The effects of midsole bending stiffness on ball speed during maximum effort soccer kicks
Bibliographic record
Abstract
The soccer kick is the most prominent movement in soccer. Kicking performance depends on two major factors: kicking accuracy and ball speed. During the soccer kick, momentum is transferred from the foot/cleat to the ball. This interplay between the foot/cleat and the ball can be modelled as a mixture of ‘impulse-like’ and ‘throwing-like’ components. While kicking the ball, the metatarsophalangeal (MTP) joint and the cleat experience deformation. This deformation can be reduced by increasing the bending stiffness of the cleat’s midsole. Therefore, the purpose of this study was to investigate experimentally the influence of midsole bending stiffness on ball speed during maximum effort soccer kicks. Twenty male subjects (mean ± SD; age: 29.5 ± 5.6 years, height: 175.5 ± 6.4 cm, mass: 74.3 ± 8.4 kg) performed six maximum effort soccer kicks in five stiffness conditions. Kinematic data were collected using an optical motion capture system consisting of eight high-speed cameras, and ball speed was recorded using a radar gun. There was no significant difference in the average ball speed between the five stiffness conditions when all subjects were pooled. Further, there was no significant difference in the change of the MTP joint angle from before ball contact to after ball contact between the stiffness configurations. Thirteen (of the 20) subjects showed the highest ball speed in the two stiffest cleats. The differences between the best and the worst performing stiffness configurations across all subjects ranged from 2.2 km/h to 9.2 km/h. The optimal stiffness condition that resulted in highest ball speed was subject specific, and therefore soccer cleats need to be tuned to individual players.
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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) | 0.001 | 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".