The Impact of Specific High-Intensity Training Sessions on Football Referees’ Fitness Levels
Bibliographic record
Abstract
BACKGROUND: In comparison to the amount of literature that has examined the match demands of football refereeing, there has been little attempt to assess the impact of high-intensity training. PURPOSE: The main goals were to get a better understanding of the long-term effect of specific intermittent training. STUDY DESIGN: The authors examined the cardiovascular strain of specific high-intensity training sessions and also their impact on referees' fitness levels. METHODS: To examine the physical workload during intensive intermittent training sessions, heart rates were recorded and analyzed relative to the referees' maximum heart rate (HR(max)). To assess the referees' fitness levels, the Yo-Yo intermittent recovery test was used. RESULTS: Both the pitch- and track-training sessions were successful in imposing an appropriate high intensity load on the referees, at 86.4 +/- 2.9% and 88.2 +/- 2.4% HR(max), respectively. Following 16 months of intermittent high-intensity training, referees improved their performance on the Yo-Yo intermittent recovery test by 46.5%, to a level that is comparable with professional players. CONCLUSIONS: As match officials are subjected to a high physical load during matches, they should follow structured weekly training plans that have an emphasis on intensive, intermittent training sessions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".