Analysis of power play in 2018 Varsity Cup Rugby competition
Bibliographic record
Abstract
Varsity Cup (VC) rugby aims to promote young talent in the university environment. Power Play (PP) was implemented in VC rugby. This study analysed and evaluated the influence of the PP law in the 2018 VC on team strategy and implementation thereof during matches. Data were collected from 33 VC games played during 2018. Video footage of matches was analysed. The number of tries, penalties, conversions and points scored, players selected to leave the field, time in the game that PP was selected, set phase restart options and areas of restart of each PP were recorded. A questionnaire with open ended questions for all coaches of the participating universities reported their experiences with the PP. Most PPs were called during the third quarter (32.8%) of the game. The centres, players 12 (28.4%) and 13 (27.6%) were sent off most frequently. The teams who called PP scored 14 times (21.9%). The most popular starting position was a right-side scrum (38.8%). Coaches agreed that PP had the opposite outcome than was expected. The increased number of errors by attacking teams and willingness of opponents to slow down play, contributed to unsuccessful implementation of PP in Varsity Cup rugby. Keywords: Rugby; Varsity Cup; Power 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".