The associations between training and match demands of male professional football players over a season
Bibliographic record
Abstract
This study had two objectives: (i) to analyze the between-position differences in training:match load ratios and (ii) to test the relationships between the weekly training and match demands of male professional football players over a season. A cohort study lasting 43 weeks was performed. Nineteen professional football players (age: 27.5 ± 4.6 years old) used a 15-Hz global positioning system (GPS) unit integrating a 100-Hz tri-axial accelerometer. Total distance (TD), metabolic power average (MPA), new body load (NBL), accelerations (ACC), and decelerations (DEC) were considered. The training:match ratio was obtained for all the external load measures. Significant between-position differences were found only for DEC. Moderate correlations between the weekly training and match demands were found for NBL (r = 0.343 (0.19; 0.48); p < 0.008) and DEC (r = 0.472 (0.327; 0.595); p < 0.001). Moderate correlations between the mean training intensity and match demands of the same week were found for NBL (r = 0.454 (0.313; 0.575); p < 0.001) and DEC (r = 0.451 (0.304; 0.577); p < 0.001). This study did not show significant position differences for the overall training:match ratios. Significant position differences were revealed for left-back players compared to all other positions. Fullbacks performed four times more DEC during training sessions than during matches. It was revealed small to moderate associations between both the volume and intensity of the overall external load measures and their respective match running demands. However, such correlations are too weak to suggest a cause-and-effect relationship.
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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) | 0.002 | 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".