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Stumbling On Wins

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Stumbling On WinsTwo Economists Expose the Pitfalls

on the Road to Victory inProfessional Sports

David J. BerriMartin B. Schmidt

Vice President, Publisher: Tim MooreAssociate Publisher and Director of Marketing: Amy NeidlingerAcquisitions Editor: Kirk JensenEditorial Assistant: Pamela BolandDevelopment Editor: Russ HallOperations Manager: Gina KanouseSenior Marketing Manager: Julie PhiferPublicity Manager: Laura CzajaAssistant Marketing Manager: Megan ColvinCover Designer: Joy Panos StauberManaging Editor: Kristy HartProject Editor: Anne GoebelCopy Editor: Geneil BreezeProofreader: Sheri CainIndexer: Erika MillenCompositor: Nonie RatcliffManufacturing Buyer: Dan Uhrig

© 2010 David J. Berri and Martin B. Schmidt

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Printed in the United States of America

Second Printing May 2010

ISBN-10: 0-13-235778-XISBN-13: 978-0-13-235778-4

Pearson Education LTD.Pearson Education Australia PTY, Limited.Pearson Education Singapore, Pte. Ltd.Pearson Education North Asia, Ltd.Pearson Education Canada, Ltd.Pearson Educación de Mexico, S.A. de C.V. Pearson Education—JapanPearson Education Malaysia, Pte. Ltd.

Library of Congress Cataloging-in-Publication Data

Berri, David J.Stumbling on wins : two economists expose the pitfalls on the road to victory in professional

sports / David J. Berri, Martin B. Schmidt.p. cm.

ISBN 978-0-13-235778-4 (hardback : alk. paper) 1. Professional sports—Economicaspects—United States. 2. Professional sports—Social aspects—United States. I. Schmidt,Martin B. II. Title. GV716.B466 2010338.4’77960973—dc22

2009040397

To Lynn and Susan:We couldn’t have done this

without you.

Contents

Acknowledgments. . . . . . . . . . . . . . . . . . . . . xi

About the Authors . . . . . . . . . . . . . . . . . . . xiii

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

Chapter 1: Maybe the Fans Are Right . . . . . . . . . . . . . . 1Sporting Rationality. . . . . . . . . . . . . . . . . . . . . . . . . 3Crunchers, “Experts,” and the Wrath ofRandomness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5A Century of Mistakes in Baseball . . . . . . . . . . . . . 7

Chapter 2: Defending Isiah . . . . . . . . . . . . . . . . . . . . . . 13Isiah Thomas Illustrates How MoneyCan’t Buy You Love. . . . . . . . . . . . . . . . . . . . . . . . 14Getting Paid in the NBA. . . . . . . . . . . . . . . . . . . . 20Coaching Contradictions. . . . . . . . . . . . . . . . . . . . 23Isiah’s Defense. . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Chapter 3: The Search for Useful Stats . . . . . . . . . . . . 33Identifying the Most “Useful” Numbers . . . . . . . 33The Most Important Position inTeam Sports? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Assigning Wins and Losses . . . . . . . . . . . . . . . . . . 47

Chapter 4: Football in Black and White . . . . . . . . . . . . 49A Brief History of the Black Quarterback . . . . . . 50Performance in Black and White . . . . . . . . . . . . . 55Quarterback Pay in Black and White . . . . . . . . . 63

Chapter 5: Finding the Face of the Franchise . . . . . . . 67Birth of the Draft . . . . . . . . . . . . . . . . . . . . . . . . . 68The Problem with Picking First . . . . . . . . . . . . . . 69How to Get Picked First? . . . . . . . . . . . . . . . . . . . 78Back to Kostka . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Chapter 6: The Pareto Principle andDrafting Mistakes . . . . . . . . . . . . . . . . . . . . 83The Pareto Principle and Losing to Win . . . . . . . 83The NBA Draft and NBA Performance. . . . . . . . 93Catching a Baseball Draft . . . . . . . . . . . . . . . . . . 100

Chapter 7: Inefficient on the Field . . . . . . . . . . . . . . . 103Just Go For It! . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Evaluating the Little Man in Football . . . . . . . . 113The Hot Hand and CoachingContradictions . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Chapter 8: Is It the Teacher or the Students? . . . . . . 119The Wealth of Coaching . . . . . . . . . . . . . . . . . . . 120“Take Your’n and Beat His’n” . . . . . . . . . . . . . . . 122Deck Chairs? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Growing Older and Diminishing Returns . . . . . 126Putting the Picture Together . . . . . . . . . . . . . . . 132

Chapter 9: Painting a Bigger Picture . . . . . . . . . . . . . 135

Appendix A: Measuring Wins Produced in the NBA . . 141A Very Brief Introduction toRegression Analysis . . . . . . . . . . . . . . . . . . . . . . . 141Modeling Wins in the NBA . . . . . . . . . . . . . . . . 143Calculating Wins Produced in the NBA. . . . . . . 148Win Score and PAWS48 . . . . . . . . . . . . . . . . . . . 154A Comment on Alternatives . . . . . . . . . . . . . . . . 156Three Objections to Wins Producedfor the NBA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

viii STUMBLING ON WINS

Appendix B: Measuring Wins Producedin the NFL . . . . . . . . . . . . . . . . . . . . . . . . . 161

Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Chapter 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189Chapter 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194Chapter 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198Chapter 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202Chapter 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209Books and Articles. . . . . . . . . . . . . . . . . . . . . . . . 209Web Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

CONTENTS ix

Acknowledgments

The stories we present are drawn both from our research, and theresearch of others. Obviously, we are indebted to the authors of allthe studies we cite. Our own research, though, is not simply the prod-uct of our efforts. Specifically we are indebted to the following list ofcoauthors: Stacey Brook, J.C. Bradbury, Aju Fenn, Rod Fort, BradHumphreys, Anthony Krautmann, Young Hoon Lee, Michael Leeds,Eva Marikova Leeds, Michael Mondello, Joe Price, Rob Simmons,Brian Soebbing, and Peter von Allmen. We would also like to thankall of the economists who have participated in sessions on sports eco-nomics at the Western Economic Association and with the NorthAmerican Association of Sports Economists. These sessions havebeen a tremendous help in our work.

We also wish to thank Stefan Szymanski, who urged us to focus onthese stories for our next book; and Dean Oliver, who once again pro-vided invaluable insights and assistance for our study of basketballstatistics.

Several people read early drafts of chapters and made many valu-able suggestions. This list includes J. C. Bradbury, Owen Breck,Stacey Brook, Juliane Clapp, Rich Campbell, Jason Eshleman, JimPeach, Kevin Quinn, Raymond Sauer, and Stephen Walters. Specialthanks go out to Leslee Watson-Flores and Fred Flores, who took thetime to read and offer valuable comments on every single chapter.

The Wages of Wins Journal—a blog we started at the suggestionof J. C. Bradbury—has proven to be an invaluable resource. Ouraudience consistently provides insightful comments into the storieswe try and tell.

The people of FT Press, specifically Martha Cooley, Kirk Jensen,Russ Hall, Anne Goebel, and Tim Moore have all been extremelypatient and helpful. This book would not have been possible withoutMartha, so she certainly deserves a great deal of credit. And Kirk’seditorial assistance was essential in transforming our ideas into thisfinal product.

