Analytic Methods in Sports

One of the greatest changes in sports analytics in the past 25 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, Third Edition, provides a concise, yet thorough, introduction to the analytic and statistical methods that are useful in studying sports. Key Features: - New to the third edition is a chapter on applying mathematical and statistical methods to the analysis of daily fantasy sports - 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 - Discusses several more advanced methods, including logistic regression models, random forests, regression models with random effects, spline methods, principal components analysis, multidimensional scaling, quantile regression, and more - Illustrates the methods using real data drawn from a wide variety of sports - Offers many of the data sets on the author's website, enabling you to replicate the analyses or conduct related analyses - R code is included for all calculations - Exercises are given for each chapter, to enable use for courses and self-study This popular textbook is primarily designed to be used to teach an introductory course on statistics to undergraduate students using sports examples. Its practical focus on application rather than theory ensures students develop immediately applicable skills for the rapidly expanding field of sports analytics. It is a perfect reference for readers comfortable with mathematics seeking to enter the growing field of sports analytics without prior statistical training. Its concise yet thorough approach makes it equally suitable for self-study by sports enthusiasts, coaches, and industry professionals looking to leverage the power of data-driven decision making in competitive environments.

Thomas A. Severini is a Professor of Statistics and Data Science at Northwestern University and a fellow of the American Statistical Association and the Institute of Mathematical Statistics. He earned his PhD in Statistics from the University of Chicago.

1. Introduction. 2. Describing and Summarizing Sports Data. 3. Probability. 4. Statistical Methods. 5. Using Correlation to Detect Statistical Relationships. 6. Modeling Relationships Using Linear Regression. 7. Regression Models with Several Predictor Variables. 8. Further Topics in Regression Analysis. 9. Some Advanced Methods. 10. Applying Analytic Methods to Daily Fantasy Sports.