Identification of critical traction values for maximum athletic performance
Bibliographic record
Abstract
The purpose of this study was to investigate the relationship between mechanically available footwear traction and performance in top-speed curved sprint running and maximum effort linear acceleration. Based on results from previous studies, it was hypothesized that performance would increase as available traction increased but only to a point after which performance would plateau and further increases in available traction would not affect performance. The goal of this study was to identify such critical traction values. Thirty-two recreational athletes performed maximum effort 2.3 m radius curve sprints and linear accelerations from a standing start using four identical mid-cut basketball shoes differing only in outsole traction. Available traction was modified by manipulating the outsole material. The traction coefficients of the test shoes, quantified with a portable traction tester on the actual test surface, were 0.26, 0.54, 0.82 and 1.13. Ground reaction forces and three-dimensional kinematics were quantified during the tests. Greater amounts of traction (both peak and average) were utilized as the mechanically available traction increased. Increases in available traction from 0.26 to 0.54 to 0.82 provided systematic performance advantages for both curved sprinting and linear acceleration. However, no further performance enhancements were detected when the available traction increased beyond 0.82. Increases in the use of available traction beyond a threshold of 0.82 were reflected in the peak but not the average utilized traction or overall ground reaction impulse generation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".