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.
Basketball Data Science: With Applications in R
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.