33 Performance predictors in ice hockey and speed skating: a systematic review
Bibliographic record
Abstract
Introduction Performance in ice hockey and speed skating relies heavily on the biomechanics of skating, which encompasses a complex interplay of joint flexibility, muscle activity, push-off mechanics, arm swing and stride mechanics. Both sports demand a high level of technical precision, power, and endurance, requiring athletes to optimize their movements to achieve peak performance. Numerous studies have investigated various aspects of skating performance, yet a comprehensive synthesis identifying the most important factors is lacking.Materials and Methods This review was carried out according to the PRISMA guidelines. Three online databases were searched, resulting in 1422 extracted articles. A total of 35 articles were included following screening. A modified AXIS was used to evaluate methodological quality where items not deemed relevant for this investigation were excluded.Results This review highlights critical performance predictors in both sports, emphasizing skating mechanics, stride parameters, off-ice variables, and muscle activity. Key factors like stride rate and length, and push-off mechanics play pivotal roles in acceleration and speed. High-caliber athletes generally displayed superior joint flexibility, balance, and coordination, enabling better transitions between push-off and gliding. Off-ice variables, such as strength and muscle activation in the lower body, also enhance on-ice performance. The average methodological quality score was 13.6/17, ranging from 11 to 16.Conclusion These findings are significant for athlete development, offering insights that coaches, trainers, and scouts can use to design more effective training programs. Understanding skating mechanics, off-ice variables, arm movement, and muscle activity will improve technique, efficiency, and performance in both sports.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".