Decoding the Tackle: Using a Machine Learning Approach to Understand Direct Head Contact Events in Elite Women's Rugby
Bibliographic record
Abstract
ABSTRACT Concerns about the cumulative effects of head acceleration events in rugby are growing, but how tackle events lead to direct head contact in women's rugby remains underexplored. This cross‐sectional study aimed to develop and evaluate a machine learning model to identify characteristics associated with direct head contact and incorrect tackler head placement in elite women's rugby. Match situational and precontact technical characteristics (n = 31) from 1500 randomly selected tackle events were coded visually and retrospectively analyzed from the 2022–23 Women's Six Nations Championship. A machine learning model was developed and evaluated using a grid search with 5‐fold cross‐validations and F1 scores (i.e., a measure of predictive performance). The top modifiable characteristics associated with the target outcomes across 100 test sets were identified by mutual importance and decision tree modeling. The top modifiable characteristics linked to direct head contact to the tackler were incorrect head placement, coming to balance, and foot placement. Tackle direction, point of contact on the tackler, and multiplayer tackles were key characteristics for incorrect tackler head placement. Tackler drop height, front/oblique tackle direction, and multiplayer tackles were strongly associated with direct head contact to the ball‐carrier. Incorrect tackler head placement, the direction of tackle, tackler drop height, and multiplayer tackles are key characteristics in direct head contact events in elite women's rugby. Addressing these characteristics should be prioritized in contact training practices, education resources, and law enforcement to enhance player safety and direct head contact events in the women's game.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".