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

Front Cover
Jim Albert, Jay Bennett
Springer Science & Business Media, Apr 8, 2003 - Juvenile Nonfiction - 410 pages
3 Reviews
"... a smart and energetic collection of essays on baseball statistics. Curve Ball doesn't play misty-eyed homage to baseball's traditions and conventional wisdoms.... This is great stuff.... Curve Ball makes clear how pleasurable [stats] can be, and arguably how important, to view the great American game with real precision." -- The Wall Street Journal "Rating: 4.5 out of 5. Must own!" -- Baseballnotebook.com "In [Curve Ball] Albert & Bennett explain the game in ways the conventional press - even titans such as Bill James - cannot." -- Baseball America "[The book] illustrates how statistical reasoning can be useful in teasing out the role of chance from performance in baseball to better assess ability.... Curve Ball represents another advance in the genre of baseball and statistics books." -- Journal of the American Statistical Association There is a fascination among baseball fans and the media to collect data on every imaginable event during a baseball game and to use these data to try to understand characteristics of the game. But patterns in baseball data are difficult to detect due to the inherent chance variation that is present. This book addresses a number of questions that are of interest to many baseball fans - including how to rate players, predict the outcome of a game or the attainment of an attainment, make sense of situational data, and decide the most valuable players in the World Series. Curve Ball is directed to a general audience and does not assume that the reader has any prior background in probability or statistics, although knowledge of high school algebra will be helpful.
  

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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

Preface to the Paperback Edition
xiii
Introduction
xv
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
THE CURVATURE OF BASEBALL
209
The DLSI Simulation Model
210
The Probability of Scoring Two Runs
211
The Probability of Scoring No Runs
213
A DLSI Example
217
Lessons from the Simulation
220
DLSI and Runs per Play
226
Where Do We Stand?
228

EXPLORING BASEBALL DATA
27
A Batch of OnBase 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
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 Averages Looking at Short Intervals
116
Runs of Good and Bad Games
119
Numbers of Good and Poor Hitting Days
120
Mr Consistent
121
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?
131
Team Play
134
A Consistent Team
138
A Streaky Team
140
Thinking about Streakiness Again
143
MEASURING OFFENSIVE PERFORMANCE
145
The Great Quest
146
Runs Scored per Game
148
Batting Average and Runs Scored per Game
155
Slugging Percentage and OnBase Percentage
158
Intuitive Techniques
167
OnBase Plus Slugging OPS
168
Batters Run Average BRA and Scoring Index DX
170
Runs Created RC
173
More Analytic Models
175
AVERAGE RUNS PER PLAYS
179
Least Squares Linear Regression LSLR
180
Adding Caught Stealing to the LSLR Model
186
Adding Sacrifice Flies to the LSLR Model
189
The LndseyPalmer Models
191
Palmer Enters the Picture
202
Comparing the LSLR and LindseyPalmer Models
204
Additive Models
229
Product Models
230
Player Evaluation in the Best Models
232
Player Evaluation on an Average Team
235
MAKING SENSE OF BASEBALL STRATEGY
245
Lindseys Run Potential Talbe
246
Old vs New Data
247
A Second Important Table
248
Stealing Second Base
249
To Steal or Not to Steal
251
A Different Criterion
253
Stealing in Other Situations
254
The Sacrifice Bunt
255
Sacrifice Bunts in the 2001 World Series
257
Should Curt Schilling Sacrifice?
259
How About Craig Counsell?
260
The Intentional Walk
262
Compare the Costs
263
MEASURING CLUTCH PLAY
269
Clutch Hits
271
Leading Off an Inning vs Not Leading Off
275
Two Outs vs NoneOne Out
277
Situation Evaluation of Run Production
279
A New Criterion for Performance
288
The Calculation of Win Probabilities
295
Player Game Percentage PGP
300
World Series Most Valuable Players
307
The 2002 World Series
311
Game 1
315
Game 2
317
Game 3
319
Game 4
320
Game 5
322
Game 6
323
Game 7
325
Looking to the Future
326
PREDICTION
327
Guessing
328
Predicting 1999 Game Results
329
How Good Were Our Predictions?
331
Predicting the Number of McGwire and Sosa Home Runs
333
A Simple Prediction Method
334
A Spinner Model for HomeRun Hitting
335
How Many AtBats?
336
Binomial Probabilities
337
What If We Dont Know Sosas True HomeRun Rate?
338
Revising Our Beliefs about Sosas HomeRun Probability
340
One Prediction
341
Many Predictions
344
Predicting Career Statistics
347
Sosas HomeRun Probabilities
348
How Long and How Many AtBats?
349
Making the Predictions
351
DID THE BEST TEAM WIN?
353
The Big Question
354
Describing a Teams Ability
356
1871 to the Present
357
Explanations for the Winning Percentages
359
A Normal Curve Model
361
Team Performance over Time Revisited
363
A Mediocrity Model for Abilities
365
A Normal Model for Abilities
366
Weak Average and Strong Teams
367
A Model for Playing a Season
368
Simulating a Season
369
Simulating an American League Season
370
Simulating Many American League Seasons
374
Performances and Abilities of Different Types of Teams
376
Simulating an Entire Season
380
Chance
382
POSTGAME COMMENTS A BRIEF AFTERWORD
385
TABLETOP BASEBALL GAMES
389
AllStar Baseball
390
StratOMatic Baseball
392
Sports Illustrated Baseball
394
Glossary
397
Bibliography
401
Index
405
Copyright

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About the author (2003)

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.

Bennett is a Principal Scientist with Telcordia Technologies and Editor of Statistics in Sport, as well as former Chair of the Sports Section of the American Statistical Association.

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