In-Season Session Training Load Relative to Match Load in Professional Ice Hockey
Bibliographic record
Abstract
ABSTRACT: Allard, P, Martinez, R, Deguire, S, and Tremblay, J. In-season session training load relative to match load in professional ice hockey. J Strength Cond Res 36(2): 486-492, 2022-The aim of this study was to describe match load and intensity across player positions and match periods, the distribution of pregame training load and intensity over training days before a match, by player position, and the cumulative weekly training load over a season. Fifty professional ice hockey players, which at one time or another were part of the roster for the Laval Rocket during the 2017-18 season of the American Hockey League, participated in this study. External load was monitored in every training session and match over the season using portable inertial measurement units. Data are reported in absolute and relative values to a match. Defensemen have a lower intensity during matches than forwards (-1.70 OIL·min-1) while load is similar across position. The first period shows a higher relative load (+5.28% relative on-ice load [OIL]) while the third has a lower relative intensity (-2.91% OIL·min-1). Defensemen seem to train at a systematic higher relative intensity than wingers and centers (+8.34% relative OIL·min-1). Finally, the weekly training load remains relatively constant throughout the season (equivalent of 3.56 ± 1.69 matches played per week). Our results support the need for player monitoring in ice hockey using an individualized approach.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".