Curve Ball: Baseball, Statistics, and the Role of Chance in the Game

Front Cover
Springer Science & Business Media, Jun 8, 2001 - Mathematics - 350 pages
2 Reviews
In its formative years, from the 1970s through the 1990s, sabermetrics was p- marily an amateur undertaking. Publications were aimed at a relatively small audience of baseball fans. To be sure, this ever-growing group of aficionados brought a lot of sophistication to baseball analysis, and were constantly looking for statistical insights beyond the listings of the top ten batters found in popular newspapers and magazines. But their influence on the baseball profession was very limited. A few consultants like Craig Wright developed temporary relati- ships with various teams, but none were able to stay long enough to create a p- manent sabermetrician staff position. (See Rob Neyer’s November 11, 2002, arti- 1 cle on ESPN. com. ) All of this changed, however, in 2002 with the hiring of Bill James by the Boston Red Sox. With that move, we have seen the admittance of the foremost proponent of sabermetrics into the top echelon of professional ba- ball management. The art and science of careful statistical analysis, it now seems, had made it into the big leagues. Since the publication of the first edition of Curve Ball in 2001, we have been overwhelmed by the positive responses from readers and critics. We’re pleased with the reception, of course, but we don’t want to rest on our laurels. Like a pitcher refining his repertoire, we’ve revised, expanded, and updated the book for its publication in this paperback edition. Several readers and critics took us 1 http://espn. go. com/mlb/columns/neyer_rob/1456664.
 

What people are saying - Write a review

LibraryThing Review

User Review  - bluetyson - LibraryThing

A look at baseball from a sports statistics and published mathematical analysis front. Interesting, but not as ground breaking as some of the amateur non university researchers came up with not too much later. A bit of an overview. Read full review

Curve ball: baseball, statistics, and the role of chance in the game

User Review  - Not Available - Book Verdict

Baseball is a fascinating game for the statistical analyst. On the surface it appears so simple and limited. But the more closely one studies the game, the more, it seems, there is to know. The ... Read full review

Contents

SIMPLE MODELS from Tabletop Baseball Games
1
Model Assumptions of AllStar Baseball
8
Introducing the Pitcher
9
The Independent Model
15
The Interactive Model
20
Which Model Is Best?
24
EXPLORING Baseball Data
27
A Batch of OnBase Percentages
28
Intuitive Techniques
165
OnBase Plus Slugging OPS
166
Batters Run Average BRA and Scoring lndex DX
170
Runs Created RC
171
More Analytic Models
174
AVERAGE RUNS per Play
177
Least Squares Linear Regression LSLR
178
Adding Caught Stealing to the LSLR Model
184

Simple Graphs
29
Typical Valuesthe Mean and the Median
31
Measures of SpreadQuartiles and the Standard Deviation
32
Interesting Values
34
A FiveNumber Summary
35
OBPs of Offensive and Defensive Players
37
Relationships Between Batting Measures
38
Relating OBP and SLG
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
INTRODUCING Probability
51
Looking for Real Effects
53
Predicting OBPs
55
Probability Models
57
Observed and True OBPs
59
Learning about Batting Ability
62
Estimating Batting Ability Using a Confidence Interval
66
Comparing Hitters
68
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
Do All Players Have the Same Ability?
80
A Model Using a Set of Random Spinners
81
Situational Effects
86
Turf vs Grass
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
STREAKINESS or The Hot Hand
111
Thinking about Streakiness
112
Interpreting Baseball Data
114
Moving AveragesLooking at Short lntervals
116
Runs of Good and Bad Games
119
Numbers of Good and Poor Hitting Days
120
Mr Consistent
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
MEASURING Offensive Performance
145
The Green Quest
146
Runs Scored per Game
148
Batting Average and Runs Scored per Game
153
Slugging Percentage and OnBase Percentage
157
Adding Sacrifice Flies to the LSLR Model
187
The LindseyPalmer Models
189
Palmer Enters the Picture
199
Comparing the LSLR and LindseyPalmer Models
202
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
MEASURING CLUTCH PLAY
243
Clutch Hits
245
Leading Off an Inning vs Not Leading Off
249
Two Outs vs NoneOne Out
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
PREDICTION
285
Guessing
286
Predicting 1999 Game Results
287
How Good Were Our Predictions?
289
Predicting the Number of McGwire and Sosa Home Runs
291
Whats Wrong with This Prediction?
292
A Spinner Model for HomeRun Hitting
293
How Many AtBats?
294
Binomial Probabilities
295
What If We Dont Know Sosas True HomeRun Rate?
296
Revising Our Beliefs about Sosas HomeRun Probability
298
One Prediction
299
Many Predictions
302
Predicting Career Statistics
305
Sosas HomeRun Probabilities
306
How Long and How Many AtBats?
307
Making the Predictions
309
DID THE BEST Team Win?
311
The Big Question
312
Describing a Teams Ability
314
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
POSTGAME COMMENTS A Brief Afterword
343
Bibliography
347
Copyright

Other editions - View all

Common terms and phrases

References to this book

All Book Search results »

About the author (2001)

Albert is Professor of Mathematics and Statistics at Bowling Green State University. He has served as Chair of the Sports Section of the American Statistical Association.

Bibliographic information