An Exploration of Ball Game Momentum Fluctuations Based on Multiple Regression Analysis and Convolutional Neural Networks
Bibliographic record
Abstract
The aim of this study is to explore the patterns of player momentum fluctuations in tennis matches through a model combining multiple regression analysis and convolutional neural network (CNN). The study first analyzes real-time match data using analysis of variance (ANOVA) to identify the key factors that affect players' scores. Based on these factors, a multivariate regression analysis model is constructed for evaluating players' winning ability and their performance at specific moments. Then, the game data are deeply analyzed by convolutional neural networks to capture the fluctuating trends of player momentum. In addition, this paper verifies the non-randomness of momentum fluctuation by hypothesis testing method, which proves that the fluctuation of momentum in a match has a certain regularity and is closely related to the performance of players. The innovation of this study is that an analytical framework combining multiple regression and convolutional neural network is proposed, which not only improves the accuracy of momentum prediction, but also provides a new idea for dynamic analysis and optimal training of tennis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".