Change of Direction Movement Evaluation in Soccer-Specific Environment with Inertial Measurement Units: Guiding Practice and Test Tasks in Youth Soccer
Bibliographic record
Abstract
Soccer players perform a multitude of change of direction (COD) movements while playing. This multiplanar movement has been related to both performance and injury-risk in previous studies. However, traditional testing of COD ability has been done with preplanned protocols that lack the aspect of perception and reaction and commonly use only running time as the main variable. Therefore, the main objectives of this thesis were to explore novel methods of COD testing with the use of inertial measurement units (IMUs) in both preplanned soccer-specific tests and during game-play. The results of Chapter Three suggest that neither peak resultant acceleration (PRA) nor peak angular velocity (PAV) is a reliable metric in final foot contact (FFC) analysis of 180° pivot turns. The intra-class correlations (ICC) for pivot turns on both sides were unacceptable. However, when separating females and males it was found that the reliability in female participants was significantly better. In Chapter Four, the in-season variability of PRA was found to be different between previously injured players and injury free players, specifically during the FFC of 180° pivot turns. Chapter Five expanded upon the game-specific demands on COD movements based on playing positions. Significant differences in volume and types of CODs by playing position were found, which raises the question if youth soccer player testing for multiplanar movement abilities, should consider specific playing position related demands better in the future. Chapter Six complemented the studies by providing results of measurements obtained with IMUs in relation to situational patterns during game-play. The findings indicated that running speed, COD angle, pressure from opposing player, and contact with another player prior or during the cut would increase the acceleration during the COD, thus increasing the demands of the neuromuscular system. In conclusion, following one or two specific metrics at single timepoints to analyze COD ability is not recommended. Future research should search for methods involving perception-reaction while performing COD and these could be complemented with wearable technology measures. The combinations of multiple variables could be used to follow-up fluctuations of player performance through a longer follow-up period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".