Arguing about sports is as old as the games people play. Over the years sports debates have become muddled by many myths that do not match the numbers generated by those playing the games. In The Wages of Wins, the authors use layman’s language and easy to follow examples based on their own academic research to debunk many of the most commonly held beliefs about sports.
In this updated version of their book, these authors explain why Allen Iverson leaving Philadelphia made the 76ers a better team, why the Yankees find it so hard to repeat their success from the late 1990s, and why even great quarterbacks like Brett Favre are consistently inconsistent. The book names names, and makes it abundantly clear that much of the decision making of coaches and general managers does not hold up to an analysis of the numbers. Whether you are a fantasy league fanatic or a casual weekend fan, much of what you believe about sports will change after reading this book.

David J. Berri is Associate Professor of Economics at California State University, Bakersfield. Martin B. Schmidt is Associate Professor of Economics at the College of William and Mary. Stacey L. Brook is Associate Professor of Economics at the University of Sioux Falls.

The Wages of Wins

Taking Measure of the Many Myths in Modern SportBy DAVID J. BERRI MARTIN B. SCHMIDT STACEY L. BROOK

Stanford University Press

Copyright © 2007 Board of Trustees of the Leland Stanford Junior University
All right reserved.
ISBN: 978-0-8047-5844-4

Contents

List of Figures and Tables……………………………………..viiPreface………………………………………………………xiPreface to the Paperback Edition………………………………..xvChapter 1 Games with Numbers……………………………………1Chapter 2 Much Talking, Little Walking…………………………..11Chapter 3 Can You Buy the Fan’s Love?……………………………30Chapter 4 Baseball’s Competitive Balance Problem?…………………46Chapter 5 The NBA’s Competitive Balance Problem?………………….69Chapter 6 Shaq and Kobe………………………………………..90Chapter 7 Who Is the Best?……………………………………..116Chapter 8 A Few Chicago Stories…………………………………146Chapter 9 How Are Quarterbacks Like Mutual Funds?…………………172Chapter 10 Scoring to Score…………………………………….201Notes………………………………………………………..227References……………………………………………………269Index………………………………………………………..279

Chapter One

GAMES WITH NUMBERS

Sports are entertainment. Sports do not often change our world; rather they serve as a distraction from our world. Though sports can often lead to heated debates and occasional violence, in the final analysis sports are mostly about having fun.

Beyond the painful losses and possible violence, there is another not-so-fun aspect of sports. Sports come with numbers. And analyzing these numbers involves math. For many, math was not a favorite subject in school. Math can be hard. Math can be confusing. Math can be scary. So why do people in sports introduce something that is not fun into something that gives our life so much joy and pleasure? Why do sports need all these pesky numbers?

Our answer begins with a simple observation. Typically fans follow teams, not players. Jerry Seinfeld has observed that people can hate a player who plays on an opposing team, then love the very same player when he plays for their team. For Seinfeld, this means that people are really just “rooting for clothes.”

Although teams are what people follow, the actions of the individual players impact what we see for the team. When a team wins, we praise the players who we think made this happen. When our teams lose, we are just as quick, if not quicker, to blame the players who are responsible for making us feel so bad. How do we evaluate these individual players? Without numbers, this would be difficult. To see this point, consider the question of who is the best. Any fan of a team sport like baseball, basketball, or football can answer this question. Can one answer the question, though, without using any numbers?

Of course, every once in a while a coach or sportswriter will argue that the games are not about the numbers. From their logic, if one really understands the games, then all those numbers are unnecessary. To test the necessity of numbers, let us consider the decisive game of the 2005 NBA Finals. In this game the San Antonio Spurs scored 81 points and the Detroit Pistons scored 74. Quickly, who won the game? For those who believe statistics do not tell us the story, this question is hard to answer.

If we can’t refer to numbers in our assessment, we could never know who won any game. In fact, all those numbers on the shiny score board probably just seem like a distraction in our efforts to enjoy watching very tall people run around a basketball court.

As our simple thought experiment illustrates, numbers are important. Numbers do more than tell us who won and lost. Numbers allow us to see what our eyes cannot follow. In baseball, where numbers have been tracked since the 19th century, numbers are obviously crucial. To illustrate, let us imagine that we wished to identify the greatest hitter in baseball history. As long as we are using our imagination, let’s say we had access to time travel so that we could actually watch every single player in the history of the game. Finally, let’s also say that we think that the best measure of a hitter’s ability is batting average, or hits per at bat. Yeah, we know. We lost you on that last bit of imagination. Time travel you could buy. We all know, though, that batting average is not the best measure of a hitter’s performance. You would think that if you figured out time travel you would have more sophisticated measures of baseball performance at your disposal.

Still, let’s stick with our story. Here we are traveling through time. We come across Ty Cobb. He looks like a very good hitter. Maybe not a nice person, but still, he seems to get hits a bit more frequently than other players. Is Cobb the best? Well, after a bit more travel we see Tony Gwynn. He also seems to get hits a bit more frequently than other players. If all we did was look at these two players, could we see who is better?

