Assessment of On-Ice Oxygen Cost of Skating Performance in Elite Youth Ice Hockey Players
Bibliographic record
Abstract
ABSTRACT: Allisse, M, Bui, HT, Desjardins, P, Léger, L, Comtois, AS, and Leone, M. Assessment of on-ice oxygen cost of skating performance in elite youth ice hockey players. J Strength Cond Res 35(12): 3466-3473, 2021-The purpose of this study was to evaluate the robustness of equations to predict the oxygen requirement during different skating circumstances commonly found in ice hockey game situations (skating forward, backward, with and without controlling a puck, during cornering and stops and starts). Twenty-four male elite ice hockey players from 3 categories (pee-wee, bantam, and midget) participated in this study. Anthropometric measurements were taken, and 4 different on-ice high-intensity and short-duration tests were performed. Execution time, heart rate, oxygen uptake, skating strides, and a skating efficiency index were measured for each test. A regression equation was calculated for each of the 4 tests providing an estimation of oxygen cost. Correlation coefficients ranged from 0.91 to 0.93, and SEE was between 4.5 and 8.4%, indicating that the precision of the regression algorithms was excellent. The results also suggest that execution time alone, which is the traditional manner to measure skating performance, is a bad estimator of oxygen uptake requirement for this kind of effort (average common variance <11%). Furthermore, age proved to be a determining factor with younger players showing an overall lower level of skating efficiency compared with older players. In addition, the introduction of a skating index also helps to better determine which factor of performance needs to be improved. Using simple and easy-to-measure variables, coaches will be able to obtain information that will allow them to intervene more precisely on the training parameters that will optimize the individual on-ice performance of their players.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".