Training and match load in professional rugby union: Do contextual factors influence the training week?
Bibliographic record
Abstract
Background: Rugby union demands a multifaceted approach to training, given the multiple physical and technical attributes required to play the sport. Objectives: The aim of this study is to describe the distribution of training throughout the week and investigate how this may be influenced by match-related factors. Methods: Training load data (session Rating of Perceived Exertion [sRPE], total distance and high-speed running [HSR]) were collected from six professional English rugby teams during the 2017/18 season. Five contextual factors were also recorded including: standard of opposition, competition type, result of previous fixture, surface type, and match venue. Results: The day prior to matches demonstrated the lowest training load (101 AU (95% CIs: 0-216 AU), 1 047 m (95% CIs:1 128-1 686 m) and 59 m (95% CIs: 0-343 m), respectively), while four days prior to the match demonstrated the highest training load (464 AU (95% CIs: 350-578), 2 983 m (95% CIs: 2 704-3 262m) and 234m (95% CIs: 0-477m), respectively). Of the five contextual factors, competition type was the only variable that demonstrated greater than trivial findings, with training before European fixtures the lowest stimulus across the four different competition types. Standard of opposition, previous result, surface type and venue had only trivial effects on training load (effect sizes = -0.13 to 0.15). Conclusion: Future studies should outline the distribution of other training metrics, including contact and collision training. This study provides a multi-club evaluation that demonstrates the variety of loading strategies prior to competitive match play and highlights competition type as the most influential contextual factor impacting the average training load.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".