The impact of COVID-19 on national hockey league players’ return to play
Bibliographic record
Abstract
Objective Evaluate the on-ice performance and return to play (RTP) rate following COVID-19 for National Hockey League (NHL) players during the 2020–21 season.Methods Players with COVID-19 during the abbreviated 2020–21 season were identified using publicly accessible online sources. Demographics and on-ice metrics were accessed using the NHL’s online statistics website. The length of time, rate of RTP, and games missed due to COVID-19 were analyzed. Primary outcomes included average time on ice (TOI) per game (TOI/G), average TOI per shift (TOI/S), and points per game (PPG) compared at different timepoints including pre- and post-COVID-19.Results A total of 73 players (47 forwards, 18 defensemen, 8 goalies) had a documented COVID-19 diagnosis during the abbreviated 2020–21 season. Players missed an average of 5.6 games (14.7 days) due to COVID-19. The post-COVID-19 RTP rate was 97.3%, including playoffs. No differences were found in TOI/G between the pre- (15.7 ± 3.9 min) and post-COVID-19 (15.8 ± 3.4 min, p = 0.874) or in the first (15.8 ± 4.0 min) and second week (15.9 ± 3.8 min, p = 0.925) returned. TOI/shift did not change from pre- (45.6 ± 5.3 sec) to post-COVID-19 (46.7 ± 4.6 sec, p = 0.035) or in first (46.2 ± 5.4 sec) and second week post-COVID-19 (46.2 ± 4.8 sec, p = .854). No differences were identified for PPG between career, pre-COVID-19, and post-COVID-19 (0.44 vs 0.38 vs 0.41; p = 0.274).Conclusion RTP post-COVID was markedly high for NHL players. While the effects of COVID-19 on specific physiological measures remains to be elucidated, this study found NHL players do not have reduced performance following COVID-19.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".