Customer Reviews

A Mathematician at the Ballpark: Odds and Probabilities for Baseball Fans

5 star:		(9)
4 star:		(4)
3 star:		(3)
2 star:		(6)
1 star:		(2)

 

 

Nowhere does one find more recorded statistical data than in and around baseball. Seemingly everything gets measured and recorded. I found this delightful book last week at a mathematics conference and looked through it because of its combination of a chatty style and no shyness about describing the mathematics of probabilities and odds computations. So I bought the book -- and I found myself reading it through in a sitting!

I have read several other popularizations of statistics as applied to baseball, but I've not seen any with the clarity and focused precision Ken Ross has lovingly put into this book. Precisely because baseball has evolved into such a rich combination of strategy and tactics, Ross knows from his teaching experience that novices need to build up a good understanding of less complex applications of probability theory prior to looking at direct applications to the richness of baseball. In this book, he has repeatedly achieved a fine balance of showing the theory, its application to a gambling application, and then to a related baseball context. (Probablility theory originated in work done by Blaise Pascal, Leonhard Euler and Joseph-Louis LaGrange specifically to compute gambling odds.) Starting with how to interpret averages to identify the best hitter of all time, Ross takes us through how to determine the probable behavior of a player or team and how to calculate the probability that a World Series willl go four, five, six or seven games. There are a lot of useful tools in this little book, and its strength is in the cheerful way it tells us how to learn to use them. All the tools that are needed to go on to deeper study (and application!) are provided, along with an annotated bibliography and pointers to SABR.

This book would make a splendid gift for a young boy or girl who likes mathematics and sports!

This book is a waste of time. The title is VERY misleading (although the author is a mathematician and has gone to a ballpark), the chapter titles are very misleading and the book is 95% a textbook on teaching statistics and probability and 5% on their relationship to baseball. The subtitle of "odds and probabilities for baseball fans" should instead say "a textbook on odds and probabilities" (a good example is over 4 pages in the middle discussing statistics with respect to AIDS, after which he states "I apologize that the preceding examples aren't about baseball.") As one with an engineering degree you might expect that I found the book somewhat interesting and easy to read - not so. And as one who over the last 16 years has spent a week each summer traveling around the country with 2 buddies (seeing baseball games in every major league stadium and about 50 minor league stadium and traveled 32,000 miles in 48 states in the process) you could imagine my interest in the topic. But after the first 25 pages touch somewhat on baseball, the next 112 are mostly statistical/probability theory. On page 137 he says "Finally, I return to baseball!" That is true but the baseball content barely goes from 5% to 25% for the final 30 pages. Skip this book and watch a game instead. The cost is about the same and you'll remember the game.

This book gives a gentle introduction to probability and odds. It's not a statistics text and doesn't pretend to be one, though the last chapter discusses some questions that can really only be answered using statistical methods. The author makes it clear that the models he discusses, and often illustrates with familiar examples, are only approximations to real-world baseball (if there is such a thing). There's genuine math, presented in an engagingly informal style at about a high school level. Not enough to guarantee the reader will win at sports gambling, but enough to give a feeling for how the pros might do it. Along the way, there are tidbits of baseball lore salted with the author's frankly personal opinions.

There's something good that can be said about any book, and in this particular case, perhaps it's worth pointing out that "A Mathematician at the Ballpark" is brief, at just 152 pages (excluding appendices). And it has large print on each of it's smallish 5x8 inch pages.

If you want a very readable account of how statistical analysis of player performance has been applied in the real world and changed the way teams are assembled and managed, I highly recommend that you buy "Moneyball" by Michael Lewis.

If you want to delve a little more into statistical inference and analysis of player performance and aren't afraid of math, then I highly recommend "Curve Ball: Baseball, Statistics and the Role of Chance in the Game" by Jim Albert and Jay Bennett.

"Curve Ball" and "A Mathematician at the Ballpark" probably compete a little more directly for the mathematically inclined reader. And, to my mind, "Curve Ball" is the more compelling book because it asks (and attempts to answer) more interesting questions, like: "Is there such a thing as streakiness or the 'hot hand?'" Can you measure "clutch play"? And, how confident can you be that the "best team" actually won the World Series. In "Curve Ball," the questions are of primary importance, and statistical concepts are introduced to the extent they help "answer" the questions. In "A Mathematician at the Ballpark," in contrast, the author's focus is on statistics first, and only secondarily on baseball. Indeed, baseball often comes across as tangential to the author's narrative. You'll pick up as much about statistics and MUCH MUCH MORE about baseball, from "Curve Ball."

