Marica Manisera – författare

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 fast-evolving 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.

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 fast-evolving 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.

Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.Features:One of the first books to provide statistical and data mining methods for the growing field of analytics in basketballPresents tools for modelling graphs and figures to visualize the dataIncludes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test caseProvides the source code and data so readers can do their own analyses on NBA teams and players