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.
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