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.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".