Quantifying Inter-Segmental Coordination during the Instep Soccer Kicks
Bibliographic record
Abstract
In order to generate a high ball speed in soccer, the inter-segmental coordination of the kicking leg is critical. The purpose of this study was to quantify the coordination between the thigh and shank movement in the sagittal plane during instep kicks. Eleven female soccer players were video recorded using a high-speed (80 Hz) video camera during penalty kicks. Hip, knee and ankle joint centers of the right leg were digitized, and the movement was analyzed using Dartfish TeamPro (6.0). The thigh and shank segment angles were generated, and the coordination was quantified using the cross-correlation and the vector coding method. Four coordination patterns were defined based on coupling angles: in-phase, anti-phase, thigh-phase and shank-phase. The time spent in each coordination pattern was analyzed. The cross-correlation coefficient was positive for all the participants, indicating that the two segments rotated with similar patterns. Based on the vector coding method, we observed dominant coordination patterns of shank-phase and in-phase during the backswing and forward swing phase, respectively. We hope the outcomes of our study could provide a better understanding of soccer kicking coordination and benefit training young soccer players. Future studies may use the methodology and outcomes in the present study to investigate the coordination of different levels of players to better understand the process of skill acquisition.
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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 | 0.001 | 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".