The science and art of testing in ice hockey: a systematic review of twenty years of research
Bibliographic record
Abstract
Introduction Ice hockey is a complex sport requiring multiple athletic and technical attributes. Considering the variety of tests developed, on-ice testing protocols have been created to measure the physiological and mechanical attributes associated with performance. To our knowledge, a lack of technical resources exists to help stakeholders opt for on-ice protocols from among those developed. It becomes crucial for researchers and practitioners to select relevant and context-specific procedures. This systematic review of the literature outlines an inventory of the on-ice tests that have been used in the domain of ice hockey research over the last twenty years, and summarize protocols mostly used in major athletic components. Methods A search was performed on three databases (PubMed, SPORTDiscus and Scopus) by following the PRISMA guidelines. Specific keywords were selected to find publications using on-ice testing protocols in the methodology. Four aspects of athletic attributes were used to categorize the protocols: aerobic capacity, acceleration-speed, agility-change of direction and ability to repeat skating sprints. Analyses were conducted regarding four categories of observations: population under study, on-ice reported test(s), outcomes measures and main findings. Results A total of 107 articles were included, resulting in 55 on-ice tests related to the on-ice assessments of four major athletic components: aerobic capacity ( n = 7), acceleration-speed ( n = 6), agility and change of direction ( n = 23) and repeated skating sprint ability ( n = 19). Testing in male and older cohorts (≥16 years old) predominates, with a primary focus on the competitive amateur level. The selected tests were mainly designed for assessing on-ice physiological responses and fitness ( n = 38), talent identification-team selection ( n = 19), efficiency of interventions ( n = 17) and validation purposes ( n = 16). Conclusion A prevalence of on-ice skating tests to assess the ability to repeat intense efforts, agility, acceleration and speed components exists, which are relevant and linked to match requirement. The wealth of on-ice tests used in the literature reflects the need to adapt the on-ice evaluation process to the population, constraints, and goals. This review is a valid toolbox and can benefit for researchers and practitioners interested in testing hockey players from different levels, with a variety of aims and needs, by helping them to select the relevant procedures to their environment and practice context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".