Teaching Statistics Using Baseball: Second Edition

Teaching Statistics Using Baseball is a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. Baseball is the most statistical of all sports since players are identified and evaluated by their corresponding hitting and pitching statistics. There is an active effort by people in the baseball community to learn more about baseball performance and strategy by the use of statistics. This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. Students often have difficulty learning statistics ideas since they are explained using examples that are foreign to the students. The idea of the book is to describe statistical thinking in a context (that is, baseball) that will be familiar and interesting to students. The book is organized using a same structure as most introductory statistics texts. There are chapters on the analysis on a single batch of data, followed with chapters on comparing batches of data and relationships. There are chapters on probability models and on statistical inference. The book can be used as the framework for a one-semester introductory statistics class focused on baseball or sports. This type of class has been taught at Bowling Green State University. It may be very suitable for a statistics class for students with sports-related majors, such as sports management or sports medicine. Alternately, the book can be used as a resource for instructors who wish to infuse their present course in probability or statistics with applications from baseball. The second edition of Teaching Statistics follows the same structure as the first edition, where the case studies and exercises have been replaced by modern players and teams, and the new types of baseball data from the PitchFX system and fangraphs.com are incorporated into the text. statistics Cover 1 Half title 2 Copyright 3 Title 4 Series 5 Contents 8 Preface to the First Edition 10 Preface to the Second Edition 12 1 An Introduction to Baseball Statistics 14 2 Exploring a Single Batch of Baseball Data 26 2.1 Looking at Teams' Offensive Statistics 26 2.2 A Tribute to Derek Jeter 30 2.3 A Tribute to Randy Johnson 33 2.4 Analyzing Baseball Attendance 36 2.5 Manager Statistics: the Use of Sacrifice Bunts 39 2.6 Exercises 41 3 Comparing Batches and Standardization 58 3.1 Albert Pujols and Manny Ramirez 58 3.2 Robin Roberts and Whitey Ford 63 3.3 Home Runs: A Comparison of Four Seasons 67 3.4 Slugging Percentages are Normal 70 3.5 Great Batting Averages 72 3.6 Exercises 74 4 Relationships Between Measurement Variables 86 4.1 Relationships in Team Offensive Statistics 86 4.2 Runs and Offensive Statistics 91 4.3 Most Valuable Hitting Statistics 93 4.4 A New Measure of Offensive Performance 99 4.5 How Important is a Run? 101 4.6 Baseball Players Regress to the Mean 104 4.7 Exercises 107 5 Introduction to Probability Using Tabletop Games 124 5.1 What is Chris Davis' Home Run Probability? 124 5.2 Big League Baseball 127 5.3 All-Star Baseball 129 5.4 Strat-O-Matic Baseball 132 5.5 Exercises 137 6 Probability Distributions and Baseball 152 6.1 The Binomial Distribution and Hits per Game 152 6.2 Modeling Runs Scored: Getting on Base 155 6.3 Modeling Runs Scored: Advancing the Runners to Home 157 6.4 Exercises 161 7 Introduction to Statistical Inference 168 7.1 Ability and Performance 168 7.2 Simulating a Batter's Performance if His Ability is Known 170 7.3 Learning About a Batter's Ability 172 7.4 Interval Estimates for Ability 174 7.5 Comparing Wade Boggs and Tony Gwynn 178 7.6 Exercises 181 8 Topics in Statistical Inference 188 8.1 Situational Hitting Statistics for Mike Trout 189 8.2 Observed Situational Effects for Many Players 191 8.3 Modeling On-Base Percentages for Many Players 194 8.4 Models for Situational Effects 198 8.5 Is Michael Brantley Streaky? 201 8.6 A Streaky Die 204 8.7 Exercises 206 9 Modeling Baseball Using a Markov Chain 224 9.1 Introduction to a Markov Chain 224 9.2 A Half-inning of Baseball as a Markov Chain 228 9.3 Useful Markov Chain Calculations 230 9.4 The Value of Different On-base Events 235 9.5 Answering Questions About Baseball Strategy 237 9.6 Exercises 238 A An Introduction to Baseball 246 A.1 The Game of Baseball 246 A.2 One Half-Inning of Baseball 247 A.3 The Boxscore: A Statistical Record of a Baseball Game 248 Bibliography 252 Index 254 Back cover 257 statistics;,baseball "A collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. Baseball is the most statistical of all sports, since players are identified and evaluated by their hitting and pitching statistics. There is an active effort by people in the baseball community to learn more about baseball performance and strategy by the use of statistics. This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. Students often have difficulty learning statistical ideas since they are explained using examples that are foreign to the students. The idea of the book is to describe statistical thinking in a context (that is, baseball) that will be familiar and interesting to students."--Provided by publisher Presents a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. The idea of the book is to describe statistical thinking in a context that will be familiar and interesting to students. Presents a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. The book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams.