Home team's advantage reduced without crowd support after the COVID-19 outbreak

Historically, fan attendance at sporting events has always been considered an advantage for the home team. The level of fan attendance at soccer matches was evaluated as a decisive effect to modify the performance of the home teams in the most important soccer leagues around the world. We exploit the effects of the absence of spectators driven by COVID-19 pandemic mitigation policies. We find that the advantage of the home field, measured by the proportion of wins and also by the numbers of goals and points scored, is remarkable in many leagues. We performed comparative hypothesis tests with eight of the world's top soccer leagues, confirming a decrease in the probability of a home win, greatly affecting the matches´ outcomes. [ FROM AUTHOR] Copyright of Soccer & Society is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Hybrid Forecasting Method SVR-ESAR for Covid-19

We know that SARS-Cov2 produces the new COVID-19 disease, which is one of the most dangerous pandemics of modern times. This pandemic has critical health and economic consequences, and even the health services of the large, powerful nations may be saturated. Thus, forecasting the number of infected persons in any country is essential for controlling the situation. In the literature, different forecasting methods have been published, attempting to solve the problem. However, a simple and accurate forecasting method is required for its implementation in any part of the world. This paper presents a precise and straightforward forecasting method named SVR-ESAR (Support Vector regression hybridized with the classical Exponential smoothing and ARIMA). We applied this method to the infected time series in four scenarios, which we have taken for the Github repository: the Whole World, China, the US, and Mexico. We compared our results with those of the literature showing the proposed method has the best accuracy.