Statistics Playbook: Statistical Analysis with R on Real NBA Data
Author(s): Trey Grainger (Author), Doug Turnbull (Author), Max Irwin (Author)
- Publisher: Manning Publications
- Publication Date: 9 Feb. 2024
- Edition: 1st
- Language: English
- Print length: 672 pages
- ISBN-10: 1633438686
- ISBN-13: 9781633438682
Book Description
Learn statistics by analysing professional basketball data!
Statistics Slam Dunk is an action-packed book that will help you build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language. This textbook will upgrade your R data science skills by taking on practical analysis challenges based on NBA game and player data.
You will take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team. And just like in the real world, you will get no clean pre-packaged datasets in this book.
You will develop a toolbox of R data 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
- Execute hypothesis tests, including t-tests and chi-square tests for independence
- Compute expected values, Gini coefficients, and z-scores
Is losing games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Each chapter in this one-of-a-kind guide uses new data science techniques to reveal interesting insights like these.
About the technology
Amazing insights are hiding in raw data, and statistical analysis with R can help reveal them! R was built for data, and it supports modelling and statistical techniques including regression and classification models, time series forecasts, and clustering algorithms. And when you want to see your results, R’s visualisations are stunning, with best-in-class plots and charts.
Editorial Reviews
Review
“An excellent way to learn exploratory data analysis and statistical analysis with R and sports statistics from the NBA.”
Bob Quintus
“This book is very impressive. Different from other similar books, this book integrates the technology of R language through storytelling.”
Chen Sun
“A great example of using R and applying it to a machine learning problem.”
John Williams
“Very interesting subject matter. The author’s enthusiasm for it really shows.”
Lachman Dhalliwal
“For users looking to get experience with real world datasets, this book will provide a great methodological approach.”
Eli Mayost
From the Back Cover
Statistics Slam Dunk: Statistical analysis with R on real NBA data is an interesting and engaging how-to guide for statistical analysis using R. It is packed with practical statistical techniques, each demonstrated using real-world data taken from NBA games. In each chapter, you will discover a new (and sometimes surprising!) insight into basketball, with careful step-by-step instructions on how to generate those revelations. You will get practical experience cleaning, manipulating, exploring, testing, and otherwise analysing data with base R functions and useful R packages. R’s visualisation capabilities shine through in the book’s 300 visualizations, and almost 30 plots and charts including Pareto charts and Sankey diagrams. Much more than a beginner’s guide, this book explores advanced analytics techniques and data wrangling packages. You will find yourself returning again and again to use this book as a handy reference!
About the reader
Requires a beginning knowledge of basic statistics concepts. No advanced knowledge of statistics, machine learning, R – or basketball – required.
About the Author
Gary Sutton is a vice president for a leading financial services company. He has built and led high-performing business intelligence and analytics organizations across multiple verticals, where R was the preferred programming language for predictive modelling, statistical analyses, and other quantitative insights. Gary earned his Undergraduate Degree from the University of Southern California, a Masters from George Washington University, and a second Masters in Data Science, from Northwestern University.
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