Effects of Game-Specific Demands on Accelerations during Change of Direction Movements: Analysis of Youth Female Soccer
Bibliographic record
Abstract
The aim of this study was to assess center of mass (COM) acceleration and movement during change of direction (COD) maneuvers during a competitive soccer game to elucidate situation-specific demands of COD performance. This information can assist in developing soccer-specific tests and training methods. Fifteen elite-level female youth soccer players were tracked for one game with inertial measurement units (IMU) attached to the lower back. COD movements in combination with situational patterns were identified using high-speed video. LASSO regression was used to identify the most important predictors associated with higher vertical peak accelerations (PAv) of the COM during COD movements. COD angle, running speed, contact, and challenge from the opposition were identified as important features related to higher PAv. This study adds to the literature on the demands of COD performance in soccer match-play. The unique approach with game-specific situational data from female youth players provides increased insight into the game-demands of COD and agility performance. PAv in games was higher with larger COD angles, increased running speed, or with contact when the player was challenged by the opposition. A larger study including more games is warranted to increase confidence in using these variables as a basis for training or testing agility.
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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".