Statistics Slam Dunk, Video Edition

Video description

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

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.

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.

Quotes
In this journey of exploration, every computer scientist will find a valuable ally in understanding the language of data.
- Kim Lokøy, areo

Transcends other R titles by revealing the hidden narratives that lie within the numbers.
- Christian Sutton, Shell International Exploration and Production

Seamlessly blending theory and practical insights, this book serves as an indispensable guide for those venturing into the field of data analytics.
- Juan Delgado, Sodexo BRS

Table of contents

  1. Chapter 1. Getting started
  2. Chapter 1. Why R?
  3. Chapter 1. How this book works
  4. Chapter 1. Summary
  5. Chapter 2. Exploring data
  6. Chapter 2. Importing data
  7. Chapter 2. Wrangling data
  8. Chapter 2. Variable breakdown
  9. Chapter 2. Exploratory data analysis
  10. Chapter 2. Writing data
  11. Chapter 2. Summary
  12. Chapter 3. Segmentation analysis
  13. Chapter 3. Loading packages
  14. Chapter 3. Importing and viewing data
  15. Chapter 3. Creating another derived variable
  16. Chapter 3. Visualizing means and medians
  17. Chapter 3. Preliminary conclusions
  18. Chapter 3. Sankey diagram
  19. Chapter 3. Expected value analysis
  20. Chapter 3. Hierarchical clustering
  21. Chapter 3. Summary
  22. Chapter 4. Constrained optimization
  23. Chapter 4. Loading packages
  24. Chapter 4. Importing data
  25. Chapter 4. Knowing the data
  26. Chapter 4. Visualizing the data
  27. Chapter 4. Constrained optimization setup
  28. Chapter 4. Constrained optimization construction
  29. Chapter 4. Results
  30. Chapter 4. Summary
  31. Chapter 5. Regression models
  32. Chapter 5. Importing data
  33. Chapter 5. Knowing the data
  34. Chapter 5. Identifying outliers
  35. Chapter 5. Checking for normality
  36. Chapter 5. Visualizing and testing correlations
  37. Chapter 5. Multiple linear regression
  38. Chapter 5. Regression tree
  39. Chapter 5. Summary
  40. Chapter 6. More wrangling and visualizing data
  41. Chapter 6. Importing data
  42. Chapter 6. Wrangling data
  43. Chapter 6. Analysis
  44. Chapter 6. Summary
  45. Chapter 7. T-testing and effect size testing
  46. Chapter 7. Importing data
  47. Chapter 7. Wrangling data
  48. Chapter 7. Analysis on 2018-19 data
  49. Chapter 7. Analysis on 2019-20 data
  50. Chapter 7. Summary
  51. Chapter 8. Optimal stopping
  52. Chapter 8. Importing images
  53. Chapter 8. Importing and viewing data
  54. Chapter 8. Exploring and wrangling data
  55. Chapter 8. Analysis
  56. Chapter 8. Summary
  57. Chapter 9. Chi-square testing and more effect size testing
  58. Chapter 9. Importing data
  59. Chapter 9. Wrangling data
  60. Chapter 9. Computing permutations
  61. Chapter 9. Visualizing results
  62. Chapter 9. Statistical test of significance
  63. Chapter 9. Effect size testing
  64. Chapter 9. Summary
  65. Chapter 10. Doing more with ggplot2
  66. Chapter 10. Importing and viewing data
  67. Chapter 10. Salaries and salary cap analysis
  68. Chapter 10. Analysis
  69. Chapter 10. Summary
  70. Chapter 11. K-means clustering
  71. Chapter 11. Importing data
  72. Chapter 11. A primer on standard deviations and z-scores
  73. Chapter 11. Analysis
  74. Chapter 11. K-means clustering
  75. Chapter 11. Summary
  76. Chapter 12. Computing and plotting inequality
  77. Chapter 12. Loading packages
  78. Chapter 12. Importing and viewing data
  79. Chapter 12. Wrangling data
  80. Chapter 12. Gini coefficients
  81. Chapter 12. Lorenz curves
  82. Chapter 12. Salary inequality and championships
  83. Chapter 12. Salary inequality and wins and losses
  84. Chapter 12. Gini coefficient bands versus winning percentage
  85. Chapter 12. Summary
  86. Chapter 13. More with Gini coefficients and Lorenz curves
  87. Chapter 13. Importing and viewing data
  88. Chapter 13. Wrangling data
  89. Chapter 13. Gini coefficients
  90. Chapter 13. Lorenz curves
  91. Chapter 13. For loops
  92. Chapter 13. User-defined functions
  93. Chapter 13. Win share inequality and championships
  94. Chapter 13. Win share inequality and wins and losses
  95. Chapter 13. Gini coefficient bands versus winning percentage
  96. Chapter 13. Summary
  97. Chapter 14. Intermediate and advanced modeling
  98. Chapter 14. Importing and wrangling data
  99. Chapter 14. Exploring data
  100. Chapter 14. Correlations
  101. Chapter 14. Analysis of variance models
  102. Chapter 14. Logistic regressions
  103. Chapter 14. Paired data before and after
  104. Chapter 14. Summary
  105. Chapter 15. The Lindy effect
  106. Chapter 15. Importing and viewing data
  107. Chapter 15. Visualizing data
  108. Chapter 15. Pareto charts
  109. Chapter 15. Summary
  110. Chapter 16. Randomness versus causality
  111. Chapter 16. Importing and wrangling data
  112. Chapter 16. Rule of succession and the hot hand
  113. Chapter 16. Player-level analysis
  114. Chapter 16. League-wide analysis
  115. Chapter 16. Summary
  116. Chapter 17. Collective intelligence
  117. Chapter 17. Importing data
  118. Chapter 17. Wrangling data
  119. Chapter 17. Automated exploratory data analysis
  120. Chapter 17. Results
  121. Chapter 17. Summary
  122. Chapter 18. Statistical dispersion methods
  123. Chapter 18. Importing data
  124. Chapter 18. Exploring and wrangling data
  125. Chapter 18. Measures of statistical dispersion and intra-season parity
  126. Chapter 18. Churn and inter-season parity
  127. Chapter 18. Summary
  128. Chapter 19. Data standardization
  129. Chapter 19. Importing and viewing data
  130. Chapter 19. Wrangling data
  131. Chapter 19. Standardizing data
  132. Chapter 19. Summary
  133. Chapter 20. Finishing up
  134. Chapter 20. Significance testing
  135. Chapter 20. Effect size testing
  136. Chapter 20. Modeling
  137. Chapter 20. Operations research
  138. Chapter 20. Probability
  139. Chapter 20. Statistical dispersion
  140. Chapter 20. Standardization
  141. Chapter 20. Summary statistics and visualization

Product information

  • Title: Statistics Slam Dunk, Video Edition
  • Author(s): Gary Sutton
  • Release date: January 2024
  • Publisher(s): Manning Publications
  • ISBN: None