Statistics Slam Dunk: Statistical Analysis with R on Real NBA Data

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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

這本書是一本以分析職業籃球數據來學習統計學的書籍！在這本充滿活力的書中，你將使用R語言深入挖掘NBA比賽和球員數據的迷人世界，進一步提升你的探索性數據分析技能。

《統計灌籃》是一本引人入勝的R統計分析指南。每一章都包含一個完整的數據科學或統計學項目，深入研究NBA數據，揭示真實世界的體育見解。作者是一位從籃球運動員轉型為商業智能和分析領域領導者的人，你將獲得實際經驗，使用最好和最新的R套件和函數整理、處理、探索、測試、建模和分析數據。

在《統計灌籃》中，你將學習到一系列R編程技能，包括：
- 讀取和寫入數據
- 安裝和加載套件
- 轉換、整理和處理數據
- 應用最佳的探索性數據分析技術
- 創建引人入勝的可視化圖表
- 開發監督和非監督機器學習算法
- 執行假設檢驗，包括t檢驗和卡方獨立性檢驗
- 計算期望值、基尼系數、z分數和其他指標

如果你想從其他語言轉換到R，或者想用tidyverse函數替換基本R，這本書是完美的培訓教材。它不僅是一本初學者指南，還教授了統計學和數據科學方法，具有大量的應用案例。就像在現實世界中一樣，你在《統計灌籃》中不會得到乾淨的預包裝數據集。你將面臨整理混亂數據的挑戰，鍛煉你成為任何數據團隊的明星球員所需的技能。

本書的前言由Thomas W. Miller撰寫。購買印刷版書籍還包括Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術：
《統計灌籃》是一本與眾不同的數據科學手冊。每一章都是一個完整的、獨立的統計學或數據科學項目，供你進行實際操作——從導入數據、整理數據、測試數據、可視化數據到建模數據。在整本書中，你將專注於NBA數據集和R語言，應用最佳的統計學技術，揭示有趣而迷人的關於NBA的真相。

關於本書：
有目的地輸掉籃球比賽是一種理性策略嗎？哪些努力統計數據對勝負有影響？花更多錢在球員薪水上是否能轉化為勝場記錄？你將回答所有這些問題，還有更多。此外，本書的300個圖表和圖表展示了R的可視化能力，包括帕累托圖、桑基圖、克利夫蘭點圖和樹狀圖。

內容簡介：
- 數據轉換、整理和處理
- 應用最佳的探索性數據分析技術
- 開發監督和非監督機器學習算法
- 執行假設檢驗和效應大小檢驗

關於讀者：
適合具備基本統計學知識的讀者。不需要高級R或籃球知識。

關於作者：
Gary Sutton是一位前籃球運動員，曾在多個行業建立和領導高效的商業智能和分析組織。

目錄：
1. 入門
2. 數據探索
3. 分割分析
4. 限制優化
5. 迴歸模型
6. 更多數據整理和可視化
7. t檢驗和效應大小檢驗
8. 最佳停止
9. 卡方檢驗和更多效應大小檢驗
10. 更多ggplot2技巧
11. K-means聚類
12. 不平等計算和繪圖
13. 更多基尼系數和洛倫茨曲線
14. 中級和高級建模
15. Lin

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.

Gary Sutton是一家領先金融服務公司的副總裁。他在多個行業建立並領導了高效的商業智能和分析組織，其中R是首選的預測建模、統計分析和其他量化洞察的程式語言。Gary在南加州大學獲得了學士學位，喬治華盛頓大學獲得了碩士學位，並在西北大學獲得了第二個數據科學碩士學位。