Dimitris Karlis – författare

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 FeaturesDiscusses various modelling strategies and predictive tools related to football analyticsIntroduces algorithms and computational tools to check the models, make predictions, and visualize the final resultsShowcases some guided examples through the use of the footBayes R package available on CRANWalks the reader through the full pipeline: from data collection and preprocessing, through exploratory analysis and feature engineering, to advanced modelling techniques and evaluationBridges the gap between raw football data and actionable insightsThis 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.

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 FeaturesDiscusses various modelling strategies and predictive tools related to football analyticsIntroduces algorithms and computational tools to check the models, make predictions, and visualize the final resultsShowcases some guided examples through the use of the footBayes R package available on CRANWalks the reader through the full pipeline: from data collection and preprocessing, through exploratory analysis and feature engineering, to advanced modelling techniques and evaluationBridges the gap between raw football data and actionable insightsThis 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.

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.