COVID-19 Return to Sport: NFL Injury Prevalence Analysis
Bibliographic record
Abstract
Abstract 1. Purpose During the COVID 19 Pandemic, the NFL teams have been reported to have limited training sections in preparation for their games. This study compares the prevalence of injury during the 2018, 2019, and 2020 NFL seasons, with the aim to determine the potential causes of the differences in prevalence. 2. Method Official injury reports from each team were counted during the 17-week regular season of each year (2018, 2019, and 2020). The data was analyzed using an unpaired t-test to compare the injury prevalence between each of the three seasons. 3. Results The 2018 season produced a total of 1,561 injuries and a mean of 48.78 injuries per team. The 2019 season produced a total of 1,897 injuries and mean of 59.28 injuries per team, while the 2020 season produced a total of 2, 484 injuries and mean of 77.63 injuries per team. An unpaired t-test was performed using the data to compare the mean number of injuries per team of each of the seasons. Comparison of the 2020 season against the 2019 season showed a statistically significant difference (P=.0003). Comparison of the 2020 season to the 2018 season found a statistically significant difference (P=.0001). Comparison between the 2019 and the 2018 seasons found a statistically significant difference (P=.0314). Conclusion Although the 2019 and 2018 season showed a statistically significant difference (P=0.0314), this difference is not as astronomical when we compare the 2020 seasons vs 2019 and 2018 seasons (P=0.0003 and P=0.0001, respectively) ( Figure 2 ). The astronomical increase in injury prevalence in the 2020 season over the previous years does raise the possibility that there was reduced physiological adaption to stress, due to the limited amount of training embarked on. The limited amount of training was the result of closure of practice facilities during the COVID-19 pandemic. More physiological investigation involving players must be done at the professional and amateur levels to determine if there is a lack of physiological adaptation due to limited use of practice and training facilities during the COVID-19 pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".