REGRESSION EQUATIONS FOR ESTIMATING THE QUALITY OF MAXIMAL INSTEP KICK BY MALES AND FEMALES IN SOCCER
Bibliographic record
Abstract
Biomechanics research of soccer skills has greatly lagged behind the sport's popularity compared to many other sports.Even for basic skills, such as the maximal instep kick, relatively few quantitative studies exist.Further, most of these fail to provide practical means to judge kick quality.The current study proposes to address this deficiency by establishing user-friendly regression equations that apply to both novice and advanced players.These allow an easy way for coaches/teachers to evaluate kick quality.The method consisted of 3D data collection (VICON system with nine high-speed cameras, 120 Hz), full-body biomechanical modeling and correlation and regression analyses of ball release speed with flexion/extension of shoulder, trunk, hip, knee and rotation of trunk as well as last stride length and body height.Twenty-four male and twenty-six female college students, equally divided into novice and advanced cohorts, participated.Results showed many of the correlations to be reliable predictors of a kick's effectiveness.However, they are not practical since extensive use of technology and time-consuming data processing is needed.Further analyses showed multi-regressions using last stride length and body height as independent parameters to have equally reliable evaluation potential.The study concludes that, since the last two independent parameters are easy to measure, these regression equations provide an eminently practical means to evaluate the maximal instep soccer kick.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".