Curve Ball: Baseball, Statistics, and the Role of Chance in the Game
Jim Albert, Jay Bennett
Springer Science & Business Media, Apr 8, 2003 - Juvenile Nonfiction - 410 pages
"... 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 ReviewUser 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 gameUser 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
Preface to the Paperback Edition
SIMPLE MODELS FROM TABLETOP BASEBALL GAMES
Model Assumptions of AllStar Baseball
Introducing the Pitcher
The Independent Model
The Interactive Model
Which Model Is Best?
THE CURVATURE OF BASEBALL
The DLSI Simulation Model
The Probability of Scoring Two Runs
The Probability of Scoring No Runs
A DLSI Example
Lessons from the Simulation
DLSI and Runs per Play
Where Do We Stand?
EXPLORING BASEBALL DATA
A Batch of OnBase Percentages
Typical Values the Mean and the Median
Measures of Spread Quartiles and the Standard Deviation
A FiveNumber Summary
OBPs of Offensive and Defensive Players
Relationships Between Batting Measures
Relating OBP and SLG
What about Pitching Data?
Strikeouts and Walks
Looking at Strikeout Totals
Defining a Strikeout Rate
Comparing Strikeout Rates of Starters and Relievers
Association Between Strikeouts and Walks?
Exploring Walk Rates
Comparing Walk Rates of Starters and Relievers
Looking for Real Effects
Observed and True OBPs
Learning about Batting Ability
Estimating Batting Ability Using a Confidence Interval
Surveying the Situation
Looking for Real Effects
Observed and True Batting Averages
Batting Averages of the 1998 Regulars
Two Models for Batting Averages
Do All Players Have the Same Ability?
A Model Using a Set of Random Spinners
Turf vs Grass
Scenario 1 No Situational Effect
Scenario 2 Situational Bias
Scenario 3 Situational Effect Depends on Ability
Finding Good Models
What Do Observed Situational Effects Look Like When There Is No Effect?
The Last Five Years Data
The No Effect Situations
The Bias Situations
The Ability Situations
How Large Are the True Ability Effects?
Game Situation Effects
A Lot of Noise
STREAKINESS OR THE HOT HAND
Thinking about Streakiness
Interpreting Baseball Data
Moving Averages Looking at Short Intervals
Runs of Good and Bad Games
Numbers of Good and Poor Hitting Days
How Does Mr Consistent Perform During a Season?
How Does Mr Streaky Perform During a Season?
Mr Consistent or Mr Streaky?
A Consistent Team
A Streaky Team
Thinking about Streakiness Again
MEASURING OFFENSIVE PERFORMANCE
The Great Quest
Runs Scored per Game
Batting Average and Runs Scored per Game
Slugging Percentage and OnBase Percentage
OnBase Plus Slugging OPS
Batters Run Average BRA and Scoring Index DX
Runs Created RC
More Analytic Models
AVERAGE RUNS PER PLAYS
Least Squares Linear Regression LSLR
Adding Caught Stealing to the LSLR Model
Adding Sacrifice Flies to the LSLR Model
The LndseyPalmer Models
Palmer Enters the Picture
Comparing the LSLR and LindseyPalmer Models
Player Evaluation in the Best Models
Player Evaluation on an Average Team
MAKING SENSE OF BASEBALL STRATEGY
Lindseys Run Potential Talbe
Old vs New Data
A Second Important Table
Stealing Second Base
To Steal or Not to Steal
A Different Criterion
Stealing in Other Situations
The Sacrifice Bunt
Sacrifice Bunts in the 2001 World Series
Should Curt Schilling Sacrifice?
How About Craig Counsell?
The Intentional Walk
Compare the Costs
MEASURING CLUTCH PLAY
Leading Off an Inning vs Not Leading Off
Two Outs vs NoneOne Out
Situation Evaluation of Run Production
A New Criterion for Performance
The Calculation of Win Probabilities
Player Game Percentage PGP
World Series Most Valuable Players
The 2002 World Series
Looking to the Future
Predicting 1999 Game Results
How Good Were Our Predictions?
Predicting the Number of McGwire and Sosa Home Runs
A Simple Prediction Method
A Spinner Model for HomeRun Hitting
How Many AtBats?
What If We Dont Know Sosas True HomeRun Rate?
Revising Our Beliefs about Sosas HomeRun Probability
Predicting Career Statistics
Sosas HomeRun Probabilities
How Long and How Many AtBats?
Making the Predictions
DID THE BEST TEAM WIN?
The Big Question
Describing a Teams Ability
1871 to the Present
Explanations for the Winning Percentages
A Normal Curve Model
Team Performance over Time Revisited
A Mediocrity Model for Abilities
A Normal Model for Abilities
Weak Average and Strong Teams
A Model for Playing a Season
Simulating a Season
Simulating an American League Season
Simulating Many American League Seasons
Performances and Abilities of Different Types of Teams
Simulating an Entire Season
POSTGAME COMMENTS A BRIEF AFTERWORD
TABLETOP BASEBALL GAMES
Sports Illustrated Baseball