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.
Author(s): Paola Zuccolotto; Marica Manisera
Publisher: CRC Press
Year: 2020
Language: English
Pages: 264
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
Authors
Part I: Getting Started Analyzing Basketball Data
Chapter 1: Introduction
1.1 WHAT IS DATA SCIENCE?
1.1.1 Knowledge representation
1.1.2 A tool for decisions and not a substitute for human intelligence
1.2 DATA SCIENCE IN BASKETBALL
1.3 HOW THE BOOK IS STRUCTURED
Chapter 2: Data and Basic Statistical Analyses
2.1 BASKETBALL DATA
2.2 BASIC STATISTICAL ANALYSES
2.2.1 Pace, Ratings, Four Factors
2.2.2 Bar-line plots
2.2.3 Radial plots
2.2.4 Scatter plots
2.2.5 Bubble plots
2.2.6 Variability analysis
2.2.7 Inequality analysis
2.2.8 Shot charts
Part II: Advanced Methods
Chapter 3: Discovering Patterns in Data
3.1 QUANTIFYING ASSOCIATIONS BETWEEN VARIABLES
3.1.1 Statistical dependence
3.1.2 Mean dependence
3.1.3 Correlation
3.2 ANALYZING PAIRWISE LINEAR CORRELATION AMONG VARIABLES
3.3 VISUALIZING SIMILARITIES AMONG INDIVIDUALS
3.4 ANALYZING NETWORK RELATIONSHIPS
3.5 ESTIMATING EVENT DENSITIES
3.5.1 Density with respect to a concurrent variable
3.5.2 Density in space
3.5.3 Joint density of two variables
3.6 FOCUS: SHOOTING UNDER HIGH-PRESSURE CONDITIONS
Chapter 4: Finding Groups in Data
4.1 CLUSTER ANALYSIS
4.2 K-MEANS CLUSTERING
4.2.1 k-means clustering of NBA teams
4.2.2 k-means clustering of Golden State Warriors’ shots
4.3 AGGLOMERATIVE HIERARCHICAL CLUSTERING
4.3.1 Hierarchical clustering of NBA players
4.4 FOCUS: NEW ROLES IN BASKETBALL
Chapter 5: Modeling Relationships in Data
5.1 LINEAR MODELS
5.1.1 Simple linear regression model
5.2 NONPARAMETRIC REGRESSION
5.2.1 Polynomial local regression
5.2.2 Gaussian kernel smoothing
5.2.2.1 Estimation of scoring probability
5.2.2.2 Estimation of expected points
5.3 FOCUS: SURFACE AREA DYNAMICS AND THEIR EFFECTS ON THE TEAM PERFORMANCE
Part III: Computational Insights
Chapter 6: The R Package BasketballAnalyzeR
6.1 INTRODUCTION
6.2 PREPARING DATA
6.3 CUSTOMIZING PLOTS
6.4 BUILDING INTERACTIVE GRAPHICS
6.5 OTHER R RESOURCES
Bibliography
Index