Finally, the list of people we have to thank includes our families,whose support is very much appreciated. Dave Berri would like tothank his wife, Lynn, as well as his daughters, Allyson and Jessica.Lynn read each and every chapter of this book, and her suggestionswent far to overcome the limitations in our writing abilities. MartinSchmidt would also like to thank his wife, Susan, as well as his chil-dren, Michael, Casey, and Daniel.

xii STUMBLING ON WINS

About the Authors

David J. Berri is an associate professor of economics at SouthernUtah University. He is coauthor of The Wages of Wins (Stanford Press).Additionally, he has authored or coauthored more than 30 academicpapers, most in the area of sports and economics. His nonacademicwriting has appeared in The New York Times, VIBE Magazine, andonline at The Wages of Wins Journal (dberri.wordpress.com). In 2009,he was elected president of the North American Association of SportsEconomists and is currently serving on the editorial board of both theJournal of Sports Economics and the International Journal of SportFinance. He lives with his wife (Lynn) and two daughters (Allyson andJessica) in Cedar City, Utah.

Martin B. Schmidt is a professor of economics at the College ofWilliam and Mary. He is coauthor of The Wages of Wins and also theauthor of nearly 40 articles primarily in the areas of monetary eco-nomics and sports economics. In addition, his writing has appeared inThe New York Times and The Sports Business Journal. He lives withhis wife and three kids in Williamsburg, Virginia.

Preface

Once upon a time, the word “moneyball” was only heard in refer-ence to a winning shot in billiards. A few years ago, though, thephrase moved out of the pool hall and onto the baseball diamond.The man responsible for this move was Michael Lewis. In 2003,Lewis published Moneyball, a book that tells the remarkable story ofthe Oakland A’s and General Manager Billy Beane. From 1996 to2006, Beane managed to consistently field a winning baseball teamwithout spending very much money on players. According to Lewis,this feat was accomplished because Beane knew something aboutmeasuring player performance that other decision-makers in baseballdidn’t know.

One year before Moneyball appeared, we published an articleexamining the coaches voting for the All-Rookie team in the NationalBasketball Association (NBA). This article suggested that coaches inthe NBA were not evaluating rookies correctly. Then in 2006 we pub-lished, along with Stacey Brook, The Wages of Wins. Our first bookexplored a variety of issues in sports and economics, including laborstrikes, competitive balance, and the ability of a player to “turn it on”in the playoffs. Within this list, we presented evidence that decision-makers in the NBA—like their counterparts in baseball—had prob-lems measuring the value of free agents.

The idea that people in baseball and basketball have trouble eval-uating players is certainly interesting to sports fans. Such stories,though, have implications beyond sports. In recent years, researchhas shown that, in general, people have trouble making “good” deci-sions. For example, Daniel Gilbert’s Stumbling on Happiness, a bookthat inspired our own title, showed how people’s efforts to find happi-ness are often sabotaged by their own actions. Dan Ariely, in Pre-dictably Irrational, presented a number of experiments that show thedifficulty people have in evaluating new information and makinggood decisions. And Richard Thaler and Cass Sunstein—in Nudge—not only describe the troubles people have making choices, but alsohow the presentation of choices can lead to better outcomes.

Much of this research is based on experimental evidence, and wefind such evidence to be persuasive. Still, in the world of professionalsports one might expect a different story. Sports come with an abun-dance of data to inform decisions. Plus, the consequences of failureare both quite severe and very public. In such an environment, weshould expect that the experts employed in the industry get it “right.”

The two stories told in Moneyball and The Wages of Wins,though, suggest otherwise. And these tales are actually just the tip ofthe iceberg. As the following pages reveal, similar stories can befound throughout the world of sports. We believe these stories shouldnot only change the way sports fans perceive the choices made bytheir favorite teams, but also impact the way economists and othersocial scientists think about human decision-making.

xvi STUMBLING ON WINS

Maybe the Fans Are Right

“I must say, with all due respect, I find it very hard to see thelogic behind some of the moves you have made with this fineorganization. In the past 20 years, you have caused myself,and the city of New York, a good deal of distress, as we havewatched you take our beloved Yankees and reduce them to alaughing stock.”

George Costanza upon meeting George Steinbrenner (ownerof the New York Yankees): Seinfeld, “The Opposite” (season5, 1994)

“What the hell did you trade Jay Buhner for?! He had 30home runs and over 100 RBIs last year. He’s got a rocket foran arm. You don’t know what the hell you’re doin’!”

Frank Costanza (George’s father) upon meeting GeorgeSteinbrenner: Seinfeld, “The Caddy” (season 7, 1996)1

Few sports fans ever meet the people who operate their belovedsports teams. Such a meeting, though, would probably inspire manyfans to get in touch with their inner “Costanza.” Given the opportu-nity, fans would love to ask:

• Why do you keep signing such lousy free agents?• Why can’t we ever draft players who actually help us win?• Why can’t we ever find a better goalie?• Why does the coach keep making that decision on fourth down?• Why does the coach keep playing that point guard?

1

1

2 STUMBLING ON WINS

Obviously, this is just a sample of the questions asked. And, just asobviously, we have cleaned up the language. What may not be obvi-ous is the economic implication of these questions.

Fans often suggest that decision-makers in sports are less thanperfect. Managers and coaches are not only accused of making badchoices, fans often accuse these people of making the same badchoices over and over again. Many economists, though, find such sto-ries unbelievable. After all, traditional economics clearly teaches thatdecision-makers are supposed to be “rational.”

What does it mean to be a “rational” decision-maker? ThorsteinVeblen sarcastically argued in 1898 that economists tend to see peo-ple as “hedonistic lightning calculators.”2 In more recent years,Richard Thaler and Cass Sunstein have just as sarcastically suggestedthat the rational decision-makers described by economists “can thinklike Albert Einstein, store as much memory as IBM’s Big Blue, andexercise the willpower of Mahatama Gandhi.”3

Both these remarks comment on the simple idea that rationaldecision-makers “choose efficiently the means that advance theirgoals.”4 Let’s imagine the behavior of a manager and coach that“chooses efficiently.” Such a person would tend to make the correctdecision given the circumstances they observe. Perhaps more impor-tantly, as the game changes, these same coaches and managers wouldchange their point of view and make different decisions. Therefore—and contrary to what sports fans often contend—it’s not possible forcoaches and managers to make the same mistake over and over again.

So who is right: fans or economists? The emerging field of behav-ioral economics—via a collection of laboratory experiments—seemsto side with the fans. Experiments have shown that people are notquite as rational as traditional economics contends.5 Some economistshave argued, though, that how people behave in a laboratory experi-ment is different from how they behave in the “real world.”6 In thereal world, people face real consequences for making mistakes, andreal consequences force people to be rational.

1 • MAYBE THE FANS ARE RIGHT 3

Sporting Rationality

To settle this debate, it might help to move out of the laboratoryand look at decisions in the “real world.” Sports are often described asbeing removed from reality. Yet for the people in this particular reality,what happens in sports matters. Consequently, we can learn about therationality—or irrationality—of human decision-making by examiningthe “real world” of sports. This examination, consistent with the exper-imental evidence, will show again and again and again (actually we willpresent at least 20 “agains”) that decisions in sports are not completelyrational.

