Shoe traction and surface compliance affect performance of soccer-related movements
Bibliographic record
Abstract
Purpose: To determine how shoe-surface interaction, specifically traction and compliance, affects performance and biomechanics of soccer-related movements.Methods: Third generation artificial turf was installed in the laboratory to allow for kinetic and kinematic data collection both on the turf and on a laboratory surface (Pulastic sports surface). Twelve male athletes performed five 5 m sprint accelerations and five 180° sprint turns in three different shoe-surface conditions (indoor soccer shoe on the laboratory surface, indoor soccer shoe on the turf surface, soccer cleat on turf surface). Comparisons between the indoor shoe across surfaces indicated compliance effects and comparisons between the cleat and indoor shoe on turf indicated traction effects.Results: Performance increased for the sprint acceleration in the indoor shoe on the turf compared to the laboratory (1.04 s vs. 1.08 s); however, no further increase in acceleration performance occurred with the soccer cleat. For the turn movement, no change in performance occurred comparing the indoor shoe across surfaces however an increase in turn performance was seen when using the soccer cleat on turf compared to the indoor shoe (2.67 s vs. 2.56 s). The cleat had both increased utilised translational and rotational traction compared to the indoor shoe on turf for the turn movement. The cleat also resulted in increased ankle eversion moments as well as increased knee abduction and external rotation moments compared to the indoor shoe on the turf surface for the turn movement.Conclusion: Both compliance and traction shoe-surface characteristics affect performance; however, the effects of the different characteristics are different depending on the movement type.
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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 | 0.002 |
| 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) | 0.002 | 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".