Well, let’s look at batting average. We see that Cobb’s lifetime mark was 0.366. When Gwynn retired his career mark was 0.338. So Cobb hit safely 37% of the time while Gwynn hit safely on 34% of his at bats. If all you did was watch these players, could you say who was a better hitter? Can one really tell the difference between 37% and 34% just staring at the players play? To see the problem with the non-numbers approach to player evaluation, consider that out of every 100 at bats, Cobb got three more hits than Gwynn. That’s it, three hits. If you saw every at bat for both players, could you see this difference? Back in the 19th century the answer must have been no, since people started to calculate batting averages. Then, because people began to suspect that there is more to hitting than a batting average, a host of other statistics and measures began to be tracked.

These numbers allow us to master time travel. We do not need to travel through time to compare Cobb and Gwynn. Now we can just look at the numbers, and like magic, we are transported across space and through time.

Of course, historical comparisons are not always possible. There is one small step that must have been taken in the past if we wish to compare players today. Someone in the past had to record the numbers. To see this point, let’s think about basketball. One of the best basketball players today is Shaquille O’Neal. How does O’Neal compare to Wilt Chamberlain, one of the best players from the 1960s? Well, now we are out of luck. For us to make this comparison we needed people in the past to keep track of all the numbers. Unfortunately, much of the data we use to evaluate performance in the NBA today was not tracked by the NBA for individual players before the mid-1970s. Wilt retired in 1973. So today we really can’t say if Wilt was better or worse than Shaq.

Now people who have seen both players might insist Wilt is better. Or they might insist Shaq is better. Without all the numbers, though, how can we tell? To illustrate, let’s just think about one of the missing stats, turnovers.

Allen Iverson in 2004-05 led the NBA with 4.6 turnovers per game. So every quarter or so, on average, Iverson turned the ball over. Chauncey Billups that same season only averaged 2.3 turnovers per game, or about one turnover per half. If you were only watching, could you tell the difference? Remember, turnovers are not the only thing happening in a basketball game. Players are making shots, missing shots, blocking shots, collecting rebounds, and creating steals. All ten players on the court are taking these actions. While all this is going on, Iverson is committing two turnovers each half while Billups only loses the ball once. If we just stare at all these players, is it possible to see the difference in turnovers committed between Iverson and Billups?

Obviously we are arguing that without the numbers you really couldn’t tell. Basically, numbers are necessary to answer the question, who is best? We would also argue that numbers can be used to tell us so much more. Of course, the analysis of numbers can be difficult. For people who are not accustomed to looking at numbers systematically, the stories they might believe may not be consistent with the stories the numbers offer. In the end, this is the tale we tell. We wish to show that these numbers tell stories that differ from popular perception.

FREAKONOMICS AND ALFRED MARSHALL

The approach we take in our work is quite similar to the approach taken by Steven Levitt. In 2005 economist Steven Levitt co-authored a book with Stephen Dubner entitled Freakonomics. A reading of this work reveals that Levitt considers himself a bit of an outsider in the world of economics.

Despite Levitt’s elite credentials (Harvard undergrad, a PhD from MIT, a stack of awards), he approached economics in a notably unorthodox way. He seemed to look at things not so much as an academic but as a very smart and curious explorer…. He professed little interest in the sort of monetary issues that come to mind when most people think about economics; he practically blustered with self effacement. “I just don’t know very much about the field of economics,” he told Dubner at one point…. “I’m not good at math, I don’t know a lot about econometrics, and I also don’t know how to do theory.” … As Levitt sees it, economics is a science with excellent tools for gaining answers but a serious shortage of interesting questions.

His dissatisfaction with economics has led Levitt to invent a field he calls “Freakonomics.” Levitt and Dubner define “freakonomics” as a field that “employs the best tools that economics can offer … (and) allows us to follow whatever freakish curiosities may occur to us” (p. 14). What Levitt appears to be saying is that he is taking the methodology of economics and applying it to real world problems, problems that are not only interesting to him but also to the vast population of non-economists who live on this planet. Although Levitt’s approach is not emphasized today as often as it should be, it is hardly a new approach.

To understand this point you need to understand that economics is a relatively young discipline. Adam Smith, who wrote both the Theory of Moral Sentiments (1759) and The Wealth of Nations (1776), is considered the founder of classical economics. Smith, though, probably did not consider himself an economist. He was specifically a moral philosopher, interested in history, sociology, psychology, and political science, in addition to having a passing familiarity with economics. Surprising to students of economics today, Smith did not offer mathematics or models in presenting his thoughts. Smith simply used words, enough words in The Wealth of Nations to fill over 1,000 pages. These words contained the basic theories Smith was presenting, but also stories and illustrations to support his arguments. In other words, Smith made an effort to connect his work to the world where his readers lived.

