Curve ball : baseball, statistics, and the role of chance in the game / Jim Albert, Jay Bennett.

Format:

Book

Author/Creator:

Albert, Jim, 1953-

Contributor:

Bennett, Jay.

Language:

English

Subjects (All):

Baseball--Statistics.

Baseball.

Genre:

Statistics.

Physical Description:

xviii, 350 pages : illustrations ; 24 cm

Place of Publication:

New York : Copernicus, [2001]

Summary:

To real baseball fans, statistics are indispensable. But how useful are baseball stats as tools for evaluating a player, choosing a strategy, or predicting a winner? In this lively, thought-provoking look at the numbers and the game, the authors examine just what is learned from baseball statistics. They show that statistics is not just a powerful tool of analysis and prediction, but a pleasurable and informative pastime in its own right.

Contents:

Chapter 1 Simple Models from Tabletop Baseball Games 1

All-Star Baseball (ASB) 1

Model Assumptions of All-Star Baseball 8

The APBA Model: Introducing the Pitcher 9

Strat-O-Matic Baseball: The Independent Model 15

Sports Illustrated Baseball: The Interactive Model 20

Which Model Is Best? 24

Chapter 2 Exploring Baseball Data 27

Exploring Hitting Data 27

A Batch of On-Base Percentages 28

Simple Graphs 29

Typical Values

the Mean and the Median 31

Measures of Spread

Quartiles and the Standard Deviation 32

Interesting Values 34

Comparing Groups 34

A Five-Number Summary 35

A Boxplot 35

Boxplots to Compare Groups 35

OBPs of Offensive and Defensive Players 37

Relationships Between Batting Measures 38

Relating OBP and SLG 39

Relating OBP and Isolated Power 39

What about Pitching Data? 41

Strikeouts and Walks 42

Looking at Strikeout Totals 43

Defining a Strikeout Rate 44

Comparing Strikeout Rates of Starters and Relievers 47

Association Between Strikeouts and Walks? 48

Exploring Walk Rates 49

Comparing Walk Rates of Starters and Relievers 50

Chapter 3 Introducing Probability 51

Beyond Data Analysis 51

Looking for Real Effects 53

Predicting OBPs 55

Probability Models 57

A Coin-Toss Model 57

Observed and True OBPs 59

Learning about Batting Ability 62

Estimating Batting Ability Using a Confidence Interval 66

Comparing Hitters 68

Chapter 4 Situational Effects 71

Surveying the Situation 72

Looking for Real Effects 74

Observed and True Batting Averages 75

Batting Averages of the 1998 Regulars 78

Two Models for Batting Averages 79

A .276 Spinner Model 79

Do All Players Have the Same Ability? 80

A Model Using a Set of Random Spinners 81

Situational Effects 86

Home vs. Away 86

Turf vs. Grass 87

The Count 87

Opposite Arm vs. Same Arm 87

Models for Situational Effects 87

Scenario 1 (No Situational Effect) 89

Scenario 2 (Situational Bias) 90

Scenario 3 (Situational Effect Depends on Ability) 91

Finding Good Models 92

What Do Observed Situational Effects Look Like When There Is No Effect? 93

The Last Five Years' Data 95

The "No Effect" Situations 96

The "Bias" Situations 98

The "Ability" Situations 101

How Large Are the True Ability Effects? 106

Game Situation Effects 107

A Lot of Noise 108

Chapter 5 Streakiness (Or, the Hot Hand) 111

Thinking about Streakiness 112

Interpreting Baseball Data 114

Moving Averages

Looking at Short Intervals 116

Runs of Good and Bad Games 119

Numbers of Good and Poor Hitting Days 120

What Is Zeile's True Hitting Ability? 120

Mr. Consistent 122

How Does Mr. Consistent Perform During a Season? 122

Mr. Streaky 126

How Does Mr. Streaky Perform During a Season? 129

Mr. Consistent or Mr. Streaky? 132

Team Play 134

A Consistent Team 138

A Streaky Team 141

Thinking about Streakiness

Again 143

Chapter 6 Measuring Offensive Performance 145

The Great Quest 146

Runs Scored per Game 148

Batting Average and Runs Scored per Game 153

Slugging Percentage and On-Base Percentage 157

Intuitive Techniques 165

On-Base Plus Slugging (OPS) 166

Total Average (TA) 166

Batter's Run Average (BRA) and Scoring Index (DX) 170

Runs Created (RC) 171

More Analytic Models 174

Chapter 7 Average Runs Per Play 177

Finding Weights for Plays 177

Least Squares Linear Regression (LSLR) 178

Adding Caught Stealing to the LSLR Model 184

Adding Sacrifice Flies to the LSLR Model 187

The Lindsey-Palmer Models 189

George Lindsey's Analysis 189

Palmer Enters the Picture 199

Comparing the LSLR and Lindsey-Palmer Models 202

Chapter 8 The Curvature of Baseball 207

The DLSI Simulation Model 208

The Probability of Scoring Two Runs 209

The Probability of Scoring No Runs 211

A DLSI Example 215

Lessons from the Simulation 219

DLSI and Runs per Play 224

Where Do We Stand? 226

Additive Models 227

Product Models 228

Player Evaluations in the Best Models 230

Player Evaluations on an Average Team 233

Sorting Out Strengths and Weaknesses 240

Chapter 9 Measuring Clutch Play 243

Clutch Hits 245

Leading Off an Inning vs. Not Leading Off 249

Runners in Scoring Position vs. Bases Empty 249

Runner in Scoring Position vs. Runner on First Base Only 249

Two Outs vs. None/One Out 251

Late Inning Pressure vs. No Late Inning Pressure 251

A Player in a Short Series 251

Situation Evaluation of Run Production 253

A New Criterion for Performance 259

The Calculation of Win Probabilities 266

Player Game Percentage (PGP) 272

World Series Most Valuable Players 279

Looking to the Future 282

Chapter 10 Prediction 285

Predicting Game Results 285

Guessing 286

Picking the Home Team 286

A "Team Strengths" Prediction Model 286

Predicting 1999 Game Results 287

How Good Were Our Predictions? 289

Predicting the Number of McGwire and Sosa Home Runs 291

A Simple Prediction Method 291

What's Wrong with This Prediction? 292

A Spinner Model for Home-Run Hitting 293

How Many At-Bats? 294

What If We Knew Sosa's True Home-Run Rate? 294

Binomial Probabilities 295

What If We Don't Know Sosa's True Home-Run Rate? 296

Revising Our Beliefs about Sosa's Home-Run Probability 298

One Prediction 299

Many Predictions 302

Predicting Career Statistics 305

Sosa's Home-Run Probabilities 306

How Long and How Many At-Bats? 307

Making the Predictions 309

Chapter 11 Did the Best Team Win? 311

The Big Question 312

Ability and Performance 312

Describing a Team's Ability 314

Describing a Team's Performance 314

Team Performance: 1871 to the Present 315

Explanations for the Winning Percentages 317

A Normal Curve Model 319

Team Performances over Time (Revisited) 321

A Mediocrity Model for Abilities 323

A Normal Model for Abilities 324

Weak, Average, and Strong Teams 325

A Model for Playing a Season 326

Simulating a Season 327

Simulating an American League Season 328

Simulating Many American League Seasons 332

Performances and Abilities of Different Types of Teams 333

Simulating an Entire Season 337

Chance 340

Chapter 12 Post-Game Comments (a Brief Afterword) 343.

Notes:

Includes bibliographical references (pages 347-350).

ISBN:

0387988165

OCLC:

45861989