Use of Aggregate Fitness Indicators to Predict Transition into the National Hockey League
Bibliographic record
Abstract
Tarter, BC, Kirisci, L, Tarter, RE, Weatherbee, S, Jamnik, V, Gledhill, N, and Mcguire, EJ. Use of aggregate fitness indicators to predict transition into the national hockey league. J Strength Cond Res 23(6): 1828-1832, 2009-Athletes (n = 345) invited to the annual combine conducted by the National Hockey League (NHL) prior to the entry draft were administered tests to measure upper body strength, lower body power, aerobic and anaerobic energy systems, and body composition. Their common variance was extracted using factor analysis from which an overall composite index was derived. A score on this index in the 90th percentile is associated with 72% and 60% probability of playing in the NHL within 4 years after the draft for defensemen and forwards, respectively. These findings demonstrate that by taking into account the shared variance on standard tests of fitness, it is possible to use the athlete's results to gauge his potential for playing in the NHL.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 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".