Detection of Mental Training Anxiety in Basketball Players in Same-Court Rivalry Centers Based on LightGBM Algorithm
Bibliographic record
Abstract
In this study, we first collected and preprocessed data from 500 basketball players between the ages of 13-35 years old in the same-court rivalry training in Northwest China, after which we utilized the Global Chaos Bat Algorithm (GCBA) for the mental training anxiety emotion feature extraction, and analyzed the correlation between each feature and the anxiety emotion through the Pearson coefficient.Finally, the LightGBM-based emotion prediction model was constructed, and the SHAP value was introduced to evaluate the feature importance of the model.The results show that the LightGBM model performs better and has higher prediction accuracy, which is as high as 96.68%; the interpretation results of the SHAP algorithm indicate that the gender and age of the basketball players are the main real-world factors for assessing their anxiety in same-court rivalry training.In addition, their game scores, opponents' strengths and injury histories during the same-court rivalry training were the main intrinsic factors for their anxiety.In conclusion, the psychological state of basketball players can reflect the severity of their training anxiety, and it further reveals the relationship between the psychological characteristics of basketball players and their training anxiety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".