Before we get to this examination, let’s emphasize that the word“irrational” is not synonymous with the word “stupid.” When weeventually argue that decision-makers in sports are “irrational,” wewill not be saying that people in sports are not as smart as people arein other industries or other occupations. In fact, people in sports areoften better prepared for their jobs than people employedelsewhere.7 Furthermore, it seems likely that whatever “irrationali-ties” are observed in sports are likely to be found elsewhere.

We make this claim because at first glance decision-makers insports perhaps more than anyone else should be “rational.” There aretwo characteristics of the sports industry that bolster this expectation.First, despite being a relatively small industry in the American econ-omy,8 sports receive an inordinate amount of attention from themedia. After all, no other industry has an entire section of each localpaper devoted to its happenings. Such coverage raises the cost of fail-ure to the participants in sporting contests. Losing in sports, as notedearlier, is not a private affair. Sports fans both near and far witnessyour failure and are often not shy in expressing their disappointment.Although people do pay some attention to failures in non-sportsindustries, it’s rare to see interested observers in other industries paymoney to yell obscenities at those who fail to achieve success.

4 STUMBLING ON WINS

Sports are not only different in terms of attention received. Insports, success and failure would seem to be—relative to other indus-tries—somewhat easy to understand. To illustrate, ask yourself thisquestion: At your place of employment, who is the most productiveworker? Yes, we know. It must be you. But is this something you couldprove? We suspect, for many people, this would be difficult. For work-ers in many non-sports industries, measuring worker productivity isdifficult.

Take our profession, college professors. We both think of our-selves as above average professors. But such a self-assessment may bedubious. In fact, a survey at the University of Nebraska revealed that94% of college professors thought they were better teachers than theaverage at that same institution.9 We don’t think this obvious delusionis unique to Nebraska. Neither of us can recall meeting a fellow pro-fessor who thought he or she was below average.

It also turns out that professors are not the only people who over-estimate their abilities. Thaler and Sunstein find evidence of this phe-nomenon in surveys of MBA students, drivers, and new businessowners,10 and this is just a partial list. They go on to note that “unreal-istic optimism is a pervasive feature of human life; it characterizesmost people in most social categories.”11

In sports, though, there’s a brake on this natural tendency. If weasked Jeff Francoeur of the Atlanta Braves how his hitting in 2008compared to the league average, Francoeur would be hard pressed toargue he was above average. With respect to most of the standardmeasures of hitting performance, Francoeur was below average.Likewise, Francoeur’s teammate Chipper Jones can be pretty confi-dent that he really was an above-average hitter in 2008. Again, that’swhat the stats indicate.12

Because sports come with numbers, evaluating worker perform-ance in sports would seem to be easier. Consequently, the path to suc-cess would seem—relative to what’s seen in other industries—easier


to navigate. Unfortunately, there are a few stumbling blocks on thepath to victory.

The stumbling blocks can be separated into two broad categories.First, numbers have to be understood. Coaches and general man-agers can see the numbers associated with each player’s performance.But how these numbers connect to wins is not always appreciated.Even if the numbers were understood, though, another stumblingblock gets in the way. Understanding the past doesn’t have muchvalue if the past can’t predict the future. Some numbers in sports aresimply inconsistent across time. When that’s the case, following theunpredictable numbers makes the path to victory hard to find.

What the numbers mean for the present and future is the founda-tion of our story. But before we get to that story, we need to address afundamental objection to any sports analysis offered by academics.Specifically, is it likely that academics would be able to say anythingthat the “experts” employed in the sports industry don’t already know?

Crunchers, “Experts,” and the Wrathof Randomness

Even if you don’t believe people are perfectly rational, you mightstill expect decision-makers in sports—where there is an abundanceof information, clear objectives, and severe consequences for fail-ure—to get it “right.” After all, these people are the “experts.” Thereis no reason to think that some college professors armed with a sliderule can do any better.

Let’s respond to that by noting that neither of us owns a slide rule(or knows how to use one). We do, though, have spreadsheets andsome fairly sophisticated econometric software. There are a numberof examples where people armed with such tools can see things that“the experts” miss. Some of our favorite examples come from placesas diverse as the wine industry,13 analysis of Supreme Court

6 STUMBLING ON WINS

decisions,14 and the treatment of heart patients in the emergencyroom.15 In essence, it appears that human beings—who are not actu-ally lightning calculators—tend to lose in a contest against actuallightning calculators.16 Such an outcome is observed whether or notthe human being is an “expert.”

Related to the obvious point that people are not lightning calcula-tors is a classic finding in psychology. People in sports often claim theycan simply watch a player during a game and “know” if he is good orbad. The seminal work of George Miller, though, has shown that thehuman mind can only track about seven items at one time.17 In sports,though, a multitude of events are happening throughout the contest.All these events not only have to be seen and noted, the impact ofthese factors on wins must be ascertained. To claim that you can sim-ply watch a player and see his or her overall contribution to wins sug-gests that you believe your mind can do something that researchsuggests is difficult. Despite the limitations of personal observation,though, human beings still tend to believe the analysis based on thisapproach is correct. Such overconfidence can often cause people toignore contradictory information.

Statistical analysis, though, can overcome these issues. Spread-sheets and statistical software can evaluate more games than a personcan ever personally observe. These evaluations can also allow us tolook past the “most dramatic factors” and identify which factors trulymatter most in terms of wins. Furthermore, the analysis can also eas-ily change as new data arrives. Perhaps most importantly, statisticalmodels come with confidence intervals.18 In other words, statisticalmodels can assess the quality of the prediction being made. Try get-ting that kind of service from a human expert!

Number crunching does more than offer better explanations thanwhat we get from “experts.” It can also tell us when there really isn’tan explanation. In other words, number crunching can help us seewhen a process is inherently random.


Let’s illustrate this last point with an oddity from the Super Bowl.As of 2009, the National Football Conference (NFC) team has wonthe coin toss at the Super Bowl for 12 consecutive years. Such a streakclearly indicates that the NFC has some secret that allows it to betterpredict coin tosses; and the American Football Conference (AFC)better do some work if it hopes to close the “coin toss predicting gap.”Then again, maybe there’s another possibility. Flipping a coin is a ran-dom process.19 Even if you flipped a coin 12 times in a row with thesame result, the process is still random. The outcomes don’t tell usanything about the skill level of the NFC teams. This point should beobvious, since predicting a coin toss is not an actual skill.

This simple story highlights an additional advantage of analyzingsports data, and another potential pitfall for decision-makers. Somenumbers that we associate with an athlete represent the skills of theperformer. Other numbers, though, are not about a player’s skill, butinstead are determined by the actions of the player’s teammates (orcoaching or some random process). The analysis of numbers canactually clue us in on the skills versus non-skills argument. In theabsence of such analysis, though, a decision-maker can actually sufferfrom the “wrath of randomness.” Specifically, a decision-maker canbe fooled by numbers that are as reliable predictors of the future asthe numbers generated by our coin-flipping game. When that hap-pens, money can be wasted on players who are not really helping. Oron the flip side, a player with some supposedly poor numbers can beremoved from the roster when in fact the player is actually helpingthe team win.

