Название: Basketball Data Science: With Applications in R Автор: Paola Zuccolotto and Marica Manisera
Издательство: Chapman and Hall/CRC
Серия: Data Science Series
Год: 2020
Страниц: 245
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Data Science is the discipline aimed at extracting knowledge from data in various forms, either structured or unstructured, in small or big amounts. It can be applied in a wide range of fields, from medical sciences to finance, from logistics to marketing. By its very nature, Data Science is multidisciplinary: it combines Statistics, Mathematics, Computer Science and operates in the domains of multivariate data analysis, data visualization, artificial intelligence, machine learning, data mining, and parallel computing. In fact, several skills and abilities are required for a Data Scientist: he needs to be familiar with Computer Science, as he has to handle complex databases in different formats from different sources and to use or develop codes to run algorithms; Statistics and Mathematics are then necessary, to the aim of extracting knowledge from data through more or less sophisticated methods and models; furthermore, a Data Scientist greatly benefits from some expertize in the application field he is working on, in order to ask the right research questions and translate them into hypotheses to be tested with statistical methods.
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.
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