Learn statistics by analyzing professional basketball data! In this action-packed book, you’ll build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language.

Statistics Slam Dunk is an engaging how-to guide for statistical analysis with R. Each chapter contains an end-to-end data science or statistics project delving into NBA data and revealing real-world sporting insights. Written by a former basketball player turned business intelligence and analytics leader, you’ll get practical experience tidying, wrangling, exploring, testing, modeling, and otherwise analyzing data with the best and latest R packages and functions.

In

Statistics Slam Dunk

you’ll develop a toolbox of R programming skills including:

Reading and writing data

Installing and loading packages

Transforming, tidying, and wrangling data

Applying best-in-class exploratory data analysis techniques

Creating compelling visualizations

Developing supervised and unsupervised machine learning algorithms

Executing hypothesis tests, including t-tests and chi-square tests for independence

Computing expected values, Gini coefficients, z-scores, and other measures

If you’re looking to switch to R from another language, or trade base R for tidyverse functions, this book is the perfect training coach. Much more than a beginner’s guide, it teaches statistics and data science methods that have tons of use cases. And just like in the real world, you’ll get no clean pre-packaged data sets in Statistics Slam Dunk. You’ll take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team.

Foreword by Thomas W. Miller.

About the technology

Statistics Slam Dunk

is a data science manual with a difference. Each chapter is a complete, self-contained statistics or data science project for you to work through—from importing data, to wrangling it, testing it, visualizing it, and modeling it. Throughout the book, you’ll work exclusively with NBA data sets and the R language, applying best-in-class statistics techniques to reveal fun and fascinating truths about the NBA.

About the book

Is losing basketball games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Does spending more on player salaries translate into a winning record? You’ll answer all these questions and more. Plus, R’s visualization capabilities shine through in the book’s 300 plots and charts, including Pareto charts, Sankey diagrams, Cleveland dot plots, and dendrograms.

What's inside

Transforming, tidying, and wrangling data

Applying best-in-class exploratory data analysis techniques

Developing supervised and unsupervised machine learning algorithms

Executing hypothesis tests and effect size tests

About the reader

For readers who know basic statistics. No advanced knowledge of R—or basketball—required.

About the author

Gary Sutton

is a former basketball player who has built and led high-performing business intelligence and analytics organizations across multiple verticals.

Table of Contents

1 Getting started

2 Exploring data

3 Segmentation analysis

4 Constrained optimization

5 Regression models

6 More wrangling and visualizing data

7 T-testing and effect size testing

8 Optimal stopping

9 Chi-square testing and more effect size testing

10 Doing more with ggplot2

11 K-means clustering

12 Computing and plotting inequality

13 More with Gini coefficients and Lorenz curves

14 Intermediate and advanced modeling

15 The Lindy effect

16 Randomness versus causality

17 Collective intelligence

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