A Century of Mistakes in Baseball

Although the “wrath of randomness” does rear its head in thestudy of sports, often the numbers do tell a story. Let’s start with agreat story that reveals a century of mistakes in Major League Base-ball (MLB).

8 STUMBLING ON WINS

In 1997, the Oakland A’s ranked toward the bottom in MajorLeague Baseball, in respect to both team payroll and winning per-centage. The next season, Billy Beane became general manager, andpart of this story stayed pretty much the same. Specifically, the lack ofspending on players didn’t change. What did change were the out-comes achieved by the A’s. From 1999 to 2002, only the New YorkYankees, a team that spent three times more on playing talent thanBeane, managed to win more games in the American League. Theterm “more” is a bit misleading. The Yankees actually won only twomore games than the A’s across these four seasons.

How was this possible? It’s been argued20 that the key was Beane’sability to recognize specific inefficiencies in baseball’s labor market.Such inefficiencies allowed Beane to pick up talent that was bothcheap and productive.21

At least, that’s the story that’s been told. For the empirical evi-dence supporting this tale, we turn to the work of Jahn Hakes andRaymond Sauer. These economists decided to investigate whetherthe baseball player market was, as they say, “grossly inefficient.”Before we get to their answer, however, let’s briefly describe anefficient labor market. A basic tenet in economics is that workers arepaid in line with their expected productivity, that is, workers who areexpected to be the most productive get paid the most. This suggeststhat baseball players who are expected to perform the best are paidthe highest salaries (at least, once they become free agents). In aworld where some teams are “rich” and others “poor,” the best play-ers typically end up on teams that have the ability to pay the most. Inother words, we would expect the Yankees—or the “rich” team—toget the best talent, and a “poor” team like the Oakland A’s should endup with the less capable players.

The key to the above reasoning is the phrase “ballplayers who areexpected to be the most productive.” This tells us that having moneyisn’t enough. Teams have to be able to identify the “most productive”players. If one team can do a better job at identifying the “most


productive,” then that team might be able to field a very good teamthat’s not very expensive.

To see if the Oakland A’s actually followed this blueprint, Hakesand Sauer needed to connect three dots:

• They needed to uncover how various performance characteris-tics impact wins in Major League Baseball.

• They needed to figure out what individual teams were willingto pay for each performance characteristic.

• They needed to determine whether the salaries that variousperformance characteristics command is consistent with howthose measures impact wins.

To cut to the chase, Hakes and Sauer found that “...hitters’salaries during this period (2000-2003) did not accurately reflect thecontribution of various batting skills to winning games.” Further-more, “this inefficiency was sufficiently large enough that knowledgeof its existence, and the ability to exploit it, enabled the OaklandAthletics to gain a substantial advantage over their competition.”22

How did they reach this conclusion? First, data was collected onteam winning percentage, team on-base percentage,23 and team slug-ging percentage24 for all 30 MLB teams from 1999 to 2003. They thenran a simple regression.

Okay, we get ahead of ourselves. What’s a “simple regression?”Regressions25 are essentially the test tubes of economics. When achemist seeks to understand the world, he or she steps into a labora-tory and starts playing around with test tubes. These test tubes allowa chemist to conduct controlled experiments. Hakes and Sauer,though, could not conduct a controlled experiment with MajorLeague Baseball (at least, Major League Baseball probably wouldn’tlet them do this). What they could do, though, is employ regressionanalysis. This is simply a standard technique economists employ touncover the relationship between two variables (like player salary andon-base percentage), while statistically holding other factors constant.

10 STUMBLING ON WINS

When properly executed, regression analysis allows one to see if therelationship between two variables exists; or more precisely, if therelationship between two variables is statistically significant.

Beyond statistical significance, we can also measure the economicsignificance of a relationship,26 or the size of the impact one variablehas on another. Consider how on-base percentage and slugging per-centage relate to team wins. Hakes and Sauer found both to be statis-tically significant. On-base percentage, though, had twice the impacton team wins. Such a result suggests that players should be paid morefor on-base percentage. The study of salaries, though, suggested thatprior to 2004, it was slugging percentage that got a hitter paid. In fact,in many of the years these authors examined, on-base percentage wasnot even found to have a statistically significant impact on playersalaries.

After 2004, though, the story changed.27 An examination of datafrom 2004 to 2006 reveals that on-base percentage had a bigger impacton player salaries than slugging percentage. In other words, an ineffi-ciency exploited by Billy Beane was eventually eliminated.28

It’s important to note, though, how long this took. The NationalLeague came into existence in 1876. All of the data necessary to cal-culate on-base percentage was actually tracked that very first seasonin the 19th century. However, it was not until the 21st century—orafter more than 100 years—that these numbers were understood bydecision-makers in baseball. It appears that decision-makers in base-ball made the same mistake in evaluating talent year after year, andthis continued for a century. Such a tale suggests that maybe all thosefans are on to something. Maybe coaches and general managers arecapable of repeating the same mistakes.

Of course, one story from the real world of sports doesn’t make apoint. What we need is a multitude of stories. And that’s what weprovide. The stories we tell give insight into how free agents areevaluated, how teams make decisions on draft day, and even howchoices are made on game day. We even present evidence that the


evaluation of coaches in the National Basketball Association (NBA)is less than ideal.

All of these tales from the world of sports tell one very importantstory. Decision-making is not often as rational as traditional econom-ics argues. And that story has an impact on our understanding of bothsports and economics.

INDEXNote: Page numbers followed by n

are located in the Endnotes.

AAbbott, Henry, 125, 180n, 205nAbdul-Jabbar, Kareem, 84, 131, 206nAbdur-Rahim, Shareef, 94-95ACC (Atlantic Coast Conference), 49Adelman, Rick, 123adjusted field goal percentage

(NBA), 16adjusted plus-minus statistic,

183-184nAdjusted Production per 48 minutes

played (AdjP48), 203nAFL (American Football League), 50African-American quarterbacks

history of, 50-55pay discrepancy between black and

white quarterbacks, 63-66performance of, 55-63

age and performance, 126-129, 205nAguirre, Mark, 88-89Aikman, Troy, 66, 168-170Albeck, Stan, 124All-Rookie Team (NBA), voting

factors for, 27Allen, Ray, 84Allmen, Peter Von, 189nAlmond, Morris, 24American Football League (AFL), 50

225

American International Group, Inc., 174n

Anderson, Dave, 202nAnderson, Ken, 88-89, 168-170Ariely, Dan, 173nAriza, Trevor, 178nArizona Cardinals, 113Armstrong, Hilton, 152-153Ashenfelter, Orley, 174nassigning wins and losses, 47-48assists (NBA), 16Atlantic Coast Conference (ACC), 49Auerbach, Red, 23, 133, 179-180nAyres, Ian, 174n

BBanks, Don, 188nBanks, Tony, 52-53, 71-73Barkley, Charles, 194nBarzilai, Aaron, 184nbase stealing, efficiency of, 103-106,

198-199nBaseball-Reference.com, 181nbaseball. See Major League BaseballBasketball-Reference.com,

176n, 182nbasketball. See National

Basketball AssociationBasketballValue.com, 183nBatch, Charlie, 52-53batting average, 33, 135

