An Introduction to Applied Sport Analytics offers a step-by-step path for applying data-driven methods in sports. The book begins with the evolution of sport analytics and foundational concepts like the Pythagorean theorem, correlation, and regression, then moves into hands-on instruction with industry tools such as Excel, SQL, R, Python, and Power BI. Readers learn how to explore data, evaluate performance, and make informed decisions across team operations, player valuation, and sport business strategy. The book features real-world examples, chapter exercises, and review questions and a dedicated section on data visualization walks readers through designing reports and dashboards using Power BI and Tableau.
The book also introduces the growing role of artificial intelligence in sport, showing how tools like machine learning and coding assistants can enhance analysis. A robust ancillary program also provides support to students with additional practice opportunities. With its practical focus and clear structure, this book is ideal for undergraduate and graduate courses in sport management, analytics, and business, as well as for professionals seeking to build essential skills in a data-driven sport industry.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Acknowledgment - p. vii 7. Section I. Laying the Groundwork - p. 1 8. Chapter 1. The Evolution of Sport Analytics - p. 3 9. Chapter 2. The Pythagorean Theorem of Sports - p. 11 10. Section II. Applying the Fundamentals - p. 17 11. Chapter 3. Applying the Pythagorean Theorem - p. 19 12. Chapter 4. Correlation - p. 37 13. Chapter 5. Simple Linear Regression - p. 47 14. Chapter 6. Multiple Linear Regression - p. 57 15. Chapter 7. Expected Value Metrics - p. 65 16. Section III. Visualizing the Game - p. 75 17. Chapter 8. Data Visualization and Communicating Insights - p. 77 18. Chapter 9. Introducing Microsoft Power BI - p. 85 19. Chapter 10. Building Visuals in Power BI - p. 99 20. Chapter 11. Analyzing Data With Power BI - p. 113 21. Chapter 12. Introducing Tableau - p. 129 22. Section IV. Tools of the Trade - p. 137 23. Chapter 13. SQL - p. 139 24. Chapter 14. Python - p. 145 25. Chapter 15. R - p. 151 26. Chapter 16. Artificial Intelligence - p. 159 27. Section V. Sharing Your Insights - p. 173 28. Chapter 17. Next Steps - p. 175 29. Index - p. 179
An Introduction to Applied Sport Analytics offers a step-by-step path for applying data-driven methods in sports. The book begins with the evolution of sport analytics and foundational concepts like the Pythagorean theorem, correlation, and regression, then moves into hands-on instruction with industry tools such as Excel, SQL, R, Python, and Power BI. Readers learn how to explore data, evaluate performance, and make informed decisions across team operations, player valuation, and sport business strategy. The book features real-world examples, chapter exercises, and review questions and a dedicated section on data visualization walks readers through designing reports and dashboards using Power BI and Tableau.
The book also introduces the growing role of artificial intelligence in sport, showing how tools like machine learning and coding assistants can enhance analysis. A robust ancillary program also provides support to students with additional practice opportunities. With its practical focus and clear structure, this book is ideal for undergraduate and graduate courses in sport management, analytics, and business, as well as for professionals seeking to build essential skills in a data-driven sport industry.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Acknowledgment - p. vii 7. Section I. Laying the Groundwork - p. 1 8. Chapter 1. The Evolution of Sport Analytics - p. 3 9. Chapter 2. The Pythagorean Theorem of Sports - p. 11 10. Section II. Applying the Fundamentals - p. 17 11. Chapter 3. Applying the Pythagorean Theorem - p. 19 12. Chapter 4. Correlation - p. 37 13. Chapter 5. Simple Linear Regression - p. 47 14. Chapter 6. Multiple Linear Regression - p. 57 15. Chapter 7. Expected Value Metrics - p. 65 16. Section III. Visualizing the Game - p. 75 17. Chapter 8. Data Visualization and Communicating Insights - p. 77 18. Chapter 9. Introducing Microsoft Power BI - p. 85 19. Chapter 10. Building Visuals in Power BI - p. 99 20. Chapter 11. Analyzing Data With Power BI - p. 113 21. Chapter 12. Introducing Tableau - p. 129 22. Section IV. Tools of the Trade - p. 137 23. Chapter 13. SQL - p. 139 24. Chapter 14. Python - p. 145 25. Chapter 15. R - p. 151 26. Chapter 16. Artificial Intelligence - p. 159 27. Section V. Sharing Your Insights - p. 173 28. Chapter 17. Next Steps - p. 175 29. Index - p. 179
