Relationship of Physiological Fitness Tests and Early Career Hockey Success in Elite Ice Hockey Players
Bibliographic record
Abstract
Talent identification in professional sport is an important component for building a successful team. In an effort to improve athlete identification, physiological fitness assessments are included in a number of leagues, and attempts have been made to quantify the predictive ability of individual tests. In the National Hockey League (NHL), physiological fitness measures have been shown to correlate with draft selection order, but less subjective rankings of success have previously not been considered. PURPOSE: To determine the predictive ability of draft-age physiological fitness test outcomes to forecast the cumulative total games played within the player’s first 3 consecutive NHL seasons. Games played are an accepted indicator of success at the professional level. METHODS: Multiple linear regression modeling was used with NHL Combine data (1998-2007) for a total of 451 players, who played ≥1 game per year during their first 3 NHL seasons. Separate models were developed for forwards and defense, given the differing physiological profiles and positional demands. RESULTS: For defensemen, the variables of vertical jump (VJ), push-ups, bench press, and body index were included in the final model (p=0.001, r2=0.15). The regression equation to predict number of games played by defensemen over 3yr was: total games= 177 -0.979 (VJ) + 4.068(max push up) -3.87(max bench press) +9.5 (Body index). The only predictive variable for forward players was push up repetitions (p=0.01, r2=0.04); total games =193.1 -2.3(max push-up). CONCLUSION: NHL Combine data was useful for explaining approximately 4% (forward) and 15% (defense) of the variance in games played amongst players who played ≥3yr in the league, suggesting a modest predictive ability. Upper body strength proved an important predictor for both models, while leg power was identified as a predictor only in defensemen. Homogeneity in performance amongst such elite athletes may explain the absence of more traditional measures of physiological fitness (VO2max, anaerobic power) within the predictive models, as a high level of these physiological traits is obligatory for all players who remained in the league at least three years.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".