226 INDEX

Beane, Billy, 8, 135Belfour, Ed, 43Belichick, Bill, 200nBell, Bert, 68Bellisari, Steve, 74Bender, Patricia, 178n, 195nBennell, Rick, 25Benson, Kent, 88-89Bias, Len, 88-89Bibby, Mike, 88-89Biedrins, Andris, 45Bird, Larry, 42, 84, 180nBirdsong, Otis, 88-89Birnbaum, Phil, 190nblack quarterbacks

history of, 50-55pay discrepancy between black and

white quarterbacks, 63-66performance of, 55-63

Blake, Jeff, 52-53, 73Blass, Asher, 181nBledsoe, Drew, 71-73blocked shots (NBA), 16BMI (Body Mass Index), 191nBockerstaff, Bernie, 205nBody Mass Index (BMI), 191nBogut, Andrew, 93, 195nBonds, Barry, 199nBoston Celtics, 84, 121Bowen, Ryan, 152-153Bowie, Sam, 88-90, 194nBradbury, J. C., 33, 127, 181n, 206nBradley, Shawn, 88-89Bradshaw, Terry, 57Brady, Tom, 168-170Brand, Elton, 88-89Brees, Drew, 73Briiks, Aaron, 52-53Briscoe, Marlin, 51Brock, Lou, 106Brodeur, Martin, 39-42Brodie, John, 168-170Brooklyn Dodgers, 68Brown, Devin, 152-153Brown, Kwame, 88-89Brown, Larry, 25-26, 124Brown, Shannon, 25-26

Bryant, Bear, 202nBryant, Kobe, 44, 121Bucks Diary, 149Buffalo Bills, signing of James Harris,

51, 56-57Bulger, Marc, 73Burford, Seth, 74Burger, John, 101Burke, Brian, 201nButler, Rasual, 152-153

CCalderon, Jose, 45Camby, Marcus, 44, 88-89Campbell, Jason, 52-53Carlisle, Rick, 204-205nCarr, David, 71-74, 77Carroll, Joe Barry, 88-89Carter, Quincy, 52-53Carter, Virgil, 168-170center averages (NBA), 178nChamberlain, Wilt, 23Chandler, Tyson, 88-89, 152-153Chappell, Mike, 191nChicago Bulls, 84, 89, 130Cincinnati Bengals, signing of Jeff

Blake, 53Cleveland Browns, signing of Bill

Willis and Marion Motley, 50Clifton, Nat, 186ncoaches

conflicting messages to players, 23-28

impact on performance, 119-126,203-205n

coefficient of variation, 185ncoin tosses, 7, 175nColeman, Derrick, 88-89, 94-95Collins, Doug, 205ncompetitive balance, 68, 189nconsistency

consistency across sports, 38of hitters/pitchers in MLB, 34-35importance of, 181n

Continental Basketball Association, 176n

Cooper, Chuck, 186n

INDEX 227

correlation coefficient, 184nCouch, Tim, 71, 77Craft, Kim, 185nCrawford, Jamal

acquisition by New York Knicks, 19average minutes per game in New

York Knicks, 177ngames played as New York

Knicks, 177nperformance before coming to New

York, 30-31performance in New York, 31salary with New York Knicks,

29, 178nCross, P., 174nCulpepper, Daunte, 52-53, 73Cummings, Terry, 88-89Cunningham, Randall, 51-53Curry, Eddy

acquisition by New York Knicks, 19games played as New York



29, 178nCurtis, Joseph, 43

DDaniels, Antonio, 152-153Daugherty, Brad, 88-89Davey, Rohan, 74Davis, Antonio, 178nDefensive Independent Pitching

Statistics (DIPS), 35Delhomme, Jake, 73Denver Broncos, 51, 201nDetroit Lions, drafting of Joey

Harrington, 74Detroit Pistons, 177nDiminishing Returns, Law of,

130-132DIPS (Defensive Independent

Pitching Statistics), 35Dobson, H. A., 135

Doman, Brandon, 74draft

MLB performance and, 100-102NBA draft

draft position and performance,93-100, 196-198n

intentional losses to securebetter draft position, 88-93, 194n

NBA performance and, 88-89structure of, 87

NFL draftfactors determining draft

position, 78-80history of, 68-69overview, 67player compensation and draft

order, 73-77player productivity and draft

order, 69-73purpose of, 69surplus value of drafted

players, 69-70telecast of, 191n

Drexler, Clyde, 194nDruckenmiller, Jim, 71Dubner, Stephen, 194nDumars, Joe, 84Duncan, Tim, 44, 83, 88-90, 94-95Dunleavy, Mike, 204nDunningham, Randall, 168-170

EEarned Run Average (ERA), 34-35Easterbrook, Gregg, 107economic significance of

relationships, measuring, 10economic value of sports

industry, 174nEdmonton Eskimos, 58effective field goal percentage

(NBA), 176nefficiency in sports play

base stealing (MLB), 103-106, 198-199n

decision-making on fourth down(NFL), 106-113, 200-201n

228 INDEX

“hot hand” phenomenon (NBA),115-116, 202n

kickers, evaluating performance of(NFL), 113-115, 201n

playing time, 116-118walks (MLB), 106, 199n

efficient labor markets,characteristics of, 8

82games.com, 149, 182nElam, Jason, 201nEllison, Pervis, 88-89Elway, John, 58, 66Ely, Melvin, 152-153ERA (Earned Run Average), 34-35Esiason, Boomer, 168-170Evans, Vince, 51-53Evolution of the Draft and

Lottery, 194nEwing, Patrick, 88-89

FFasani, Randy, 74Favre, Brett, 65, 73FBS (Football Bowl

Subdivision), 193nFCS (Football Championship

Subdivision), 193nFerry, Danny, 88-89field goal attempts (NBA), 16Fitch, Bill, 122, 204nFitzsimmons, Cotton, 124Follis, Charles W., 50Football Bowl Subdivision

(FBS), 193nFootball Championship Subdivision

(FCS), 193nfootball. See National Football

League (NFL)Ford, Chris, 123-124, 205nFord, Phil, 88-89forward averages (NBA), 178nfourth down decision-making,

106-113, 200-201nFouts, Dan, 168-170Fowler, J. W., 186nFox, Justin, 194nFrancis, Steve, 88-89, 178n

Francoeur, Jeff, 4, 174nFratello, Mike, 124free agents

max players, 23NBA performance factors and free

agents’ salaries, 20-23, 178-179nNHL goalies study, 46, 185-186n

free throw attempts (NBA), 16free throws (NBA), 202Frefotte, Gus, 72Froese, Bob, 43

GGame Score (NBA), 156-157Garcia, Jeff, 72-73, 168-170Garnett, Kevin, 44, 84, 94-95Garrard, David, 52-53, 74-75, 190nGasol, Pau, 44, 203ngate revenue (NBA), 92, 195nGentry, Alvin, 204nGeorge, Jeff, 71Gerrard, Bill, 175nGibbs, Joe, 106Gilliam, Armon, 88-90Gilliam, Joe, 51-53, 57, 187nGilmore, Artis, 130-131, 206nGilovich, Thomas, 115Gladwell, Malcolm, 175ngoalies (NHL), evaluating

performance of, 39, 42-47Goals Against Average (NHL), 45Goldman, Lee, 175nGoodrich, Gail, 89Goukas, Mike, 204nGrant, Horace, 84Gray, Quinn, 52-53Green, Trent, 168-170Greenwood, Dave, 88-90Griese, Brian, 168-170Griffith, Darrell, 88-89Grove, William, 175n

