Peak Kinematic and Mechanical Demands According to Playing Positions in Professional Male Soccer: Composition Analysis and Characteristics of Locomotor Activities
Bibliographic record
Abstract
This study examined the most demanding passages (MDP) of composite kinematic and mechanical activities in professional soccer according to positions. Global positioning system data were collected from 39 male soccer players across two seasons. Kinematic and mechanical MDP were identified by composite MDPk (maximal distance covered across moderate- and high-speed running and sprinting thresholds) and MDPm (maximal magnitude of high-intensity acceleration and deceleration efforts) criterion variables across 5 min moving average periods. Linear mixed models assessed the intensity, number of efforts, and duration of specific locomotor activities of each MDP by position. The kinematic MDP showed higher intensity, effort count, and relative duration of MDPk activities than mechanical MDP (p ≤ 0.001; ES: 0.7-1.7). Conversely, MDPm activities had greater magnitude, efforts, and relative duration in mechanical MDP (ES: 1.9-2.0). Similar constituent variable compositions were observed between peak periods. The MDPk comprised ~60 ± 16% moderate-speed running, ~30 ± 11% high-speed running, and ~14 ± 8% sprinting distances; MDPm included ~35 ± 23% and ~65 ± 23% high-intensity accelerations and decelerations. Positional differences revealed central defenders had lower, while full-backs and wide-midfielders had higher, MDP values. Findings from this study highlight the multidimensional characteristics of composite peak kinematic and mechanical periods in professional soccer. The differential contribution of low- and high-intensity locomotor activities during such periods, in terms of magnitude, number of efforts, and duration, should be considered by practitioners. Such insight can inform effective position-specific training prescription as well as bespoke recovery strategies based on the MDP observed during match play.
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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 | 0.001 |
| 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".