Influence of basketball shoe mass, outsole traction, and forefoot bending stiffness on three athletic movements
Bibliographic record
Abstract
Prior research has shown that footwear can enhance athletic performance. However, public information is not available on what basketball shoe properties should be selected to maximise movement performance. Therefore, the purpose of the study was to investigate the influence of basketball shoe mass, outsole traction, and forefoot bending stiffness on sprinting, jumping, and cutting performance. Each of these three basketball shoe properties was systematically varied by ± 20% to produce three shoe conditions of varying mass, three conditions of varying traction, and three conditions of varying bending stiffness. Each shoe was tested by 20 recreational basketball players completing maximal effort sprints, vertical jumps, and a cutting drill. Outsole traction had the largest influence on performance, as the participants performed significantly worse in all tests when traction was decreased by 20% (p < 0.001), and performed significantly better in the cutting drill when traction was increased by 20% (p = 0.005). Forefoot bending stiffness had a moderate effect on sprint and cutting performance (p = 0.013 and p = 0.016 respectively) and shoe mass was found to have no effect on performance. Therefore, choosing a shoe with relatively high outsole traction and forefoot bending stiffness should be prioritised, and less concern should be focused on selecting the lightest shoe.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".