An Assessment and Prediction Model for Momentum in Tennis Based on EWM-TOPSIS and Random Forest Method
Bibliographic record
Abstract
In the realm of sports, the concept of "momentum" encapsulates the mechanism wherein athletes or teams, spurred by favorable factors within a competitive encounter, exhibit enhanced performance, thereby fostering a virtuous cycle of "success begetting success." The current research endeavors to dissect and analyze the momentum exhibited by tennis players, particularly utilizing empirical data stemming from the 2023 Wimbledon Men's Singles Final. The study's primary objective is to quantify this momentum and delve into its potential impact on player performance. This study analyzes momentum in tennis by developing the Player Performance Evaluation Model, based on Entropy Weight Method and TOPSIS evaluation algorithm. The study incorporates factors like winning status, match lead, movement distance, winning shots, and double faults, differentially weighing the winning incentives for servers and receivers and uses an exponential decay accumulation of evaluation indicators, akin to the Momentum algorithm in deep learning. Through binomial testing, the study builds a significant correlation between momentum score and win rate fluctuations and focuses on quantifying momentum and determining its influence on player performance. The Momentum Advantage Prediction Model based on Random Forest instead of LSTM model, predicts the next play's momentum advantage from previous moment data. The model attained accuracy 84.7%.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".