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 MBA 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.
"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 forward: "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 Asssistant 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
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.
Reflecting the interdisciplinary nature of the field, this new data science book series brings together researchers, practitioners, and instructors from statistics, computer science, machine learning, and analytics. The series will publish cutting-edge research, industry applications, and textbooks in data science.
* Presents the latest research and applications in the field, including new statistical and computational techniques
* Covers a broad range of interdisciplinary topics
* Provides guidance on the use of software for data science, including R, Python, and Julia
* Includes both introductory and advanced material for students and professionals
* Presents concepts while assuming minimal theoretical background
The scope of the series is broad, including titles in machine learning, pattern recognition, artificial intelligence, predictive analytics, business analytics, visualization, programming, software, learning analytics, data collection and wrangling, interactive graphics, reproducible research, and more. The inclusion of examples, applications, and code implementation is essential.