Functional Performance Tests, On-Ice Testing and Game Performance in Elite Junior Ice Hockey Players
Bibliographic record
Abstract
This study aimed to explore relationships between fitness, on-ice physical abilities and game performance among elite junior male ice hockey players. Twenty-one major junior ice hockey players (18.9 ± 1.4 years old) participated in the study. Measures including five fitness tests (anthropometric measures, pull up test, bench press test, broad jump, vertical jump) and three on-ice skating tests (multi-stage aerobic skating test, 44-m sprint test, and backward skating test) were assessed during their pre-season training camp. Game performance metrics (collected during the regular season) were collected using InStat software. Results of the (on-ice and off-ice) functional performance test protocol and on-ice tests were analyzed by evaluating correlation coefficients in multiple areas of game performance: 1) physical implication (body checks), 2) offensive contribution (expected goals for, types of zone entries), and 3) defensive actions (blocked shots, expected goals against). They revealed that performance in the broad jump test was associated with skating speed. Some significant correlations were also observed between on-ice test performance indicators such as received body checks, expected goals and blocked shots. In summary, results indicate that on-ice test protocols were associated with players' performance in multiple aspects of the game. Partial correlation analyses revealed that some of these relationships were specific to the player's position. Forward skating was associated with forwards' offensive play, and backward skating was specifically related with defensemen's performance (offense and defense). The addition of on-ice physical tests appears essential for interpreting the results of ice hockey players' physical tests and integrating these results into players' physical preparation and the in-season follow-up.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".