In the early 2000s, Michael Lewis was investigating a theory.

In 1999, he published The New New Thing: A Silicon Valley Story. Born from his experiences working at Salomon Brothers – American multinational bulge bracket investment bank – Lewis detailed the growing entrepreneurial culture just south of San Francisco at the height of the Internet boom. His interest in Wall Street, investments, and the allocation of finances lead him down a slightly different path of interest: Major League Baseball. 

At the time, player contracts within the MLB had been changing rapidly. To put it into perspective, New York Mets outfielder Darryl Strawberry was the league’s highest paid player in 1991. He made $3.8 million. At the end of the 2000 season, Alex Rodriguez signed a blockbuster ten-year deal with the Texas Rangers worth $252 million. He was the MLB’s highest paid player in 2001 at $22 million – a 479 percent increase from Strawberry’s 1991 contract. Rodriguez wasn’t alone. Twenty three other players in 2001 had penned contracts awarding them at least $10 million. Not a single player before 1997 made $10 million.

Contracts in baseball weren’t just increasing. They were booming out of proportion, but not equally – which caught the interest of Lewis.

While Rodriguez and company were reaping the rewards of baseball’s salary boom, the rest of the field was not nearly being compensated at the same rate. To put this into perspective, the 2001 Rangers had a total salary of $87.8 million. Rodriguez’s contract accounted for 27.8 percent of it. His teammate across the infield – second baseman Randy Velarde – was making just $3.15 million. The only player who came close to Rodriguez was 36-year old Rafael Palmeiro. He made $9 million that season. 

The asymmetry in player salaries created potential class dynamics within big league locker rooms. It’s one thing if you’re a pitcher and Rodriguez makes an error that costs you a run. It’s another when he’s doing this while making more than four times your salary. Lewis decided to investigate the potential problem, but realized with research the players actually weren’t resentful of teammates and opponents earning bigger contracts. The real disparity was between teams, which brought him to the east side of the San Fransisco Bay.

Home of the Oakland Athletics. 

In 2001, three teams had payrolls that exceeded $100 million – New York Yankees, Boston, and Los Angeles. Two teams that season won over 100 games. One of these teams – Seattle – won 116 games in one of the most memorable seasons in league history. Their payroll stood at $74.7 million. The other – Oakland – won 102 games. Their entire team payroll stood at $33.8 million – $2.2 million short of what Yankees pitcher Gerrit Cole will make this season.

When Lewis approached Oakland General Manager Billy Beane about his salary cap heroics, Beane was actually a little surprised. No one had ever asked him about this before, yet it was the single most important question he had ever answered in his life. Lewis had struck gold. It wasn’t until Lewis started to spend more time in the Athletics locker room that he started to realize his newspaper feature was worth a book. What he expected to see did not match up to what he actually saw.

And he wasn’t looking at excel spreadsheets.

During the 2007 NBA Draft, newly hired Houston Rockets GM Daryl Morey made a mistake that completely changed how he evaluated potential NBA prospects.

Over the past several years, Morey had assembled a data driven model for player evaluation inspired by The Bill James Historical Baseball Abstract. The model was Morey’s attempt to eliminate subjective mistakes he noticed throughout scouting departments during his time in the Boston Celtics front office. He wanted to bypass human bias and instead focus on statistics and indicators that strongly correlated with NBA performance. 

Which, as it turns out, was the the thing that ended up costing him a once-in-a-lifetime draft pick. 

Out of all the players Morey evaluated going into the 2007 draft, there was one player his model really liked: Marc Gasol, center from Barcelona, Spain. Gasol, twenty-two, had been playing professionally in Europe. At seven foot one, his size and athleticism made him an intriguing prospect for teams in need of a big man. And then Rockets scouts got their hands on a shirtless picture of Gasol. When they saw his pudgy body, baby face, and jiggly pecs, they decided to give him a nick name: “Man Boobs.” 

Morey’s model might have loved “Man Boobs,” but as a first-year GM he lacked the courage and conviction to argue with his staff. He watched Gasol go 48th overall to the Los Angeles Lakers. The odds of getting an All-Star with this pick is well below one hundred. Since then, Gasol has been selected to not just one – but three All-Star teams (2012, 2015, 2017). In 2013, he won Defensive Player of the Year. In 2015, he added an All-NBA First Team selection.

