Impact of Sudden Rule Changes on Player Injuries and Performance: Insights from Australian Football
Bibliographic record
Abstract
This study investigated the effects of reduced quarter time due to COVID-19 pandemic rule changes, on running performance and injuries in Australian Football. Microsensor data for eight matches performed by the same 17 players were compared between the 2019 (standard) and 2020 (COVID-19) seasons using linear and generalised mixed models. Injury rates were assessed in 34 players across the full 2019 season, and 32 players across the full 2020 season. The total distance (ES = 1.28 [0.55 to 2.02]), high-speed (>18 km/h) (ES = 0.44 [-0.24 to 1.12]) and very highspeed (>24 km/h) (ES = 0.27 [-0.41 to 0.94]) distances, PlayerLoad™ (ES = 0.96 [0.25 to 1.67]), high-intensity efforts (ES = 0.48 [-0.20 to 1.16]), and accelerations (ES = 0.33 [-0.34 to 1.01]) were smaller (p ≤ 0.01) for the 2020 than the 2019 season. Expressed relative to playing time, distance (ES=-0.38 [-1.06 to 0.30]), PlayerLoad™ (ES = -0.27 [-0.94 to 0.41]), and acceleration efforts (ES = -0.50 [-1.18 to 0.18]) were greater (p < 0.05) for the 2020 than the 2019 season. No significant differences in maximum ball-in-play periods nor the difference between the 1st and 4th quarters were evident. Injury rates remained similar between 2019 (3.36 per game) and 2020 (3.55 per game). However, the proportion of injuries that led to lost time (missed games) was greater for the 2020 (38%) than 2019 season (24%). The changes in the rules had a profound impact on player performance and increased the likelihood of time loss injuries.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".