» Curve Ball: Baseball, Statistics, and the Role of Chance in the Game
Curve Ball: Baseball, Statistics, and the Role of Chance in the Game Details
Binding: PaperbackDewey Decimal Number: 796.357021
EAN: 9780387001937
ISBN: 038700193X
Label: Springer
Manufacturer: Springer
Number Of Items: 1
Number Of Pages: 432
Publication Date: 2003-04-08
Publisher: Springer
Studio: Springer
Accessories for Curve Ball: Baseball, Statistics, and the Role of Chance in the Game
Items related to Curve Ball: Baseball, Statistics, and the Role of Chance in the Game
Curve Ball: Baseball, Statistics, and the Role of Chance in the Game Reviews
Customer Rating:




Summary: An Important Addition to a Baseball Library
Comment: For anyone who wishes to understand cause and effect in baseball, this is a very insightful book. The authors clearly and convincingly demonstrate that many of the statistical outcomes we generally attribute to a player's ability are really nothing more than random effects. For example, a player with a lifetime batting average of .300 who hits .280 during a season is said to have had an "off year." The authors show that any player who has a true batting ability to produce base hits in 30% of his at bats (i.e., a .300 hitter) can be expected to hit .280 or less or .320 or higher about one-third of the time. For someone who grew up thinking that all these year-to-year fluctuations were the result of "good years" and "bad years," the very significant impact of randomness came as a rude awakening! But for the serious student of the game, this is a critically important insight.
Similarly, the authors show that a team's win-loss record during any single season may not reflect the team's real ability. Again, in 162 game season, randomness rears its head. It is not that uncommon for a team to win 12 to 15 games less (or more) than its underlying talent would suggest.
As we reduce the number of games in a series (for example, consider the typical best-of-seven post-season playoff format), the effects of randomness are greatly magnified. Thus it is not at all uncommon for the best major league team in any season to fail to win the World Series.
"Curve Ball" is well written, and the authors do a good job of explaining the statistical models they employ. I often find myself returning to this book to refresh my understanding of baseball probabilities.
The one deficiency that bothers me most is the lack of a subject index. Thus the reader is forced to thumb through the book to locate some particular topic of interest. But even so, this is an excellent book that belongs in any good baseball library.
Customer Rating:





Summary: baseball statistics interpreted by professional statisticians
Comment: Jim Albert and Jay Bennett share two traits that make them the perfect authors for this type of book (1) they are both baseball fans who know the game and have seen many games and much statistics from many angles and (2) they are both professional statisticians who understand probability and the subtle aspects that chance can have on statistics. By being professional statisticians they also know how sophisticated statistical techniques can add to ones ability to seriously address questions of strategy and comparison of player performance. That is what they accomplish in this book, teaching some basic probability and statistics along the way.
They also make it very interesting to the baseball fan by raising interesting baseball questions related to players that the fans relate to, namely the stars that the fans follow and the great clutch hits and clutch defensive plays that we baseball fans have imprinted in our memories, like Mazeroski's game winning home run in the 1960 World Series, or Willie Mays' famous over the shoulder catch of Vic Wertz's long fly ball in the 1954 series, or Bobby Thompson home run that won the 1951 playoffs for the Giants.
In the very beginning Albert and Bennett distinguish themselves from the sports statisticians that are hired by the teams. The sports statisticians collect the data and present it in various ways. However, this is merely exploratory data analysis. Albert and Bennett point out that a numerical difference in a hitting statistic such as on base percentage between Chuck Knoblauch and Kenny Lofton may be a real difference in ability but may also be a small enough difference to be merely due to chance. Finding ways to analyze the baseball data to make probabilistic inferences like answering the question of whether Lofton is better at getting on base than Knoblauch is the focus of what professional statisticians do and is the theme of the book.
In the course of reading the book you will learn many things about baseball. Some may agree with previous notions and some will be surprises. You will learn about the massive amount of major league baseball data available, about SABR a society for baseball research and more. You will be opened up to the hinden world of professional statistics where probability models have been used for over a century to handle military, engineering, energy, environmental, agricultural and medical problems. These same tools in recent years have been used to handle baseball questions also.
They start with simple table top baseball games like All Star Baseball to introduce concepts. They then move on to baseball data and probability. Then they look at statistical questions, situational effects in Chapter 4, hot hitting in Chapter 5, methods of measuring offensive performance in Chapter 6, more sophisticated measures in Chapter 7, simulation models in Chapter 8, measures of clutch play and team value in Chapter 9, ways to predict performance in Chapter 10, analyzing World Series results in Chapter 11 and final comments in Chapter 12.
This is a great book for any one who loves baseball and baseball statistics. It also is a great way to learn and become interested in the techniques of the professional statistician.
For statisticians that teach statistics, it provides a wealth of interesting examples to help illustrate important statistical concepts in basic or even advanced courses, including the value of Bayesian methods, the need for overdispersion models (e.g. batting averages) and the value of linear and nonlinear prediction models.
Customer Rating:





Summary: Non Fiction
Comment: 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.
Customer Rating:





Summary: good statisticians, pretty good writers
Comment: This one is a book for the Sabermetrically inclined who already have a background in stats. In the first couple of chapters, the authors review some basic concepts through the lens of baseball before getting into some deeper analyses. To be honest, there's nothing in here that you can't get in Baseball Between the Numbers (although to the authors' credit, this book predates BBTN by 6 years) but it's a decent starter's guide. Worth the read, although those with a background in Sabermetrics will probably want to pass.
Customer Rating:





Summary: For people with serious statistical interest
Comment: I enjoyed this book a lot, but it's definitely for people who are serious about the use of statistics, not for fans looking to settle arguments about "who was better." You don't have to be a statistician, but you need to have some understanding of the basis of the math of statistics to understand this book.



