Head impact biomechanics across men’s and women’s contact sports: a comparative and clustering analysis
Bibliographic record
Abstract
Sports head impacts have been associated with both acute and long-term brain trauma. While wearable sensors provide valuable biomechanics insight, most studies focus on single sports, and the variability in sensor methodologies limits cross-sport comparisons. Our objectives were to conduct a multisport comparison and clustering of head impact biomechanics features implicated in brain injury risk. We uniformly processed a multisport dataset gathered using instrumented mouthguards containing direct head impacts in men's football, men's hockey, women's rugby, and women's soccer. We statistically compared directional and resultant peak kinematics, impulse durations, and impact directionality metrics. Then, we applied unsupervised k-means and t-distributed stochastic neighbour embedding (t-SNE) models to examine clustering in impact magnitude and frequency features. Statistically significant cross-sport differences were found in all biomechanical features. Men's football exhibited the highest resultant median peak kinematics, while women's soccer showed lowest median resultant kinematics. However, directional comparisons revealed unexpected trends such as women's soccer impacts exhibiting high sagittal kinematics relative to other sports. Clustering analyses grouped impacts into low and high magnitude/frequency clusters that transcended sport boundaries, with only women's soccer impacts demonstrating tight clustering patterns due to consistent heading biomechanics. We uniquely curated a standardized dataset for multisport head impact biomechanics comparisons. Cross-sport differences in under-investigated biomechanical features such as directional peak kinematics may need to be further examined for potential sport-specific injury risk considerations. Despite substantial gameplay differences, we found interesting shared biomechanical patterns across sports, warranting joint analyses to inform implications in protective equipment design and injury prevention strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".