Functional Performance Testing in Ice Hockey: the Role of the Single Leg, Medial Countermovement Jump
Bibliographic record
Abstract
Due to the physical nature of the game, injuries are common in ice hockey. Injury rates have been difficult to interpret due to the inconsistencies in the definitions of injury and athlete exposure. Consensus statements on injury definitions have been developed for sports such as soccer and rugby but have not been established in ice hockey. Furthermore, many different off-ice tests are performed, but a hockey-specific performance test has not been promoted. Accordingly, the objective for this thesis was to investigate injury rates, injury definition, athlete exposure and injury type in men’s ice hockey, and providing information on a practical test practitioners can use to monitor fatigue and measure performance. This was achieved through three research projects. An integrative literature review was conducted to suggest a specific definition of injury and athlete exposure (Chapter 2). This study identified that the International Ice Hockey Federation’s definition of injury is preferred based on the clarity and relevance of the injury description and that the preferred athlete exposure metric is player game-hours based on accuracy and ease of use. In addition, lower extremity injuries were identified as common and costly in men’s ice hockey. The single leg, medial countermovement jump was identified as an appropriate hockey-specific performance test. This jump enables objective measures of frontal plane force and power and is particularly applicable for ice hockey players given that ice skating involves applying lateral forces. All twelve parameters of the jump showed moderate to excellent reliability (Chapters 3) suggesting that this jump is a reliable test for assessing frontal plane force and power in ice hockey players. Finally, normative values and asymmetry indices were presented in ninety-one male youth hockey players aged 10–18 years (Chapter 4). In conclusion, lower extremity injuries are common in hockey and injury rates are difficult to interpret as the definition is not consistent. The single leg, medial countermovement jump is an appropriate functional test for measuring skating performance. Ice hockey performance staff can use this evidence-based research to measure performance, monitor fatigue, and document recovery from injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".