Increasing Running Shoe Traction can Enhance Performance
Bibliographic record
Abstract
The outsole of a running shoe must provide enough traction for the athlete to avoid slipping during running. What is unknown is whether there is any point to designing running shoe outsoles with traction above this minimum required traction. The purpose of this study was to investigate whether performance could be enhanced by increasing the outsole traction of a running shoe. A commercially available running shoe (Control) was compared against the same shoe model with the outsole modified with a higher traction rubber (High Traction). The available traction of each shoe was measured with a traction testing system. Twenty male athletes completed a maximal effort timed running course in both shoes on two different surfaces. When wearing the Control running shoe, the athletes were able to complete the course on an asphalt road surface at maximal effort without slipping. When completing the same course wearing the High Traction shoe, the subjects were able to perform the course even faster. Therefore, the results show that the role of running shoe outsole traction is not to merely provide adequate traction to avoid large scale slips, but can also help athletes enhance performance of high-traction tasks such as accelerations and changes in direction.
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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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".