More than 100 years after Smith, another great economist defined the methodology he believed economists should follow. Alfred Marshall, the intellectual heir of Smith and the man often credited as the father of neoclassical economics, defined economics as “a study of mankind in the ordinary business of life”(Marshall, 1920, p. 1). For Marshall, economics should not be limited to an abstract model presented on the chalkboard, but should move beyond the math and offer useful insights into the lives people lead.

In a letter to A. L. Bowley in 1906 Marshall laid forth his basic step-by-step approach, which we paraphrase below:

The Marshallian Method

Step One: Math can be used, but only as a “shorthand language.”

Step Two: Any math should be translated into words.

Step Three: A theory should be illustrated by examples that are “important in real life.”

Step Four: With words and real world illustrations in hand, you can now “burn the mathematics.”

Step Five: If you cannot find any real world examples, burn the theory.

Marshall was only joking when he said one should burn the math. Marshall, though, did place most of his math in footnotes and appendices and primarily used words and illustrations to present his ideas. In essence, the Marshallian method is Levitt and Dubner’s “freakonomics.” Marshall argued the research must be illustrated “by examples that are important in real life.” As the number of economists grew in the century since Marshall wrote this letter, the discipline moved away from this basic sentiment.

Why did economics change? Again, it is all about the numbers. In Marshall’s day, when the number of economists was relatively small, an economist who could not communicate with non-economists would not have much of an audience. Today, though, the population of economists is large enough that one can do very well in our discipline speaking only to fellow economists. In fact, as Levitt may feel, those who try to communicate economics to non-economists could be thought of as freaks in the discipline.

Our work will follow in the footsteps of Levitt and return to the original Marshallian method. We will be utilizing the tools of economics. These tools will allow us to make observations relevant to the real life, or what passes for the real life, of sports fans. We hope that much of what we say will be interesting. Although we will of course reference numbers, we are going to follow the example of Marshall and relegate the math and statistical analysis to the endnotes of this book, the web sites associated with the book (www.wagesofwins.com and dberri.wordpress. com), and our published work in academic journals and collections. If you look quickly through this book you will see tables with numbers, but nothing more complicated than what you might find in a box score in the local paper. As for equations, you will be hard pressed to find any of these. Although we did not burn the math, we did make every effort to get it out of the way of the story we are trying to tell.

THE CONVENTIONAL WISDOM

We begin our story drawing upon an important concept employed by Levitt: Conventional Wisdom. This term was both coined and defined by John Kenneth Galbraith, one of the leading economists of the 20th century. According to Galbraith (1958):

[A] vested interest in understanding is more preciously guarded than any other treasure. It is why men react, not infrequently with something akin to religious passion, to the defense of what they have so laboriously learned. Familiarity may breed contempt in some areas of human behavior, but in the field of social ideas it is the touchstone of acceptability. Because familiarity is such an important test of acceptability, the acceptable ideas have great stability. They are highly predictable. It will be convenient to have a name for the ideas which are esteemed at any time for their acceptability, and it should be a term that emphasizes this predictability. I shall refer to these ideas henceforth as the conventional wisdom. (Galbraith, 1958, pp. 6-7, italics added)

An abundance of “conventional wisdom” can be found in sports. Here is a “Top-Ten” list drawn from our research into the economics of sports.

1. The teams that pay the most, win the most. In other words, sports teams can buy the fans’ love.

2. Labor disputes threaten the future of professional sports.

3. Major League Baseball has a competitive balance problem.

4. A league’s competitive balance is determined by league policy.

5. National Basketball Association (NBA) teams need “stars” to attract the fans.

6. The best players in basketball score the most.

7. The best players in basketball make their teammates more productive.

8. The best players in basketball play their best in the playoffs.

9. Quarterbacks should be credited with wins and losses in the National Football League (NFL).

10. If we understand a quarterback’s past performance we can predict his future productivity.

For sports fans most, if not all, of these ideas should be familiar. Players, coaches, and members of the media recite these lines often in the discussion of professional sports. Beyond being representative of conventional wisdom, what else do these ideas have in common? Those numbers we spoke of previously suggest that all of these ideas are not quite true. Of course, to those raised on the conventional wisdom of sports, this contention simply cannot pass the “laugh test.” Are we suggesting that player strikes don’t threaten the survival of professional sports? Or that baseball does not have a competitive balance problem? Or, and for basketball fans this might be the greatest heresy, scorers like Allen Iverson are not the best players in the NBA? For some sports fans the mere suggestion that the conventional wisdom is untrue has led to a bit of laughter and the closing of this book. For everyone else, let’s think a bit harder about that “laugh test.”

THE LAUGH TEST

What is the “laugh test”? As professors of economics, with a passing familiarity with statistical analysis, we looked long and hard for evidence that this test exists. From what we have seen of formal statistics, there is no such thing as a “laugh test.” Still, people often employ this term when they come across analysis that violates conventional wisdom.

(Continues…)

Excerpted from The Wages of Winsby DAVID J. BERRI MARTIN B. SCHMIDT STACEY L. BROOK Copyright © 2007 by Board of Trustees of the Leland Stanford Junior University. Excerpted by permission.
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