Estimating the change in soccer's home advantage during the Covid-19 pandemic using bivariate Poisson regression.

In wake of the Covid-19 pandemic, 2019-2020 soccer seasons across the world were postponed and eventually made up during the summer months of 2020. Researchers from a variety of disciplines jumped at the opportunity to compare the rescheduled games, played in front of empty stadia, to previous games, played in front of fans. To date, most of this post-Covid soccer research has used linear regression models, or versions thereof, to estimate potential changes to the home advantage. However, we argue that leveraging the Poisson distribution would be more appropriate and use simulations to show that bivariate Poisson regression (Karlis and Ntzoufras in J R Stat Soc Ser D Stat 52(3):381-393, 2003) reduces absolute bias when estimating the home advantage benefit in a single season of soccer games, relative to linear regression, by almost 85%. Next, with data from 17 professional soccer leagues, we extend bivariate Poisson models estimate the change in home advantage due to games being played without fans. In contrast to current research that suggests a drop in the home advantage, our findings are mixed; in some leagues, evidence points to a decrease, while in others, the home advantage may have risen. Altogether, this suggests a more complex causal mechanism for the impact of fans on sporting events.

Observed Versus Expected COVID-19 Infections Among National Football League Players During the 2020 Season.

BACKGROUND: The National Football League (NFL) and National Football League Players Association implemented a set of strict protocols for the 2020 season with the intent to mitigate COVID-19 risk among players and staff. In that timeframe, the league's 32 teams completed 256 regular season games and several thousand meetings and practices. In parallel, community cases of COVID-19 were highly prevalent. We assess the risk of holding a 2020 NFL season by comparing community and player COVID-19 infections. METHODS: We used county-level COVID-19 test data from each team to establish baseline distributions of infection rates expected to occur in a population similar in age and sex to NFL players. We used a binomial distribution to simulate expected infections in each community cohort and compared these findings with observed COVID-19 infections in players. RESULTS: Over a 5-month period (1 August 2020 to 2 January 2021), positive NFL player infections (n = 256) were 55.7% lower than expected when compared with simulations from NFL community cohorts. For 30 of 32 teams (94%), observed counts fell at or below expectation, including 28 teams (88%) for which rates were lower. Two teams fell above baseline expectation. CONCLUSIONS: The NFL/NFLPA protocols that governed team facilities, travel, gameday, and activities outside of the workplace were associated with lower infection rates among NFL players compared with the surrounding community. The NFL's 2020-2021 season are consistent with the hypothesis that robust testing and behavioral protocols support a safe return to sport and work.