A Comparison of On-Ice External Load Measures Between Subelite and Elite Female Ice Hockey Players
Bibliographic record
Abstract
ABSTRACT: Douglas, AS, Rotondi, MA, Baker, J, Jamnik, VK, and Macpherson, AK. A comparison of on-ice external load measures between subelite and elite female ice hockey players. J Strength Cond Res 36(7): 1978-1983, 2022-This study quantified and examined differences in measures of on-ice external load for subelite and elite female ice hockey players. External load variables were collected from subelite (N = 21) and elite (N = 24) athletes using Catapult S5 monitors during the preseason. A total of 574 data files were analyzed from training and competition during the training camp. Significant differences between groups were found across all variables. Differences in training between the 2 groups ranged from trivial (forwards PlayerLoad, p = 0.03, effect-size [ES] = 0.18) to large (forwards Explosive Efforts [EEs], p < 0.001, ES = 1.64; defense EEs, p < 0.001, ES = 1.40). Match comparisons yielded similar results, with differences ranging from small (defense Low Skating Load [SL], p = 0.05, ES = 0.49; Medium SL, p = 0.04, ES = 0.52) to very large (forwards PlayerLoad, p < 0.001. ES = 2.25; PlayerLoad·min-1, p < 0.001, ES = 2.66; EEs, p < 0.001, ES = 2.03; Medium SL, p < 0.001, ES = 2.31; SL·min-1, p < 0.001, ES = 2.67), respectively. The differences in external load measures of intensity demonstrate the need to alter training programs of subelite ice athletes to ensure they can meet the demands of elite ice hockey. As athletes advance along the development pathway, considerable focus of their off-ice training should be to improve qualities that enhance their ability to perform high-intensity on-ice movements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".