HHaden, Pat, 187nHakes, Jahn, 8Hamilton, Richard, 25

INDEX 229

Hanners, Dave, 26Hardaway, Anfernee, 94-95, 178nHardaway, Tim, 94-95Harrington, Joey, 74, 77Harris, Al, 204nHarris, James, 51-53, 56-57, 168-170Hart, Jim, 168-170Hasek, Dominik, 43heart attacks, diagnosing via statistical

model, 175nHeeran, Dave, 156help factors (NBA), 16Henderson, Rickey, 106Hextall, Ron, 43Hill, Grant, 94-95hitters (MLB)

age and performance, 127consistency of, 34-35

Hockey-Reference.com, 182n, 185nhockey. See National Hockey LeagueHollinger, John, 144, 156, 176nHones, Bert, 168-170Horn, Keith van, 88-90Horowitz, Ira, 202nHouston Oilers, signing of Warren

Moon, 59Houston Rockets, 90Houston Texans, drafting of David

Carr, 74Howard, Dwight, 44, 88-89, 94-95Hrudey, Kelly, 43Huard, Damon, 168-170Huizinga, John, 202nHumphreys, Brad, 174n

IAn Inquiry into Nature and Causes of

the Wealth of Nations (Smith), 120instrumental rationality, 173nIverson, Allen, 88-91, 195n, 206n

JJackson, Phil, 120-125, 132, 203n,

205nJackson, Tarvaris, 52-53James, Bill, 135-136, 175n, 198n

James, Jerome, 178nJames, LeBron, 44, 83, 88-90, 94-95James, Mike, 152-153Johnson, Kevin, 196nJohnson, Larry, 88-89Johnson, Magic, 42, 83-84, 88-90, 121Jones, Chipper, 4, 174nJones, K. C., 123Jordan, Eddie, 204nJordan, Michael, 42, 84, 121,

194n, 203nJoseph, Curtis, 42

KKarl, George, 122, 204nKelley, Kevin, 107Kelly, Jeff, 74Kelly, Jim, 66kickers (NFL)

evaluating performance of,113-115, 201n

Kidd, Jason, 44, 88-89Kilmer, Billy, 168-170King, Peter, 188nKing, Shaun, 52-53Kiprusoff, Miikka, 43Kirilenko, Andrei, 94-95Kitna, Jon, 190nKittner, Kurt, 74Klingler, David, 71Kostka, Stanislaus, 68, 80-81, 189nKoufos, Kosta, 25Krautmann, Anthony, 189nKuper, Simon, 207n

LLA Lakers, 84, 203nlabor markets

characteristics of efficient labormarket, 8

market for sports executives, 174nLaimbeer, Bill, 177nLandry, Greg, 168-170Lane, Ferdinand, 135Law of Diminishing Returns, 130-132

230 INDEX

Lead to Succeed: 10 Great Traits ofLeadership in Business and Life(Pitino), 203n

Lee, David, 44Leftwich, Byron, 52-53Lehrer, Jonah, 198nLemon, Cleo, 52-53Lewin, David, 193nLewis, Michael, 175n, 201nlimitations of personal observation, 6Lindbergh, Pelle, 43Lindsey, George, 182n, 199nLinear Weights, 182n, 207nLloyd, Earl, 186nLos Angeles Rams

signing of James Harris, 51signing of Kenny Washington, 50

lossesassigning, 47-48intentional losses to secure better

draft position, 88-93, 194nLoughery, Kevin, 123-124, 205nLucas, Ray, 52-53Luhn, Steve, 180nLuongo, Roberto, 43luxury tax in National Basketball

Association (NBA), 180n

MMacaulay, Fred, 194nMadden, John, 199nMadden NFL 2008®, 107, 199nMagliore, Jamaal, 94-95Majerle, Dan, 94-95Major League Baseball (MLB)

batting average, 135draft and MLB performance,

100-102efficiency of base stealing, 103-106,

198-199nefficiency of walks, 106, 199nevaluating player performance in

age, 127consistency of hitters and

pitchers, 34-35DIPS (Defensive Independent

Pitching Statistics), 35

ERA (Earned Run Average), 34-35

identifying the most usefulnumbers, 33-35

New York Yankees, 8Oakland A’s case study, 7-10on-base percentage

definition of, 175nimpact on team wins, 10importance of, 135

racial integration, 49relationship between payroll and

wins, 13-14repeated mistakes by

decision-makers, 136slugging percentage

definition of, 175nimpact on team wins, 10

Manning, Danny, 88-89Manning, Eli, 71, 77Manning, Peyton, 63, 71, 73, 168-170Marbury, Stephon

acquisition by New York Knicks, 15, 19

career averages, 16-18games played as New York

Knicks, 177non his style of play, 23performance before coming to New


29, 178nMarino, Dan, 168-171Marion, Shawn, 94-95Marks, Dean, 152-153Marshall, George Preston, 187nMartin, Andrew, 174nMartin, Kenyon, 88-89Massey, Cade, 69Mathletics (Winston), 207nmax players (NBA), 23Mays, Dave, 51-53, 187nMcCarthy, Ryan, 205nMcCloskey, Deirdre, 175nMcCown, Josh, 74-75McCracken, Voros, 182n

INDEX 231

McDyess, Antonio, 45, 88-89McGwire, Dan, 71McHale, Kevin, 84McNabb, Donovan, 52-53, 65,

168-170, 188nMcNair, Steve, 52-53, 65, 71, 73,

168-170McNeal, Don, 107measuring Wins Produced

for NBA playersadjusting for position played,

150-151adjusting for production of

teammates, 148-150alternative statistics, 156-157basics of regression analysis,

141-143calculating value of player’s

production, 148calculating WP48 and Wins

Produced, 151-154modeling wins in NBA, 143-148objections, 158-159Win Score and PAWS48,

154-156for NFL players, 161-162, 166-167,

170-171media coverage of sports industry, 3Meehl, Paul, 175nMiami Dolphins, 106Miles, C. J., 25Milicic, Darko, 88-90Miller, George, 6Miller, Mike, 44Ming, Yao, 88-89, 94-95Minnesota Golden Gophers, 68, 189nMinnesota Vikings, signing of Warren

Moon, 60Mirabile, Mac, 192nMLB. See Major League Baseballmodeling wins in NBA, 143-148Moe, Doug, 205nMoiso, Jerome, 196nMontana, Joe, 58, 168-170, 187nMoon, Warren, 51-53, 58-61, 63Morrison, Adam, 26, 180n

Morton, Craig, 168-170Moseley, Mark, 106Motley, Marion, 50Motta, Dick, 122Mourning, Alonzo, 88-89Murphy, Troy, 44Mutombo, Dikembe, 94-95The Myth of the Rational Market

(Fox), 194n

NNagurski, Bronco, 68, 81Nall, Craig, 74Namath, Joe, 168-170, 187nNational Basketball Association

