{"product_id":"predictive-modelling-for-football-analytics-paperback","title":"Predictive Modelling for Football Analytics - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eLeonardo Egidi\u003c\/b\u003e (Author), \u003cb\u003eDimitris Karlis\u003c\/b\u003e (Author), \u003cb\u003eIoannis Ntzoufras\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003ePredictive Modelling for Football Analytics\u003c\/b\u003e\u003c\/i\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eDiscusses various modelling strategies and predictive tools related to football analytics\u003c\/li\u003e \u003cli\u003eIntroduces algorithms and computational tools to check the models, make predictions, and visualize the final results\u003c\/li\u003e \u003cli\u003eShowcases some guided examples through the use of the footBayes R package available on CRAN\u003c\/li\u003e \u003cli\u003eWalks the reader through the full pipeline: from data collection and preprocessing, through exploratory analysis and feature engineering, to advanced modelling techniques and evaluation\u003c\/li\u003e \u003cli\u003eBridges the gap between raw football data and actionable insights\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eIoannis Ntzoufras\u003c\/b\u003e 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).\u003c\/p\u003e\u003cp\u003eHe served as Head of the Department of Statistics at AUEB from 2020 to 2025. He was awarded the \u003cb\u003eLefkopoulion Award\u003c\/b\u003e by the Greek Statistical Institute in 2000 and is the author of the acclaimed book \u003ci\u003eBayesian Modeling Using WinBUGS\u003c\/i\u003e (Wiley), which received an honorable mention in Mathematics at the 2009 PROSE Awards. In addition, he has authored a Greek-language textbook titled \u003ci\u003eIntroduction to Programming and Statistical Data Analysis with R\u003c\/i\u003e, and he has served as the scientific editor for the Greek translations of two influential texts: Andy Field's \u003ci\u003eDiscovering Statistics with R\u003c\/i\u003e and Bernard Rosner's \u003ci\u003eFundamentals of Biostatistics\u003c\/i\u003e.\u003c\/p\u003e\u003cp\u003eProfessor Ntzoufras has served as an associate editor for several journals, including the \u003ci\u003eJournal of the Royal Statistical Society C\u003c\/i\u003e, \u003ci\u003eStatistics\u003c\/i\u003e, and the \u003ci\u003eJournal of Quantitative Analysis in Sports\u003c\/i\u003e. 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDimitris Karlis\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eLeonardo Egidi\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003eProfessor 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.\u003c\/p\u003e\u003cp\u003eIn 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.\u003c\/p\u003e\u003cp\u003eBeyond 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.\u003c\/p\u003e\u003cp\u003eHe is associate editor for the \u003ci\u003eJournal of Quantitative Analysis in Sports \u003c\/i\u003eand the creator and the maintainer of the CRAN R package \u003ci\u003efootBayes.\u003c\/i\u003e\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 246\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.55 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e November 07, 2025\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47337260089593,"sku":"9781032030630","price":110.14,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/ttir9W0Km9781032030630.webp?v=1769678611","url":"https:\/\/bookscloud.io\/products\/predictive-modelling-for-football-analytics-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}