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.
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.