Falling and Landing Framework (FLF): A Consensus on a Novel Falling and Landing Video Analysis Framework for Use Across Rugby Codes
Bibliographic record
Abstract
Understanding how players experience head-acceleration events (HAE) whilst playing rugby is a priority area of research. In both rugby union and league, video analysis frameworks have been developed to comprehensively define key features of contact events. However, these frameworks were developed prior to recent advances in our understanding regarding the proportion of HAEs that occur due to head-to-ground mechanisms and do not consider important post-contact variables. Therefore, there is a need to supplement the existing frameworks in order to capture how players fall and land post-tackle. This study used the Delphi method with an interdisciplinary, international team of researchers, coaches and video analysts (working with a variety of playing levels in rugby union and league) to establish a consensus for defining falling and landing events. Subsequently, a draft framework was developed on which the research team provided feedback via online meetings, culminating in the falling/landing framework that each member of the research team rated agreement on, via a nine-point Likert-type scale, with consensus deemed to be reached when the median score was ≥ 7. The median scores were 8.0 (7.8-8.0), 8.0 (7.0-9.0) and 8.0 (8.0-9.0) for 'Additional Contextual Characteristics for Carry and Tackle Events,' 'Falling Characteristics of Tackle and Carry Events,' and 'Landing Characteristics of Tackle and Carry Events,' respectively. This novel framework defines more comprehensive falling and landing variables to capture post-contact injury and performance markers in both rugby union and league, through a standardised approach.
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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 | 0.088 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.005 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".