Using data mining techniques to optimise athlete training and recovery programmes in tertiary physical education
Bibliographic record
Abstract
Aiming at the demand for scientific training of athletes in college sports education, this paper integrates data mining technology to propose athlete training and optimisation methods, and constructs an athlete training quality monitoring system and intelligent recovery assessment system.The traditional Apriori algorithm is improved by using multidimensional association rules, and multidimensional attribute mining is carried out on the collected data of athletes' training data to search for frequent item sets and output strong association rules, so as to achieve the monitoring of training quality and adjustment of training programmes.Using the improved fuzzy decision-making method to filter out the optimal feature subset, and integrating the improved whale algorithm and random forest to achieve intelligent recovery effect evaluation.By carrying out the practice of training and recovery optimisation, it can be seen that the total score of physical fitness test of track and field athletes increased from 18.19 to 19.8 before the experiment, and the training quality was significantly improved.Various health indicators such as heart rate, blood lactate, serum creatine kinase, etc. gained significant improvement in adopting the recovery optimisation method of athletes in this paper.The mean values of training status, coaching factors, and personal situation satisfaction evaluation dimensions were 4. 35, 4.425, and 4.38, respectively, and the training and recovery plan of this experiment was well received by the subject athletes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.000 | 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 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".