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Full Description
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.
Contents
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 Some Advanced Methods
