Physiological Predictors Of On-ice Hockey Performance In Elite Adolescent Hockey Players
Bibliographic record
Abstract
Ice hockey is a physiologically demanding sport, requiring high levels of fitness and skill. The use of physiological variables to predict success in hockey has gathered plenty of attention at the NHL level. Despite the emphasis placed on fitness at every competitive level of the sport, there is a paucity of research examining the physiological predictors of individual success in hockey at an adolescent level. Understanding which factors will translate into on-ice success will allow a coach to focus training, thus optimizing player development and performance. PURPOSE: To examine and identify physiological variables that are predictive of on-ice hockey performance in adolescents. METHODS: One hundred and fifty-one male bantam and midget level hockey forwards were tested before the 2013-14 AAA hockey season. Various tools and methods were used to assess physiological measures, including player height, weight, body fat percentage, aerobic capacity, anaerobic power, agility and strength. Individual player goal and assist totals were congregated following the regular season. Stepwise regression was utilized in order to determine the predictive ability of pre-season player physiological test results for player goal and point totals. RESULTS: The mean age, height and mass of the players were 14.3 ± 1.2 yr, 175.3 ± 8.9 cm and 65.5 ± 12.1 kg, respectively. The off-ice beep test stage completed by a player was found to be predictive of total goals scored throughout the season (adjusted R2 = 0.101, p < 0.05), and average player points per game (adjusted R2 = 0.090, p < 0.05). The 5-10-5 shuttle run time was found to be predictive of total point production (adjusted R2 = 0.092, p < 0.05). When assessed together, beep test and shuttle run results were predictive of total goals (adjusted R2 = 0.138, p <0.05) and total points (adjusted R2 = 0.125, p <0.05). CONCLUSION: For adolescent hockey forwards, aerobic capacity (beep test) and agility (5-10-5 shuttle run) can significantly predict total goal and point production. Moreover, player size, strength, and anaerobic power were not predictive of goal or point output. Thus, to augment hockey success in adolescence, an emphasis should be placed on increasing aerobic- and agility-fitness parameters.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".