Over the past decade, only two players – Kevin Durant and Blake Griffin – rank higher than Gasol in terms of draft value. The Rockets didn’t want him because they couldn’t get over how he looked without a shirt on – which is funny, because that’s exactly what sparked Michael Lewis to write his best-selling book Moneyball. Billy Beane didn’t beat the salary cap by recruiting players who “looked” the part. He wanted players that did not – which is why Morey made a new rule for his scouts going forward from the 2007 Draft:

No nick names.

“You would never guess they were professional athletes.”

 

Lewis remembers the moment like it was yesterday.

At the time, he was in the midst of collecting interviews from players throughout the Athletics roster. Beane had put together a collection of misfits that were returning more money on the dollar than any other team in the bigs – and it wasn’t even close. Lewis wanted to figure out why. It didn’t really hit him until he noticed in the locker room just how bad most of the players looked without a shirt on.

Turns out, this wasn’t an accident. It was deliberate. Beane had learned just how much the market under appreciated players who didn’t “look” like professional athletes. As a result, he was able to sign players with bad bodies to favorable deals. You wouldn’t be able to pick someone like Jason Giambi off the street and recognize him as a professional athlete. His body made him look like anything but.

When Beane realized how much physical appearance played into market value, he began to seek players with the opposite effect. If teams weren’t paying players who didn’t “look” the part, those were the kinds of players he needed to target as GM of a small market franchise. The players he signed were viewed as investments. The best way to buy low is to find the players no one is paying for. Those were often the ones who didn’t pass the swimsuit test. Once Lewis recognized this, he realized his idea for a magazine article had grown into a book. 

Most people think of Moneyball as the first domino in baseball’s data revolution, but the motivation behind Lewis’ book wasn’t rooted in spreadsheets. It grew from a fundamental problem when we try to assign value to people. Beane showed us what happens when we’re able to express good judgement in spite of bias. Morey showed us what happens when we fail to express good judgement in the face of bias. Both situations ultimately leave us with an interesting question:

How can we properly value people? 

Before we attempt to answer this question, I think we need to first look at it from a different angle. Let’s start here:

What causes us to under value people? 

Every April, there’s a specific photo that often makes its rounds through social media. This photo was taken over 20 years ago. It’s humorous, but it’s also a specific reminder to all of us who were fooled when we first looked at Tom Brady without a shirt on. He might not have had “man boobs,” but his notorious combine photo did not match up with our perception of what a professional football player should look like. He didn’t run like one. He didn’t jump like one, either. If you saw him at a bar, you wouldn’t be able to tell him apart from your average Joe Schmo coming out of college.

And then we saw just how “good” he looked with pads and the number twelve jersey on. 

While there are a lot of layers that go into scouting and evaluating potential professional prospects, I think there’s one test we need to stop putting a ton of value into. The “eye test” isn’t just a poor indicator of future performance. It’s the one test most of us can’t get past when we’re evaluating potential prospects – and we’re wrong more often than we’re right. You don’t need washboard abs or a “high ass” to play forward, throw a football, or hit a 95 mph baseball. Sport is a testament of skill – not physical appearance. When we put our value into appearances, we are incapable of properly assigning value. We become blinded by bias.

I’m not suggesting physical appearance has a negative correlation, but I think it’s time we stop letting a factor that’s really not that important play a factor in important decisions. If we want to consistently express good judgement in a field littered with poor judgement, we have to avoid relying on poor indications of future performance. The stories from above seem to suggest a compelling narrative – even though we consistently rely on a contrary one. 

Professional athletes come in all sizes, shapes, and body types. The best way to avoid making gross generalizations is to avoid bucketing them in the first place. Our model for high performance is constantly being recalibrated. Russell Wilson was too short and undersized to play quarterback in the NFL, until he won a Super Bowl with Seattle. Jeremy Lin wasn’t “athletic” enough to start in the NBA as an Asian-American, and then he took the league by storm in 2011 as an after thought on a depleted New York Knicks roster. Brady, Giambi, and Gasol all share the same story: They weren’t supposed to do what they did because of how they looked. Our eyes fooled us. 

Richard Feynman, award-winning physicist and Nobel laureate, said it best:

“The first principle is that you must not fool yourself – and you are the easiest person to fool.” 

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