Does disallowing body checking impact offensive performance in non-elite under-15 and under-18 youth ice hockey leagues? A video-analysis study
Bibliographic record
Abstract
Policy that disallows body checking (BC) lowers the injury and concussion rate for youth ice hockey players. However, little is known about how disallowing BC influences in-game metrics of performance. This prospective cohort video-analysis study examined offensive performance in Under-15 (ages 13–14) and Under-18 (ages 15–17) youth ice hockey players in leagues allowing and disallowing BC. Fifty-two games were filmed (n = 13 BC, n = 13 non-BC) for Under-15 and Under-18 non-elite (lowest 60% and 45% divisions, respectively) divisions in Calgary, Canada. Footage was analyzed for offensive performance metrics on the puck-carrier using the validated ice hockey adapted team sport assessment procedure. Puck metrics included how the player acquired puck possession (e.g. conquered puck from an opponent, received pass from a teammate) and the outcome (e.g. shot on goal, lost puck to opponent). The puck metrics were used to compute a performance composite score for each player that accounted for the quantity (rate of puck possessions per shift time) and quality (a ratio of positive performance metrics to all metrics) of play. Mean difference's (MD) in performance composite scores were compared using multivariable linear regression (adjusted for player position and cluster by team-game) between leagues allowing and disallowing BC for both age groups. Analyses revealed no significant MD in the performance composite scores between players in BC and non-BC leagues for both age groups (Under-15: MD = 0.02, 95%CI: −0.08, 0.12; Under-18: MD = −0.06, 95%CI: −0.16, 0.03). These findings suggest no differences in offensive performance when BC is disallowed in Under-15 and Under-18 non-elite leagues.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".