Are recommended tackle techniques associated with superior performance outcomes? A retrospective video analysis study of elite women’s rugby union
Bibliographic record
Abstract
This cross-sectional study aims to identify the situational characteristics, ball-carrier technical variables, and Tackle Ready recommended techniques associated with performance outcomes in elite women’s Rugby Union. Using retrospective video analysis, 43 tackler and ball-carrier technical characteristics for 1500 tackle events in the 2022–23 Women’s Six Nations Championship were assessed, considering match situation and performance outcomes. Rate ratio (RR) was determined using propensity rates. Effective tackles were associated with match situations involving two defenders, forwards tackling forwards, defensive teams moving forwards, and tackles initiated closer to attackers at ball reception. Seven out of the 22 coded Tackle Ready techniques were significantly associated with superior performance outcomes. Techniques associated with the greatest likelihood of effective tackle outcome included wrap and clamp (RR 46.8) and ear to body (RR 20.9). Tackles made to the hip and leg of the ball-carrier increased the risk of missed tackles. This study provides the first analysis of tackle characteristics associated with performance outcomes in women’s rugby, providing a reference to inform coaching practice and the implementation of tackle education resources and law changes. Further research is warranted to explore techniques associated with injury risk, and interactions between match situations and subsequent tactical/technical tackle actions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".