Movement Demands in Australian Rules Football as Indicators of Muscle Damage
Bibliographic record
Abstract
The purpose of this study was to determine if there is an association between variables that describe movements in an Australian Rules football (ARF) game with muscle damage. Fourteen elite junior ARF players were monitored with a global positioning system (GPS) during a match, and muscle damage was estimated by determining creatine kinase (CK) 24 hours postmatch. The players were median split based on CK levels, into a high and low CK group, and the groups were compared with independent t-tests. The primary finding was that the group that experienced greater muscle damage (high CK group) generally covered significantly (p < 0.05) greater distances. This was the case for running speeds between 4 and 7 m·s(-1) and, with the exception of high acceleration, all intensities of acceleration and deceleration. The high, as compared with the low, CK group also produced a significantly greater (42%) "player load." All of these significant differences were accompanied by large effect sizes. Group-specific Pearson (r) correlations between CK level and GPS variables suggest that a certain volume of movement is required before the elicitation of a positive relationship beyond trivial or small. Correlations between CK and running speeds >4 m·s(-1) and moderate-high acceleration and deceleration were negative in the low CK (lesser volumes) group. With the exception of low-intensity acceleration/deceleration, the same relationships were positive and generally of a moderate-to-large magnitude in the high CK (greater volumes) group. It may be that a certain volume of movement is required for that movement to be strongly associated with CK levels. It was concluded that selected GPS variables obtained from ARF games can be used as indicators of muscle damage, and this information may be used to individualize recovery strategies after games.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".