Impact Biomechanics Reveal Positional and Session Type Differences in Canadian Collegiate Football
Bibliographic record
Abstract
Frequent head impacts are common in Canadian football, yet the biomechanical determinants underlying repeated subconcussive exposure and their potential implications remain poorly characterized. To address this, we investigated the biomechanical impact characteristics of college-level Canadian varsity football players, aiming to elucidate the underlying factors that drive subconcussive impacts. Sixty-four athletes were outfitted with head impact sensors during games, practices, and training camps. We examined impact frequency, peak linear and rotational acceleration, impact duration, area under the acceleration-time curve (AUAC), impulse, and head jerk, grouping participants as small skill (SS), big skill (BS), or linemen (LN). Significant differences emerged based on both player position and session type. Linemen experienced the highest AUAC and impulse values, whereas SS and BS positions were associated with less frequent but higher-magnitude impacts. Session type further influenced exposure, with games producing greater peak accelerations and longer impact durations than practices or training camps. These results demonstrate that analyzing linear acceleration time series reveals more nuanced insights into the complex dynamics of subconcussive impacts than peak magnitudes alone. Such analyses establish a critical foundation for linking biomechanical parameters to injury risk and neurophysiological biomarkers, ultimately informing data-driven strategies to enhance athlete safety in contact sports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".