Bibliographic record
Abstract
Many studies try to understand fundamental soccer skills, most focusing on kicking. However, a full picture of an efficient kick remains incomplete owing to constraints of test designs and difficulties that arise in synchronizing and analyzing information generated by multiple assessment techniques. Previous scientific studies may be generally categorized as: two-dimensional kinematic analysis using high-speed cameras, muscle activity studies using electromyography, three-dimensional analysis of the kicking-leg using a partial-body model, and kinetic studies using force measurements and modelling. No existing inquiries have used full-body three-dimensional motion capture and modelling to examine kicking. The current study remedies this deficiency and defines a full-body model capable of revealing more detailed characteristics of kicking. Additionally, it reveals effects of long-term training by comparing novices with skilled athletes and explores new parameters that have potential to aid quantitative evaluations of skill. Results show effective upper-body movement to be a key factor in creating better initial conditions for a more explosive muscle contraction during kicking. It permits a more powerful quasi whip-like movement of the kicking leg. Finally, the timely change of distance between the kick-side hip and the non-kick-side shoulder provides a quantitative means of measuring kick quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.100 | 0.033 |
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".