Sports analytics in practice with R / Ted Kwartler, Robert Baker

Includes bibliographical references and index.

Introduction to R -- Data Vizualization & Dashboards: Best practices -- Geospatial Data: Understanding changing baseball player behavior -- Machine Learning Basics: Modeling football draft patterns, pick number & clusters among athletes -- Logistic Regression: Explaining basketball wins & losses with coefficients -- Natual Language Processing: Understanding cricket fan topics & sentiment -- Linear Optimization: Programmatically seleting an optimal fantasy football lineup

Available to OhioLINK libraries

"R is an open-source, freely available programming language used throughout this book. R is a powerful and longstanding programming language developed more than 20 years ago. It is a derivative of the "S" programming language for statistics originating in the mid-nineties developed by AT&T and Lucent Technologies. Unlike other programming languages R is optimized specifically for statistics including but not limited to simulation, machine learning, visualizations and traditional statistical modeling (linear regression) as well as tests. Due to the open-source nature of R, many developers, academics, and enthusiasts have contributed to its development for their specific needs. As a result, the language is extensible meaning it can be easily used for various purposes. For example, through R markdown simple websites and presentations can be created. In another use case, R can be used for traditional linear modeling or machine learning and can draw upon various data types for analysis including audio files, digital images, text, numeric and various other data files and types. Thus, it is widely used and non-specialized other than to say R is an analysis language. This differs from other languages which specialize in web development like Ruby, or python which has extended its functionality to building applications not just analysis. In this textbook, the R language is applied specifically to sports contexts. Of course, the code in this book can be used to extend your understanding of sports analytics. It may give you insights to a particular sport or analytical aspect within the sport itself such as what statistics should be focused on to win a basketball game. However, learning the code in this book can also help open up a world of analytical capabilities beyond sports. One of the benefits of learning statistics, programming and various analysis methods with sports data is that the data is widely available, and outcomes are known. This means that your analysis, models and visualizations can be applied, and you can review the outcomes as you expand upon what is covered in this book. This differs from other programming and statistical examples which may resort to boring, synthetic data to illustrate an analytical result. Using sports data is realistic and can be future oriented, making the learning more challenging yet engaging. Modeling the survivors of the Titanic pales in comparison since you cannot change the historical outcome or save future cruise ship mates. Thus, modeling which team will win a match or which player is a good draft pick is a superior learning experience"-- Provided by publisher