Linear skating speed key performance indicators in ice hockey: global or cohort-dependent?
Bibliographic record
Abstract
The purpose of this investigation was to determine whether off-ice key performance indicators (KPIs) of linear skating speed are global across all skaters or modulated based on relevant cohort-dependent covariates. A total of 112 development- and university-level hockey players completed on-ice (30-m skate; ICE 0–30 m) and off-ice assessments (30-m sprint with split times, countermovement jump; CMJ, broad jump, and maximum chin ups). A linear regression model was created to predict ICE 0–30 m times from off-ice inputs with height, body mass, age level, and strength level included as covariates. Model parameters were estimated using the LASSO method with k-fold cross validation. The final model had a cross-validated R2 of 0.806. The strongest predictor of ICE 0–30 m times was 20–30 m sprint time (ß = 0.088). Relative propulsive mean power (ß = -0.064) from the CMJ, 0–30 m sprint time (ß = 0.058), and broad jump (ß = −0.046) represented second-tier predictors. Both relative braking net impulse (ß = 0.043) and relative braking mean power (ß = −0.009) from the CMJ were predictive factors for lower strength players only. The results indicate that top speed sprinting represents the primary global KPI and closest off-ice proxy for skating speed regardless of cohort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".