The Effect of Sequence of Skating-Specific Training on Skating Performance
Bibliographic record
Abstract
PURPOSE: To determine the effectiveness of a progressively "skating specific" periodized off-season training program on skating performance in competitive hockey players. METHODS: Twenty (M = 18; F = 2) highly skilled hockey players (age 15.9 +/- 1.5 yr) completed 16 wk of standardized resistance and stability training supplemented with either off-ice simulated skating using the SkateSIM (SIM) or plyometric training (PLY) in a crossover design. Group 1 (PLY-SIM; N = 11) completed 8 wk of PLY followed by 8 wk of SIM. Group 2 (SIM-PLY; N = 9) completed 8 wk of SIM followed by 8 wk of PLY. Subjects completed on- and off-ice testing PRE, MID, and POST training. RESULTS: Significant improvements in on-ice 35-m skating sprint (1.0%; P = .009) with significant improvements of 5% to 12% in various off-ice testing measures were observed PRE-MID in both groups. While few off-ice tests improved MID-POST, on-ice 35-m skating sprint times improved MID-POST by 2.3% (P = .000) with greater improvement in PLYSIM (3.5%) versus SIM-PLY (0.8%; P < .002). Off-ice 30-m sprint (r = 0.56; P = .010) and Edgren side shuffle (r = -0.46; P < .040) were the only off-ice tests that significantly correlated to improvements in on-ice skating sprint performance. CONCLUSION: The initial gains PRE-MID and then the lack of improvement in many off-ice tests from the MID-POST supports the principle of diminishing returns in response to standardized resistance training. The improvement in on-ice skating sprint performance when supplemental training progressed in specificity supports the principle of specificity and promotes transfer to a complex sporting movement such as skating.
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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 | 0.001 |
| 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) | 0.002 | 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".