Unlock the power of data in basketball. This in-depth guide walks you through how to use the R programming language to analyze NBA statistics, evaluate player performance, and build advanced basketball insights using real data.
Whether you’re an aspiring sports analyst, a coach, or a data science student, this ebook equips you with the tools and techniques to extract real value from NBA data.
📊 What You’ll Learn:
✅ Load and clean NBA datasets from official and open sources
✅ Calculate advanced metrics: PER, TS%, USG%, AST/TO ratios, and more
✅ Visualize player performance across seasons or teams
✅ Compare athletes by cluster and role
✅ Apply regression and classification models for predictive insights
✅ Build dashboards to explore trends and key matchups
👥 Ideal For:
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Basketball analysts and fans who want to go deeper into stats
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Data scientists and students exploring sports analytics
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Coaches seeking data-informed decisions
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R users looking for real-world analytics projects
📦 What’s Included:
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62 pages of hands-on guidance with annotated R code
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Case studies using real NBA datasets
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Visualizations with ggplot2 and tidyverse
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Predictive modeling with caret and regression packages
This is your playbook to understanding basketball through the lens of data science—whether you’re courtside, courtside coding, or scouting the next MVP.
Michael R. –
A solid ebook to get started with NBA analytics using R. The content is clear and practical, with easy-to-follow explanations of both the code and basketball metrics. Great option if you’re into basketball and want to apply analytics to your own projects.