Foundations & Setup

5 chapters

The Foundations section provides everything you need to get started with basketball analytics. Whether you're new to programming or experienced in other areas, these chapters will teach you the essential skills: setting up R and Python environments, understanding the basketball data ecosystem, mastering data manipulation with tidyverse and pandas, and creating your first visualizations.

Skills You'll Learn

R Programming Python Data Wrangling tidyverse pandas API Access

Chapters

P Beginner
The Analytics Revolution in Basketball

This introductory chapter explores how data and analytics have fundamentally transformed professional basketball over the past two decades. We examine the pioneering work of early basketball statisticians, trace the evolution from simple box score analysis to sophisticated player tracking systems, and investigate how modern NBA front offices leverage data science to gain competitive advantages.

6 sections ~25 min read
1 Beginner
Setting Up Your Analytics Environment

Before diving into basketball analytics, you need a solid foundation in the tools of the trade. This chapter guides you through setting up a complete analytics environment, including R and RStudio or Python and Jupyter notebooks. You will learn to install essential packages for data manipulation, visualization, and statistical modeling.

5 sections ~20 min read
2 Beginner
NBA Data Sources and APIs

Data lies at the heart of any analytics project, and basketball offers an extraordinarily rich ecosystem of data sources. This chapter provides a comprehensive survey of publicly available NBA data, from traditional box scores to advanced tracking metrics. You will learn to access data programmatically through APIs and web scraping.

5 sections ~25 min read
3 Beginner
Data Wrangling with tidyverse and pandas

Raw data rarely arrives in analysis-ready form. This chapter teaches essential data wrangling skills using the tidyverse in R and pandas in Python. Through practical examples with real NBA data, you will learn to filter, transform, aggregate, and join datasets, developing the foundational skills that enable sophisticated analysis.

6 sections ~30 min read
4 Beginner
Data Visualization Fundamentals

Effective visualization transforms complex statistical patterns into accessible insights. This chapter covers the principles of data visualization and their application to basketball analytics using ggplot2 in R and matplotlib/seaborn in Python. You will learn to create shot charts, player comparison plots, and other visualizations.

5 sections ~30 min read

Explore Other Topics

Metrics & Statistics
Tracking Data Analytics
Machine Learning
Data Visualization
Draft & Player Development
Betting & Fantasy Sports

Ready to Start Learning?

Begin with the first chapter in this topic

Start with The Analytics Revolution in Basketball