Neurophysiological and Biomechanical Determinants of Successful Basketball Throws
Bibliographic record
Abstract
This study investigates the neurophysiological and biomechanical factors contributing to successful basketball throw performance in novice athletes, utilizing electroencephalography (EEG) and motion capture (MoCap) to analyze joint angles, ground reaction forces (GRFs), and brain activity. Sixteen participants performed basketball throws while EEG and MoCap systems recorded data on movement mechanics and neural activity. Biomechanical findings revealed that successful trials were characterized by refined movements, reduced wrist extension, increased elbow flexion, and more stable foot positioning compared to unsuccessful trials (all p > 0.05), contributing to greater shot accuracy. Reduced movement variability in successful trials further indicated improved motor consistency, reflective of skill development. EEG results showed higher beta and gamma power in the temporal lobe during successful compared to unsuccessful trials (p < 0.05), suggesting increased engagement in visuomotor integration and neural efficiency. Notably, our novice participants demonstrated limited neural efficiency in frontal regions (p > 0.05), potentially due to cognitive interference and self-monitoring. These findings highlight the importance of coordinated biomechanical execution and neural efficiency in optimizing basketball performance. The insights gained have practical implications for designing training interventions that improve motor performance, particularly for novice 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".