Physical and Performance Differences Among Forwards, Defensemen, and Goalies in Elite Women's Ice Hockey
Bibliographic record
Abstract
Positional differences have been examined in women's basketball, field hockey, netball, and volleyball, but not in elite women's ice hockey. Our purpose was to describe and compare physical, fitness, and skating performance characteristics of forwards (F), defensemen (D), and goalies (G). Subjects were 112 University of Alberta women players (21.4 +/- 2.9 years of age). A full anthropometric battery was conducted on each player. Heath-Carter anthropometric somatotypes were calculated. Percent body fat (%fat) was estimated from both general and population-specific equations. Subjects performed off-ice fitness tests (vertical jump, 40-yd dash, Leger test for predicting .V(O2)max) and on-ice fitness (Modified 3-Repeat Sprint Skate Test-MRSS, blood lactate after sprint test) and skating performance tests (6.10-m acceleration test, Cornering S-Turn Agility Test). Descriptive statistics and multivariate analyses of variance were run using SPSS (Version 10.0) for the MacIntosh, with a significance level set a priori at p < 0.05. Significant positional differences were found for bicristal breadths (D > G, F > G); relaxed arm circumference (D > F, G > F); supraspinale and biceps skinfolds (G > D, G > F); and endomorphy (G > F). Significant differences among positions were also found for the MRSS (G > D > F) and agility tests (G > D, G > F). D tended to have the most robust build overall. F were leaner than D and G, and their smaller relaxed arm circumference measurements most likely reflect less subcutaneous fat on the upper arm. F had greater anaerobic power than D, followed by G, and they tended to have greater aerobic capacity. F and D were more agile than G. Performance demands appear to be position specific. F need to be the most versatile and fit because of a greater amount and variety of work performed both during practices and games; their required degrees of versatility and fitness are followed by those required of D and G.
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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 | 0.001 | 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".