(NBA)All-Rookie Team, voting factors

for, 27Boston Celtics, 84, 121box score numbers, 16Chicago Bulls, 89, 130coaches

conflicting messages to players,23, 25-28

impact on performance, 120-126, 203-205n

Detroit Pistons, 177ndraft

draft position and performance,93-100, 196-198n

intentional losses to securebetter draft position, 88-93, 194n

and NBA performance, 88-89structure of, 87

evaluating player performance inage, 126-129, 205naverage center numbers, 178naverage power forward

numbers, 178naverage shooting guard

numbers, 177nfactors determining player

productivity, 98factors producing wins,

132-133

232 INDEX

Game Score, 156-157“hot hand” phenomenon,

115-116, 202nmeasuring Wins Produced,

141-159NBA Efficiency, 156-157performance factors and free

agents’ salaries, 20-23, 178-179n

playing time, 116-118relationship between

possessions utilized andefficiency, 22, 179n

shooting efficiency, 206nteammates and performance,

130-132, 206ntop 20 players in 2008-09,

44-45gate revenue, 92, 195nHouston Rockets, 90LA Lakers, 84, 203nluxury tax, 180nmax players, 23New Orleans Jazz, 89New York Knicks case study,

14-20, 28-31Pareto Principle, 83-84, 87Philadelphia 76ers, 205-206nrelationship between payroll and

wins, 14repeated mistakes by

decision-makers, 136-138salary cap, 180nSan Antonio Spurs, 130top three producers of wins on NBA

champions 1978-2008, 87National Football League (NFL)

Arizona Cardinals, 113Buffalo Bills, signing of James

Harris, 51, 56-57Cincinnati Bengals, signing of Jeff

Blake, 53Cleveland Browns, signing of Bill

Willis and Marion Motley, 50coin tosses, 7decision-making on fourth down,

106-113, 200-201n

Denver Broncos, 51, 201nDetroit Lions, drafting of Joey

Harrington, 74draft

factors determining draftposition, 78-80

history of, 68-69overview, 67player compensation and draft

order, 73-77player productivity and draft

order, 69-73purpose of, 69Scouting Combine, 78-79surplus value of drafted

players, 69-70telecast of, 191n

evaluating player performance inidentifying the most useful

numbers, 35, 37-39kickers, 113-115, 201nmeasuring Wins Produced,

161-162, 166-167, 170-171performance of black

quarterbacks, 55-63Quarterback Rating, 161

Houston Oilers, signing of WarrenMoon, 59

Houston Texans, drafting of DavidCarr, 74

Los Angeles Ramssigning of James Harris, 51signing of Kenny

Washington, 50Minnesota Vikings, signing of

Warren Moon, 60quarterbacks

compensation and draft order,75-76


history of black quarterbacks,50-55

NFL Quarterback WonderlicScores, 192n

INDEX 233

pay discrepancy between blackand white quarterbacks, 63-66

performance of blackquarterbacks, 55-63

productivity and draft order,71-73, 77

recent Hall-of-Famequarterbacks, 59

Relative Plays, 190nRelative WP100, 191n

racial integrationhistory of black quarterbacks,

50-55overview, 49-50pay discrepancy between

black and white quarterbacks,63-66


relationship between payroll andwins, 14

repeated mistakes by decision-makers, 137-138

San Diego Chargers, signing ofJames Harris, 51

Scouting Combine, 191nSuper Bowl XVII, 106Washington Redskins, 13

National Hockey League (NHL)evaluating worker performance in

goalie performance, 39, 42-47identifying the most useful

numbers, 37-39New York Rangers, 13relationship between payroll and

wins, 14NBA. See National Basketball

AssociationNelson, Don, 124, 204-205nNet On Court/Off Court, 182nNet48, 182nNew Orleans Jazz, 89New York Knicks, 14-31, 138New York Rangers, 13New York Yankees, 8, 13

NFL Quarterback Wonderlic Scores, 192n

NFL. See National Football LeagueNorth, Douglass, 173n

OO’Brien, Jim, 124, 205nO’Donnell, Neil, 73O’Neal, Shaquille, 88-89, 94-95,

121, 203nO’Sullivan, J. T., 74Oakland A’s, 7-10Oakley, Charles, 94-95offensive scoring (NFL), 163-164Okafor, Emeka, 88-89Olajuwon, Hakeem, 83, 88-90, 194nOliver, Dean, 144, 177n, 179n,

184n, 197nOlowokandi, Michael, 88-89on-base percentage

definition of, 175nimpact on team wins, 10importance of, 135

OPS (on-base percentage plusslugging percentage), 33

PPA (Possessions Acquired), 145-146Palmer, Carson, 71-73Palmer, Pete, 207nPareto Principle, 83-84, 87, 194nPareto, Vilfredo, 83Parish, Robert, 84Pate, Wes, 74, 190nPaul, Chris, 44, 94-95

calculating Wins Produced for, 148-154

PAWS48 (Position Adjusted WinScore per 48 minutes), 154-156

payroll expendituresBoston Celtics, 20in National Football League (NFL)


pay discrepancy between black and white quarterbacks,63-66

234 INDEX

player compensation and draftorder, 73-77

Miami Heat, 20New York Knicks, 20, 29, 178nrelationship between payroll and

wins, 13-14San Antonio Spurs, 20

Payton, Gary, 88-89Peete, Rodney, 51-53Pennington, Chad, 71PER (Player Efficiency Rating), 156personal fouls (NBA), 16personal observation, limitations of, 6Peterson, Morris, 152-153Philadelphia 76ers, 205-206nPhiladelphia Eagles, 68Phillips, Bum, 119, 122, 202nPierce, Paul, 84, 94-95Pippin, Scottie, 84pitchers (MLB)

age and performance, 127consistency of, 34-35DIPS (Defensive Independent

Pitching Statistics), 35Pitino, Rick, 120-124, 203nPlayer Efficiency Rating (PER), 156player performance, evaluating

adjusted plus-minus statistic, 183-184n

age and performance, 126-129, 205nassigning wins and losses, 47-48consistency, 34-35, 38identifying the most useful number,

33-35, 38-39NBA “hot hand” phenomenon,

115-116, 202nNFL kickers, 113-115, 201nNHL goalies, 42-47Oakland A’s case study, 7, 9-10overview, 4-5performance of black quarterbacks,

55-63playing time, 116-118plus-minus statistic, 38, 182nshooting efficiency, 206n

teammates and performance, 130-132, 206n

Wins Produced for NBA playersadjusting for position played,

150-151adjusting for production of

teammates, 148-150alternative statistics, 156-157basics of regression analysis,



Produced, 151-154modeling wins in NBA, 143-148objections, 158-159Win Score and PAWS48,

154-156Wins Produced for NFL players,

161-162, 166-167, 170-171playing time, 116-118plus-minus statistic, 38, 182npoints scored (NBA), 16Points-Per-Shot (PPS) in NBA, 176nPollard, Fritz, 186nPopovich, Gregg, 124Posey, James, 152-153Position Adjusted Win Score per 48

minutes (PAWS48), 154-156position played, adjusting for (NBA

players), 150-151possessions (NBA), 16, 176nPossessions Acquired (PA),

145-146, 176nPossessions Employed, 176npower forward averages (NBA), 178nPPS (Points-Per-Shot) in NBA, 176nPrater, Matt, 201nPredictably Irrational (Ariely), 173nPrice, Paul, 174nproathletesonly.com, 189nProvidence College, 121Przybilla, Joel, 44Pulaski Academy, 107

