Relative intensities and compositions of multifactorial peak kinematic and mechanical demands in elite youth soccer
Bibliographic record
Abstract
Background: Characterizing the most demanding passages (MDP) of physical activity during soccer competition is essential for optimizing training prescription and player monitoring. However, research investigating kinematic and mechanical MDP using a multifactorial criterion variable approach in elite youth soccer players remains limited. This study examined the relative intensities and compositional structure of multifactorial kinematic and mechanical MDP across different durations in an elite youth international tournament. Methods: ) and the univariate constituent compositions of MDPk [moderate-speed running [MSR], high-speed running [HSR], sprinting [SPR]] and MDPm [high-intensity accelerations [ACC3], decelerations [DEC3]] across rolling (R') 1-, 3-, and 5-minute durations, comparing matches and positional groups (central, lateral). Results: Differences between matches and positions were observed only for R1' MDPk intensities. Both MDPk and MDPm intensities decreased as duration increased. Between-match differences were observed for MDPk composition, particularly for %HSR and %SPR. MDPk composition was duration-dependent, as %HSR and %SPR were greater in R1' than R3' and R5', while %MSR followed the opposite trend. In contrast, MDPm composition remained stable across durations. Conclusions: Practitioners should consider both the relative intensities and compositional structures of MDPk and MDPm when evaluating multifactorial peak demands in soccer. Analyzing kinematic and mechanical MDP as multifactorial constructs offers critical insights into the contribution of specific locomotor demands across various durations. This approach emphasizes the importance of duration-specific analyses in optimizing training, recovery, and match preparation strategies, thereby facilitating targeted training interventions and enhancing player readiness for competition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".