Home advantage and crowd attendance: evidence from rugby during the Covid 19 pandemic

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS
Fernando Delbianco, Federico Fioravanti, F. Tohm'e
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引用次数: 8

Abstract

Abstract The COVID-19 pandemic forced almost all professional and amateur sports to be played without attending crowds. Thus, it induced a large-scale natural experiment on the impact of social pressure on decision making and behavior in sports fields. Using a data set of 1027 rugby union matches from 11 tournaments in 10 countries, we find that home teams have won less matches and their point difference decreased during the pandemic, shedding light on the impact of crowd attendance on the home advantage of sports teams.
主场优势和观众上座率:来自2019冠状病毒大流行期间橄榄球的证据
COVID-19大流行迫使几乎所有职业和业余体育活动都在没有人群的情况下进行。因此,它诱导了一个大规模的自然实验,研究社会压力对体育领域决策和行为的影响。使用来自10个国家11项锦标赛的1027场橄榄球联盟比赛的数据集,我们发现,在疫情期间,主队赢得的比赛较少,他们的分差也有所缩小,这揭示了观众上座率对运动队主场优势的影响。
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来源期刊
Journal of Quantitative Analysis in Sports
Journal of Quantitative Analysis in Sports SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
2.00
自引率
12.50%
发文量
15
期刊介绍: The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the growing field of sports analytics. Articles come from a wide variety of sports and diverse perspectives, and address topics such as game outcome models, measurement and evaluation of player performance, tournament structure, analysis of rules and adjudication, within-game strategy, analysis of sporting technologies, and player and team ranking methods. JQAS seeks to publish manuscripts that demonstrate original ways of approaching problems, develop cutting edge methods, and apply innovative thinking to solve difficult challenges in sports contexts. JQAS brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.
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