatter plots to illustrate the fluctuations. The findings of this research offer valuable insights to tennis coaches and players, equipping them with a deeper comprehension of match dynamics and a strategic framework to enhance their competitive performance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".