Abstract While the use of expected goals (xG) as a metric for assessing soccer performance is increasingly prevalent, the uncertainty associated with their estimates is often overlooked. This work bridges this gap by providing easy-to-implement methods for uncertainty quantification in xG estimates derived from Bayesian models. Based on a convenient posterior approximation, we devise an online prior-to-posterior update scheme, aligning with the typical in-season model training in soccer. Additionally, we present a novel framework to assess and compare the performance dynamics of two teams during a match, while accounting for evolving match scores. Our approach is well-suited for graphical representation and improves interpretability. We validate the accuracy of our methods through simulations, and provide a real-world illustration using data from the Italian Serie A league.

Bernardo Nipoti

Lorenzo Schiavon

Journal of Quantitative Analysis in Sports

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.

The Journal of Quantitative Analysis in Sports (JQAS) is the official journal of the American Statistical Association, focusing on the quantitative analysis of professional and amateur sports, including collegiate and Olympic competitions. As the field of sports analytics continues to grow, there is an increasing demand for novel methods to analyze and understand data. JQAS is dedicated to publishing timely, high-quality peer-reviewed research that covers a wide variety of sports and diverse perspectives. Topics addressed include 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 approaches to problems, develop cutting-edge methods, and apply innovative thinking to solve complex challenges in sports contexts. The journal brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.

《Journal of Quantitative Analysis in Sports》（JQAS）是美国统计协会的官方期刊，专注于专业和业余体育的定量分析，包括大学和奥林匹克比赛。随着体育分析领域的不断发展，对新方法的需求日益增加，JQAS致力于发表及时、高质量的同行评审研究。期刊涵盖多种体育项目和不同视角，探讨的主题包括比赛结果模型、球员表现的测量与评估、比赛结构、规则与裁决分析、比赛策略、体育技术分析以及球员和团队排名方法。JQAS希望发表展示原创性问题解决方法的手稿，开发前沿方法，并运用创新思维解决体育领域中的复杂挑战。JQAS汇聚了来自统计学、运筹学、机器学习、科学计算、计量经济学和体育管理等多个学科的研究者。

Expected goals under a Bayesian viewpoint: uncertainty quantification and online learning

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Journal Info

Category	Quartile
SOCIAL SCIENCES, MATHEMATICAL METHODS	3