
Statistics Playbook
Statistical Analysis with R on Real NBA Data
Manning Publications (Publisher)
Published on 9. February 2024
Book
Paperback/Softback
672 pages
978-1-63343-868-2 (ISBN)
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.
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.
Reviews / Votes
"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
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 234 mm
Width: 187 mm
Thickness: 29 mm
Weight
1134 gr
ISBN-13
978-1-63343-868-2 (9781633438682)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Persons
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.