INDEX 235

QQB Score, 166quarterbacks (NFL)

black quarterbackshistory of, 50-55pay discrepancy between


performance of, 55-63compensation and draft order, 75-76evaluating performance of

identifying the most usefulnumbers, 35

measuring Wins Produced, 161-162, 166-167, 170-171


factors determining draft position,78-80

NFL Quarterback WonderlicScores, 192n

productivity and draft order, 71-73, 77

QB Score, 166Quarterback Rating, 161recent Hall-of-Fame

quarterbacks, 59Relative Plays, 190nRelative WP100, 191n

Quinn, Kevin, 174n

Rracial integration in sports

black quarterbackshistory of, 50-55pay discrepancy between


performance of, 55-63overview, 49-50

Rackers, Neil, 113-114, 201-202nRamsay, Jack, 205nRamsey, Patrick, 74

Randolph, Zachacquisition by New York Knicks, 19performance before coming to New

York, 30-31performance in New York, 31salary with New York Knicks, 29

randomness, wrath of, 7rationality of decision-making

definition of rationality, 3-4instrumental rationality, 173nlimitations of personal observation, 6overview, 2repeated mistakes by decision-

makers, 136-139rebounds (NBA), 179nregression analysis, 141-143regressions, 9relationships, measuring economic

significance of, 10Relative Plays (NFL

quarterbacks), 190nRelative WP100 (NFL

quarterbacks), 191nRhoden, William, 14Richardson, Jason, 26Richardson, Quentin

acquisition by New York Knicks, 19games played as New York


York, 30-31, 177nperformance in New York, 31salary with New York Knicks, 29,

178nRiggins, John, 107Riley, Pat, 120-122, 203nRivers, Doc, 24Robinson, David, 88-90, 94-95, 196nRobinson, Glenn, 88-89Robinson, Jackie, 198nRodman, Dennis, 94-95, 177nRoethlisberger, Ben, 73, 167Romer, David, 108, 200nRondo, Rajon, 44, 84Rose, Malik, 178n

236 INDEX

Rosenbaum, Dan, 184nRottenberg Invariance

Principle, 189nRottenberg, Simon, 189nRovell, Darren, 194nRoy, Brandon, 44Roy, Patrick, 43running backs (NFL), evaluating

performance of, 36Ruseski, Jane, 174nRussell, JaMarcus, 52-53Ruth, Babe, 103Rypien, Mark, 168-170

SSacred Hoops (Jackson), 203nSagarin, Jeff, 184nsalary caps in National Basketball

Association (NBA), 180nsample variance, 179nSampson, Ralph, 88-89San Antonio Spurs, 130San Diego Chargers, signing of James

Harris, 51Sauer, Raymond, 8Saunders, Flip, 124Schatz, Aaron, 202nscoring factors (NBA), 16Scouting Combine (NFL),

78-79, 191nShelby Athletic Club, 50shooting efficiency, 206nshooting guard averages (NBA), 177nShue, Gene, 123-124Shula, Don, 119, 122Shuler, Heath, 71-72Silas, Paul, 205nSiler, Russ, 180nSloan, Jerry, 24, 122slugging percentage

definition of, 175nimpact on team wins, 10

Smith, Adam, 120-122Smith, Akili, 52-53Smith, Joe, 88-89Smits, Rik, 88-89Soccernomics (Szymanski), 207n

sports executives, market for, 174nsports industry, economic value

of, 174nStabler, Ken, 168-170Stafford, Matthew, 76-77standard deviation, 179nstar power, effect on gate revenue,

91, 195nstatistical analysis

advantages of, 6-7of chest pain symptoms, 175ncompared to expert analysis, 175neconomic significance of a

relationship, measuring, 10regressions, 9of Supreme Court justices’

votes, 174nof wine vintage quality, 174n

Staubach, Roger, 58, 168-170stealing bases, efficiency of, 103-106,

198-199nStewart, Kordell, 52-53Stipanovich, Steve, 88-89Stojakovic, Peja, 152-153Strode, Woody, 50Success Is a Choice: Ten Steps to

Overachieving in Business and Life(Pitino), 203n

Sunstein, Cass, 2Super Bowl XVII, 106Supreme Court justices’ vote,

predicting via empirical model, 174n

surplus value, 69-70, 190nSwift, Stromile, 88-89Szymanski, Stefan, 207n

TTarkenton, Fran, 168-170Taylor, Beck, 194nTaylor, Maurice, 178nteammates and performance,

130-132, 148-150, 206nTENDEX model, 156Testaverde, Vinny, 168-170Thaler, Richard, 2, 69Theismann, Joe, 168-170

INDEX 237

Theodore, Jose, 43Thomas, Isiah, 84, 88-89, 123-124,

138, 205ncareer as manager of New York

Knicks, 14-20, 28-32career averages, 16-18ownership of Continental Basketball

Association, 176nThomas, Tim, 43Thompson, Mychal, 88-89Thorn, John, 182nThrower, Willie, 51Tisdale, Wayman, 88-89Trogdon, Justin, 194nTrue Hoop, 180nTversky, Amos, 115

VVallone, Robert, 115Vanbiesbrouck, John, 43variation in winning percentage, 176nVeblen, Thorstein, 2Vick, Michael, 52-53, 71

W-X-Y-ZWade, Dwayne, 44, 121The Wages of Wins (Berri), 18, 177nWalker, Antoine, 122Walker, James, 188nWalker, Moses Fleetwood, 186nWalker, Weldy, 186nwalks (MLB), efficiency of, 106, 199nWallace, Gerald, 44Wallace, Seneca, 52-53Walters, Stephen, 101, 174nWarner, Kurt, 72, 168-170Washington Redskins, 13, 106Washington, Kenny, 50Weaver, Earl, 103Webber, Chris, 88-89Weil, Sandy, 202nWest, David, 152-153Westhead, Paul, 205nWestphal, Paul, 123Wilkens, Lenny, 122Williams, Doug, 51-53, 57, 63, 187n

Williams, Jay, 88-89Williams, Marvin, 93, 195nWillihnganz, Ty, 149Willis, Bill, 50Win Score, 154-156, 177nwine vintage quality, statistical

models of, 174nThe Winner Within: A Life Plan for

Team Players (Riley), 203nWins per 48 Minutes (NBA), 18Wins Produced, 18

assigning wins, 47-48measuring for NBA players

adjusting for position played,150-151

adjusting for production ofteammates, 148-150

alternative statistics, 156-157basics of regression analysis,



Produced, 151-154modeling wins in NBA, 143-

146, 148objections, 158-159Win Score and PAWS48,

154-156measuring for NFL players,

161-162, 166-167, 170-171relationship between payroll and

wins, 13-14Winston, Wayne, 184n, 190n, 207nWolff, Alexander, 186nWonderlic test, 78-79, 191n, 193nWonderlic, Eldon F., 191nWorthy, James, 84, 88-89WP48 (Wins per 48 Minutes), 18,

151-154wrath of randomness, 7Wright, Anthony, 52-53Wright, Julian, 152-153

Young, Steve, 168-170, 187nYoung, Vince, 52-53

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