Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports

One of the greatest changes in the sports world in the past 20 years has been the use of mathematical methods to analyze performances, recognize trends and patterns, and predict results. Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports, Second Edition provides a concise yet thorough introduction to the analytic and statistical methods that are useful in studying sports.

The book gives you all the tools necessary to answer key questions in sports analysis. It explains how to apply the methods to sports data and interpret the results, demonstrating that the analysis of sports data is often different from standard statistical analyses. The book integrates a large number of motivating sports examples throughout and offers guidance on computation and suggestions for further reading in each chapter.

Features

• Covers numerous statistical procedures for analyzing data based on sports results
• Presents fundamental methods for describing and summarizing data
• Describes aspects of probability theory and basic statistical concepts that are necessary to understand and deal with the randomness inherent in sports data
• Explains the statistical reasoning underlying the methods
• Illustrates the methods using real data drawn from a wide variety of sports
• Offers many of the datasets on the author’s website, enabling you to replicate the analyses or conduct related analyses

New to the Second Edition

• R code included for all calculations
• A new chapter discussing several more advanced methods, such as binary response models, random effects, multilevel models, spline methods, and principal components analysis, and more
• Exercises added to the end of each chapter, to enable use for courses and self-study

• Full solutions manual available to course instructors.

Thomas A. Severini is currently professor of statistics at Northwestern University. His research areas include likelihood inference, nonparametric and semiparametric methods, and applications to econometrics. He is also the author of Likelihood Methods in Statistics, Elements of Distribution Theory, and Introduction to Statistical Methods for Financial Models. He received his PhD in statistics from the University of Chicago. He is a fellow of the American Statistical Association.