It's not a "bad" book. There are just better alternatives.

I have an extensive math background and found this book tough to read. Not because the math described is particularly complicated (it is most definitely not...high school algebra is sufficient), but because Ross' writing style is too verbose. 99% of readers should know the meaning of "average," yet Ross devotes a lot of text to explaining this concept. I'm not sure who his audience is. Math-savvy people may feel that concepts have been dumbed-down and over explained, but math-phobic people may be scared off by the use of formulas...and the majority of times they aren't really needed anway.

I recently read Mathematician at the Ballpark and thoroughly enjoyed it. One should know up front, though, that this is not a book about sabremetrics, or the so-called "new" statistics of baseball.

The book is intended to help the layperson understand the theories and formulas of probability that are the foundation of what most people call "odds". It is written in a refreshingly breezy style, especially considering that it's a math book and provides a nice step-by-step procedure to understanding how probability works and how one can convert that to odds.

Filled with examples using players both old and new, it will provide those interested in studying all those questions about baseball that begin "What's the chance of this happening?" a framework with which to do so. I highly recommend the book.

This is a wonderful little book! It gently introduces many of the basics of probability using baseball as a context. The author is a retired mathematician who learned to love numbers and probability through baseball. This book would be great for a bright kid (or anyone) who loves baseball but thinks math is boring. The author writes in a very friendly style, making things simple without being condescending.

This cheerful and meticulous little volume uses baseball as a medium for introducing clear, mathematically correct thinking about probability and statistics to high school and college students. What stands out most about Ross' writing are: (1) his ability to express his love for his subjects (math and baseball) in a dignified way, and (2) the clarity of the presentation.

If you are interested in a more hard-core sabremetrics dissertation that deeply probes the limits to what mathematics can reveal about baseball, you are looking in the wrong place. However, if you want to learn (or teach) basic concepts of probability and a correct understanding of random processes to add a new dimension your enjoyment of baseball (and understanding of the world in general), this book is a great place to start.

Ken Ross, a former President of the Mathematical Association of America and a lifelong student of baseball, has written a lively and highly illuminating account of the mathematics of baseball. Ross uses a rich collection of examples populated by real players to illustrate mathematical ideas that are being used with increasing frequency by more and more teams. Starting with Batting Average (AVG), that most basic of baseball terms, he soon has the reader appreciating Total Bases (TB), Slugging Percentage (SLG), On - Base Percentage (OBP), and BRA.

Ross is likely to surprise you, too, with the following puzzler:
In 1995, Derek Jeter's AVG was .250 and David Justice's AVG was .253. In 1996, Jeter's AVG was .314 and Justice's AVG was .321. Based on batting averages alone, you'd probably guess that Justice did better than Jeter over those two years. That guess is wrong. Read Mathematician at the Ballpark if you want to see why.

He next introduces a bit of elementary probability and explains how he used it in developing his own 'system' for betting on games. What is especially interesting about his system is it's reliance on betting on the underdog.

He follows, some might say of necessity, with great chapters titled When to Stop Betting and Streaks.

Ross is a gifted writer who really knows baseball and mathematics. You will apprciate both subjects a lot more after reading his book.

The reader should be aware that this is not a book about baseball: It is a book about probability and statistics. A well written, engaging, relevant and approachable book about probability and statistics, and seen in this light, a very valuable book.

Ross has created a book that will introduce any reader with a reasonable command of algebra to the key ideas of probability, odds-making and statistics. Unfortunately, Ross quickly discovers that his desire to use Baseball statistics as the primary motivation leads to a quadmire. Baseball outcomes are simply too complex: The pitcher, batter, ball-park, weather, time of day and game situation all have profound effects on the outcome of a particular at-bat. This leads Ross to focus just as much time on Roulette as he does on baseball.

I don't fault him too much for this, though perhaps he should have used a less mis-leading title. On the positive side, this book contains all the precision and care that is missing from Aczel's "Chance," and the book is a great read. I intend to use it as a supplementary text in my senior level Algorithms course to introduce students who have not yet taken Statistics to the key ideas of probability.