Rotational traction of soccer football shoes on a hybrid reinforced turf system and natural grass
Bibliographic record
Abstract
Traction between a football shoe and the playing surface influences a players’ ability to perform football-specific movements. Too little traction means a player might slip. Too much traction is thought to increase the risk of injury due to foot fixation on the turf. Rotational traction is linked to increased injury risk in football. Elite football is increasingly played on hybrid reinforced natural grass playing surfaces. Our aim is to assess the magnitude of rotational traction of one new hybrid turf system and compare that to a natural grass (control) surface. Nine different Football shoes from three outsole groups (artificial grass, firm ground, soft ground) were loaded onto a portable shoe-surface traction machine to collect rotational traction data on two different playing surfaces (1. Natural Rye grass, 2. A hybrid reinforced turf system) at a single testing session. Peak rotational traction was significantly different across different shoe models (F = 379.8, df = 8, p < 0.0001) and shoe outsole groups (F = 387.4, df = 2, p < .0001). No significant difference was found between the natural grass surface and the hybrid reinforced turf system after considering the minimal detectable change (MDC) of the traction device. Wide-ranging differences in peak rotational traction were found across different individual soccer shoes and outsole groups. The Adidas Nemesis (AG) showed the lowest traction and the Nike Vision (SG) shoe had the highest traction (MD 28.7 N.m; 95% CIs 26.4–30.9; p < 0.0001). The artificial grass (AG) group showed the lowest traction values while the soft ground (SG) group the highest. This objective shoe-surface traction data can help with more informed footwear choices for football played on this type of hybrid playing surface to minimize the risk of lower extremity injury.
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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 | 0.000 |
| 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 | 0.000 | 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".