Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player’s shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.
· One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball.
· Presents tools for modelling graphs and figures to visualize the data.
· Includes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test case.
· Provides the source code and data so readers can do their own analyses on NBA teams and players.
Table of Contents
1. Introduction. 2. Finding Groups in Data. 3. Finding Structures in Data with Machine Learning. 4. Modelling Relationships in Basketball. 5. Concluding Remarks and Future Perspectives.
Paola Zuccolotto and Marica Manisera are, respectively, Full and Associate Professor of Statistics at the University of Brescia. Paola Zuccolotto is the scientific director of the Big & Open Data Innovation Laboratory (BODaI-Lab), where she coordinates, together with Marica Manisera, the international project Big Data Analytics in Sports (BDsports).
They carry out scientific research activity in the field of Statistical Science, both with a methodological and applied approach. They authored/co-authored several scientific articles in international journals and books, participated to many national and international conferences, also as organizers of specialized sessions, often on the topic of Sports Analytics. They regularly act as scientific reviewers for the world’s most prestigious journals in the field of Statistics.
Paola Zuccolotto is a member of the Editorial Advisory Board of the Journal of Sports Sciences, while Marica Manisera is Associate Editor of the Journal of Sports Analytics; both of them are guest co-editors of special issues of international journals on Statistics in Sports. The International Statistical Institute (ISI) delegated them the task of revitalizing its Special Interest Group (SIG) on Sports Statistics. Marica Manisera is the Chair of the renewed ISI SIG on Sport.
Both of them teach undergraduate and graduate courses in the field of Statistics and are responsible for the scientific area dedicated to Sport Analytics at the PhD “Analytics for Economics and Management” of the University of Brescia. They also teach courses and seminars on Sports Analytics in University Masters on Sports Engineering and specialized training projects devoted to people operating in the sports world. They supervise students’ internships, final reports and master’s theses on the subject of Statistics, often with applications to sport data. They also work in collaboration with high-school teachers, creating experimental educational projects to bring students closer to quantitative subjects through Sport Analytics.
"This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball."
–Virgilio Gomez Rubio
From the foreword: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."
–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs
"Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community."
–Patrick Mair, Harvard University
"The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans."
–Jason Osborne, North Carolina State University
"My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport"
–Jack Davis, Simon Fraser University
“The real strength of this book is that it is meant to be hands-on. As part of the text, the authors provide access to a custom-built package in R, along with an excellent pre-prepared data set (one full season’s worth of NBA box score and play-by-play data). The authors then guide the reader through many examples of building graphs and tables using their R package and data. The graphs are often intricate and visually detailed, but the text shows how to make them quickly, giving detailed instructions. I imagine that a reader looking to get into basketball analysis could find this book very exciting, because it provides a quick and easy entry point into conducting sophisticated analyses and making visually arresting graphs and figures. A reader can easily follow along and replicate everything that is done in the book. Or, what is more likely, the reader can skim through the text until they come to a plot that looks particularly cool, and then by reading the surrounding section they can quickly learn how to do such an analysis for themselves.”
–Brian Skinner, MIT