Power output produced during the cycling Power Profile is associated with match-running performance in elite Australian Rules Football
Bibliographic record
Abstract
Australian Rules Football (ARF) match-play requires a high aerobic energy contribution interspersed with near-maximal sprinting.To help prepare for these demands, cycling is a widely used cross-training tool.However, relationships between cross-training performance measures and game-running outputs are unknown.Data was collected from 50 athletes from one elite ARF club over a three-year period.The cycling Power Profile was completed at the beginning of each pre-season period (November).Mean power output (PO) for maximal efforts over 6s, 15s, 30s, 1min and 4min durations were recorded.During in-season games, total distance completed (TD), total high-speed running distance (HSR; >14.4 km.h -1 ) and sprint distance (>25.0 km.h -1 ) were collected via global positioning systems.Relationships between performance in the cycling Power Profile and game-running outputs were assessed utilising linear mixed models.Higher 6s PO was associated with higher sprint distance covered in games (7.4%, 13m, p<0.001).Higher 15s PO was associated with higher total HSR (6.1%, 147m, p<0.001), but lower TD (-1.4%, -183m, p<0.001).Higher 30s PO was associated with reduced sprint distance (-9.0%, -16m, p=0.04).Higher 1min PO was associated with reduced sprint distance (-20.3%, -37m, p<0.001) and HSR distance (-8.0%, -194m, p=0.02).Higher 4min PO was associated with higher TD (10.4%, 1338m, p<0.001) and HSR (30.1%, 872m, p<0.001) but had no effect on sprint distance.Players physiological profile characterised by the cycling Power Profile in pre-season appear to be associated with the running profile of players during in-season matches in elite Australian Football.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".