Examining the relationship between off-ice testing and on-ice performance in male youth ice hockey players
Bibliographic record
Abstract
Background: In an elite sport setting, physical assessments are administered for talent selection purposes, as well as for continuous monitoring to ensure the effective implementation of training methods to reach optimal sport performance. Physical assessment allows coaches and trainers to determine where an athlete ranks compared to other players, as well as to identify the strengths and weaknesses of the individual. In ice hockey, research has focused on high performance players (e.g., NHL prospects) and the physical characteristics that they possess. To date, the early assessment of youth minor hockey players, and the relationship between off-ice and on-ice performance has received little attention. Purpose: The purpose of this investigation was to examine the relationship between off-ice physical fitness performance and sport-related performance on on-ice assessments in male, minor ice hockey players. Methods: Eleven male minor hockey players were recruited across three birth years (2004, 2005, and 2006). Participants completed a battery of 14 off-ice testing protocols that measured body composition, musculoskeletal fitness, aerobic fitness, and anaerobic fitness, as well as 4 on-ice protocols that measured skating speed, skating agility, skating acceleration, and shot velocity. Results: Older players were taller and heavier than the younger players, and defensemen were taller and heavier when compared to forwards. Across participants, standing long jump was positively correlated to all skating tests (i.e., speed, agility, and acceleration). Players who jumped further demonstrated significantly greater on-ice skating performance. Significant correlations were also found between player weight and maximum speed, agility, and shot velocity. Lighter players were faster and more agile on the ice, while players with a greater mass demonstrated higher scores in shot velocity. A significant relationship was also found between push-ups and off-ice sprinting capability. Conclusion: These findings were consistent with high performance research with adults revealing that physical measures (such as standing long jump) may have predictive value for on-ice performance even in young, pre-pubertal ice hockey players. While such measures may contribute to the successful identification and selection of players for high performance, utilizing such assessments also has important training implications for the long-term development and performance of all 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) | 0.000 | 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) | 0.002 | 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".