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Full Description
Advanced Basketball Data Science: With Applications in R is the essential next step for anyone looking to push basketball analytics beyond standard metrics. Expanding on the foundation of Basketball Data Science (2020), this book takes readers into the fastevolving world of advanced statistical modeling, machine learning, and modern computational techniques applied to the game.
From lineup optimization and clutch-performance analysis to player tracking, pose estimation, and ball-trajectory modeling, the book shows how cutting-edge data can reveal the hidden patterns that shape decision-making on and off the court. Readers learn not only what to analyze, but how to build robust, reproducible workflows using real data, fully executable R code, and a structured project environment.
Designed for analysts, coaches, researchers, and graduate students, this volume translates complex concepts into actionable tools that can immediately elevate scouting, strategy, and performance evaluation. Whether you aim to understand spatial tendencies, quantify player impact, or model scoring probabilities with machine learning, this book provides the framework to do so with clarity and confidence.
Advanced Basketball Data Science is where rigorous methodology meets practical basketball insight, an indispensable resource for anyone committed to understanding the game through the power of data.
• Combines advanced statistical methods, machine learning, and computer-vision techniques to provide a unified and cutting-edge framework for basketball analytics.
• Offers fully reproducible workflows - complete with datasets, R code, and additional functions - which enable readers to directly apply and extend all analyses.
• Integrates real-world case studies from diverse data sources (play-by-play, tracking, pose estimation, ball trajectories) to demonstrate how rigorous methodology translates into actionable basketball insights.
Contents
Foreword Preface 1 Getting started: overview and supporting materials I Analyzing and comparing game splits 2 Beyond individual skills 3 Drilling down on clutch splits: measuring performance when it matters most 4 The race to the finish: exploring the relationship between season segments and final rankings II Decoding motion 5 Understanding players' spatial dynamics 6 Athletic motion kinematics analysis 7 Tracking and analyzing ball trajectories III Spatial performance analysis 8 Basketball performance maps based on court segmentation 9 Scoring probability maps via Machine Learning algorithms Bibliography R packages Index
