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.
Features:
· 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.