Predictive Modelling for Football Analytics discusses the most well-known models and the main computational tools for the football analytics domain. It further introduces the footBayes R package that accompanies the reader through all the examples proposed in the book. It aims to be both a practical guide and a theoretical foundation for students, data scientists, sports analysts, and football professionals who wish to understand and apply predictive modelling in a football context.
Key Features
Discusses various modelling strategies and predictive tools related to football analytics
Introduces algorithms and computational tools to check the models, make predictions, and visualize the final results
Showcases some guided examples through the use of the footBayes R package available on CRAN
Walks the reader through the full pipeline: from data collection and preprocessing, through exploratory analysis and feature engineering, to advanced modelling techniques and evaluation
Bridges the gap between raw football data and actionable insights
This text is primarily for senior undergraduates, graduate students, and academic researchers in the fields of mathematics, statistics, and computer science willing to learn about the football analytics domain. Although technical in nature, the book is designed to be accessible to readers with a background in statistics, programming, or a strong interest in sports analytics. It is well-suited for use in academic courses on sports analytics, data science projects, or professional development within football clubs, agencies, and media organizations.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Professional Practice & Development
Illustrationen
18 s/w Abbildungen, 18 s/w Zeichnungen
18 Line drawings, black and white; 18 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
Gewicht
ISBN-13
978-1-032-03064-7 (9781032030647)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Ioannis Ntzoufras is a distinguished statistician and academic, widely recognized for his contributions to statistical modeling, Bayesian analysis, and sports analytics. He is a full professor in the Department of Statistics at the Athens University of Economics and Business (AUEB). He is particularly known for his work in Bayesian statistics, including the development and application of Markov Chain Monte Carlo (MCMC) methods and Bayesian variable selection techniques. His research also addresses computational strategies and prior formulation for Objective Bayesian model comparison. These methodologies have been applied across various domains, with a strong emphasis on sports analytics-especially in football (soccer).
He served as Head of the Department of Statistics at AUEB from 2020 to 2025. He was awarded the Lefkopoulion Award by the Greek Statistical Institute in 2000 and is the author of the acclaimed book Bayesian Modeling Using WinBUGS (Wiley), which received an honorable mention in Mathematics at the 2009 PROSE Awards. In addition, he has authored a Greek-language textbook titled Introduction to Programming and Statistical Data Analysis with R, and he has served as the scientific editor for the Greek translations of two influential texts: Andy Field's Discovering Statistics with R and Bernard Rosner's Fundamentals of Biostatistics.
Professor Ntzoufras has served as an associate editor for several journals, including the Journal of the Royal Statistical Society C, Statistics, and the Journal of Quantitative Analysis in Sports. As of April 2025, Professor Ntzoufras has authored 76 peer-reviewed journal articles, accumulating over 6,100 citations and an h-index of 29 on Google Scholar. He remains actively engaged in research, with current projects focusing on Bayesian methodology, variable selection, applied statistics, biostatistics, psychometrics, and sports analytics. His contributions to sports analytics have led to the creation of models that enhance performance prediction and strategic planning in football, basketball, and volleyball.
Dimitris Karlis is a distinguished statistician and academic widely recognized for his contributions to the fields of statistical modeling, discrete valued time series analysis, model-based clustering and sports analytics. He is full professor at the Athens University of Economics and Business, where his research focuses on the development and application of advanced statistical methods for various problems and disciplines. He has served as director of the MSc in Statistics program at AUEB, (2019-today), Director of the Laboratory of Computational and Bayesian Statistics (2017 -today) and vice-President of the Research Committee of AUEB (2019 -today). Professor Karlis has made significant contributions to the statistical analysis of sports data, especially in football (soccer), basketball, handball and other team sports. His work on modeling match outcomes, player performance, and team strategies has had a substantial impact on both academic research and practical applications in the sports industry. He is known for pioneering methods such as the use of generalized linear models and mixed-effects models for analyzing sports data as well as the development of innovative model for various sports.
Leonardo Egidi is a distinguished statistician and academic, recognized for his significant contributions to the fields of Bayesian statistics, sports analytics, and statistical modeling. He is assistant professor of statistics at University of Trieste, where his research primarily focuses on applying advanced statistical methods to real-world problems, with a particular emphasis on sports data analysis, genomics, and predictive modeling.
Professor Egidi is well-known for his work in theoretical Bayesian inference and in football analytics, particularly in the development of models to predict match outcomes, assess player performance, and optimize team strategies. His research includes the application of machine learning algorithms and Bayesian methods to enhance the accuracy of predictions and provide insights into various aspects of the game. He has published extensively in leading academic journals and has collaborated with both academic researchers and sports organizations to advance the field of sports data science.
In addition to his work on football, Professor Egidi has also contributed to statistical methodology in other domains, including economics, biostatistics, and social sciences. His expertise lies in the integration of complex data structures, such as hierarchical models, into practical solutions that can drive decision-making processes.
Beyond his research, Leonardo Egidi is actively involved in teaching and mentoring, fostering the next generation of statisticians and data scientists. His work has made a substantial impact on both the academic community and the sports industry, cementing his reputation as a leading figure in the application of statistics to sports analytics.
He is associate editor for the Journal of Quantitative Analysis in Sports and the creator and the maintainer of the CRAN R package footBayes.
1. A short introduction to football analytics. 2. Methods, Algorithms and Computational Tools. 3. Tournament and game prediction via simulation. 4. Implementation of basic models in R via footBayes. 5. Additional statistical models for the scores. 6. Modelling international matches: the Euro and World Cups experience. 7. Compare statistical models' performance with the bookmakers.
