Characterizing the most demanding passages of kinematic andmechanical activity in elite football: a multifactorial approach
Bibliographic record
Abstract
This study investigated the 5-minute most demanding passages (MDP) of kinematic (distance covered) and mechanical (acceleration and deceleration) activities in elite male football according to multifactorial criterion performance variables.Global positioning systems data were collected from 39 players across 45 matches in the Swedish first division (n = 329 observations).The multifactorial kinematic variable (MDPk) was composed of the concurrent distances covered at moderate-speed running, high-speed running, and sprinting distances, and the multifactorial mechanical (MDPm) considered the concurrent occurrences of high-intensity acceleration and deceleration activities.A moving average method was used to identify the MDP across a 5-minute period.The frequency distribution of the multifactorial variables, and differences in the time of occurrence and magnitude between multifactorial performance variables and their univariate constituent variables were investigated.Frequency distribution analysis revealed MDPk and MDPm peaked in the first 5 min of the match (MDPk: χ 2 (15, N = 329) = 135.88,p ≤ 0.001, W: 0.64, 115.99 ± 32.7 s; MDPm: χ 2 (15, N = 329) = 31.02,p ≤ 0.001, W: 0.31, 101.21 ± 25.1 s; p ≤ 0.0004).Within each half, differences in the MDP commencement time between the multifactorial variables and their respective discrete univariate constituent variables were trivial to small (MDPk effect size (ES): 0.04-0.21;MDPm ES: 0.02-0.11).Linear mixed model analysis demonstrated that the MDP magnitude of multifactorial variables were approximately 8-28% and 8-21% lower across the match and halves, respectively, compared to the MDP of their respective univariate constituent variables (p ≤ 0.001; r = 0.26-0.62).The greatest differences between the respective multifactorial and discrete constituent variables were observed for sprinting (-28%) and high-intensity acceleration (-22%).The results reveal that the MDP is distinguished based on peak kinematic and mechanical demands, which occur at discrete periods and exhibit distinct locomotor profiles across the match and within each half.Practitioners should consider the methods of identifying the MDP, as the selection of univariate and multifactorial kinematic and mechanical performance variables can impact MDP characterization, which can qualify the designing of bespoke training protocols.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".