Fun, but very low-level math.

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I was a little bit disappointed in the level of mathematical sophistication in this book. Maybe I was expecting too much, but the math was really low level. I guess this is a plus for many readers, since basically anyone with a high school education and an understanding of Excel would be able to understand most of the book.

An example of the low level of math is that often times after the author introduces a mathematical model for a sport, instead of computing probabilities using a formula he resorts to Monte-Carlo simulation. I guess this makes it easier for non-mathematicians to follow, but as a probabilist it's a bit frustrating for me.

He does have some cool examples, but some of the chapters get a bit repetitive. At least half of the chapters seem to consist of
1) Gather data
2) Do a regression using Excel
This gets a bit repetitive. I think the beginning of the book is the strongest. This is the section on baseball. He does a nice job here at building up the techniques from simple analysis to more and more complicated analysis of individual players. I'm not an expert, but it seems to follow what likely was the historical development of sabermetrics.

Overall, if you like math and sports it's a fun read, just don't expect too much serious math.

Many topics, very shallow treatment. Disappointing.

The book talks about various aspects of using statistics and probability theory in professional sports. It is divided to four parts: baseball (MLB), American football (NFL) and basketball (NBA), and the fourth section talks about some sport gambling and general comments that are not a good fit to any of the other sections. The author of the book is a professor for operations and decision technologies and was also a statistics consultant for several professional teams such as the NBA's Dallas Mavericks (season 2006-2007).

Generally, the topics discussed in the book are interesting (to me both as a sports fan and with an interest and background in Math') and include topics like how to evaluate players, is there a correlation between teams wealth and the probability to win and how to compare players from different years.

However, the book itself is not an interesting read mainly because each topic is discussed in a very shallow level. The basic flow of each topic is to introduce the motivation of what statistical insights we are now checking, give the required math formula (usually without enough explanations or examples to understand it thoroughly), and than a single conclusion of the analysis is presented before continuing to the next topic. This results in the reader being left without any interesting findings or insights to learn about the topic in question with respect to different years, teams or players. For each given topic I could easily come up with several other questions that every die-hard NBA fan would like to see treated.

Basically, the book looks like a cooking book, that present an idea, gives you the formula (often discusses Excel implementation) and leaves all the hard work to the reader. Now, as the author consulted the Dallas Mavericks, most of these conclusions (in the NBA section) refer to that team in that relevant year (2006-2007). I would expect to see additional interesting results on each topic and without that I think the book will disappoint most readers.

Let me exemplify what I mean: a topic named "Are college basketball games fixed?" sounds like a very deep topic which should have profound consequences. However. this topic is exactly two pages long (57 lines to be exact), that obviously results in a very shallow treatment without any important conclusion. Another example, "Analyzing Team and Individual Matchups" topic (again, in the NBA section) is two and a half page long of text (and another 2 pages table) which only deals with the Spurs-Mavericks 2006 western conference semifinal. I guess most readers would want deeper analysis of more interesting encounters and see some data manipulation on other series.

As I wanted to get more information about these topics a quick look on various Internet website (which the book does give as good references points) I could find various articles that were more interesting than the book itself.

Overall, the book gave me a feeling that it is mostly a quick and naive compilation of a series of articles that were already posted somewhere else. Each topic (or an article in a blog\newspaper in a previous life) is treated without any deep thought or any desire to show deeper observations. If you are just looking for formulas, the internet is full of resources. This book presents very little interesting finding to sports fan.

Interesting read for math geek sports fans

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Wayne Winston addresses a myriad of topics in baseball, basketball and football via a statistics-heavy approach. There are 50 different "bites" spread out over 350 pages. There are many familiar topics for quantitative sports fans - Pythagorean theorem, platoon effects, player evaluations in different sports, and power rankings to name but a few.

The entire book is moderately math heavy - over half of it is devoted to quantitative solutions using algebra, statistics and Excel worksheets (which you can find online via included addresses). If you do not enjoy the mathematical side of sports, you'll find most of the book unreadable. If you do enjoy math, stats or using quantitative approaches to gambling, this book is a nice review of most of the interesting approaches out there. The bibliography of cited books reads like a "who's who" of credible quantitative sports texts.

A vast majority of the "bites" are already discussed extensively in other sources. The advantage of this book for most readers is that you can get such a diverse taste of different topics under one cover. If you are a sports modeler, the wide array of topics and approaches could help stir your own creativity. On more than one topic, I found myself saying "this assumption isn't valid!" But my making these assumptions and challenging them yourself, his approach opens up many unintended doors for the reader. For example, one bite addresses and argues that teams should pass more and run less than they do. To support this hypothesis, the book looks at a payoff chart for the yardage gained from a pass attempt versus a run attempt. The payoff chart does not consider volatility (rushing for 3 yards EVERY play is better than passing for 20 yards 1/4th of the time). It also doesn't look at the "disaster side" of passing - interceptions, quarterback fumble distribution, or greater offensive issues in a 2d and 10. Each article does make you think, which is its own payoff.

My only criticism is that the writing style seemed a little clunky. If you are not fluent in math, the combination of writing style and the amount of non-math quantity may turn you off.