On-ice acceleration as a function of the Wingate anaerobic test and a biomechanical assessment of skating technique in elite ice hockey players
Bibliographic record
Abstract
Success in ice hockey depends on an individual?s ability to accelerate from a standing \nstart or change direction and continue skating quickly and efficiently. Previous research to \ndetermine those factors which had the greatest contribution to on-ice acceleration was limited to \ntwo-dimensional biomechanical analyses of skating technique, without regard for the influence \nof physiological measures. The purpose of the present study was therefore to predict on-ice \nacceleration using peak anaerobic power from a Wingate test and kinematic variables from a \nthree dimensional analysis of the biomechanics of skating technique. A sub-purpose of the \npresent study was to examine the variability of skating technique at the elite level. The \nparticipants in this research study were thirty-seven ice hockey players from the Florida Panthers \nand Los Angeles Kings of the National Hockey League participating in the 1999 Prospects Camp \nin Thunder Bay, Ontario. The players completed a thirty second, maximal intensity Wingate \nanaerobic cycle ergometer test against a resistance of 0.095 kg-kg bodyweight-1. Peak anaerobic \npower was calculated and recorded as the highest anaerobic power value (number of flywheel \nrevolutions) produced during any of the five-second intervals. One week following the Wingate \nanaerobic test, the players performed two maximal, on-ice accelerations over a distance of \ntwenty meters, while being taped by two, Panasonic? CL-350 digital cameras mounted on Peak \nPerformance? pan/tilt heads. The Peak Performance? 3D Video Analysis System and a 23- \npoint spatial model were used to extract the raw coordinates for the fastest of the two trials for \neach player, as measured by a photoelectric timer. The system was then used to smooth the raw \ndata from both camera views and to combine the smoothed data to produce a three-dimensional \nimage. Center of mass and kinematic variables of interest were measured at push-off and \ntouchdown for the first five strides.
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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 | 0.001 | 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) | 0.001 | 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".