Effect of Forefoot and Midfoot Bending Stiffness on Agility Performance and Foot Biomechanics in Soccer
Bibliographic record
Abstract
Footwear bending stiffness is known to positively affect performance in agility maneuvers due to improved energy storage and propulsion based on a stiffer foot-shoe complex. However, the functional properties of the forefoot and midfoot differ. Therefore, the present study investigates the effect of the interface of longitudinal bending stiffness and the ratio of forefoot to midfoot bending stiffness on agility performance and foot biomechanics. A total of 18 male soccer players performed 2 agility tasks in footwear conditions that were systematically modified in forefoot and midfoot bending stiffness. Results revealed that higher longitudinal bending stiffness caused more foot exorotation at the initial ground contact (P < .05), less torsion (P < .001), and an anterior shift in the point of force application during push off (P = .01). In addition, the authors observed decreased forefoot bending (P < .05) and increased torsion (P < .01) in footwear with a higher forefoot-midfoot ratio. Finally, the agility performance was significantly impaired by 1.3% in the condition with the highest forefoot-midfoot ratio (P < .01). The high forefoot-midfoot ratio, that is, a stiff forefoot in combination with a soft midfoot, seemed to shift the flex line from anterior to posterior that may explain the performance impairment.
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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) | 0.000 | 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".