Unveiling the Biomechanical Insights: Motor Control Shifts Induced by Shoe Friction Adjustments and Their Impact on Defensive Slide, Crossover Dribbling, and Full Approach Jump in Basketball
Bibliographic record
Abstract
This study endeavors to explore the intricate interplay between the fundamental skills of basketball—defensive slide, crossover dribbling, and full approach jump—and the shoe outsole friction coefficient, with the overarching goal of advancing our comprehension regarding the pivotal role of footwear in athlete performance. Employing a comprehensive methodology that integrates 3D motion capture, force platform dynamometry, and biomechanical modeling, the study seeks to quantify the inherent motor control intricacies associated with these fundamental skills. Data collection involved 12 varsity players, and the research systematically assesses the influence of the shoe friction coefficient on both skill quality and injury risk, utilizing a set of 13 parameters for evaluation. The findings unveil that, with an increased friction coefficient, the following changes occur: for the defensive slide, we observed decreased contact time (p < 0.05), boosted medio–lateral impulse (p < 0.05), and lowered ankle torque (p < 0.01); for crossover dribbling, we observed increased anterior–posterior impulse (p < 0.05) and ankle torque (p < 0.05); for the full approach jump, we observed decreased contact time (p < 0.05) and increased jump height (p < 0.05). Generally, the equal increment in the shoe outsole friction coefficient did not result in equal changes in the selected parameters of motor skill control, indicating a non-linear relationship between the performance quality of essential basketball skills and the shoe friction coefficient. The results suggest the potential existence of an optimal value for skill execution. Notably, the study identifies that, while an augmentation in the friction coefficient enhances specific skill aspects, there is a discernible saturation point, signifying diminishing returns. This investigation makes a substantial contribution to our understanding of the precise impacts of shoe friction coefficients on basketball skills, thereby prompting considerations for the judicious selection of optimal friction coefficients and advocating for possible personalized footwear recommendations based on individual biomechanical profiles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".