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 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 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".