Home training recommendations for soccer players during the COVID-19 pandemic
Bibliographic record
Abstract
The new coronavirus (COVID-19) is an infectious disease caused by a newly discovered virus (SARS-CoV-2). This pandemic has a major impact on people's lives, and several governments ordered extended quarantine and requested social isolation to contain the spread of COVID-19 and flatten its contagion curve. Soccer practice was also severely affected by these pandemic effects, including the postponement of several championships, which involve large audiences. This process will be also dangerous for the players in the moment of returning to matches, mainly considering the abrupt spikes in the load that may occur in a very short term (from quarantine to competitive congested fixture periods). Here, we outline the benefits of home workouts using a multidimensional approach. First, we provide practical recommendations for physical, psychological, and tactical training. Next, we propose an example of a home training program spanning one weekly microcycle for soccer players, using load control based on the rating of perceived exertion. We highlighted that is crucial to make all these exercises fun and entertaining during the self-isolation period. In addition, coaches can adopt a video meeting with the players with the purpose to maintain the relationships and clear possible doubts about the workouts. The home training recommendations discussed and proposed in this article can and should be adjusted by the coaches according to their own ideas and athletes' access to equipment (e.g., treadmills, flywheel training, virtual reality). Finally, these recommendations do not apply to athletes showing any symptoms of COVID-19; in such case, self-isolation and complete rest are mandatory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".