Are Data Nerds the Next Kings of Sport?

(11 min read – 6th March 2017)

When thinking about the “kings of sport”, everyone begins with players such as LeBron James, Michael Jordan, Tom Brady, Babe Ruth, Pelé, Zinedine Zidane and many more. Some others think about famous coaches such as Vince Lombardi (NFL), Phil Jackson (NBA), Greg Popovich (NBA) or even Sir Alex Ferguson (European soccer). But not so many people think about the professionals running the Front Offices of sport franchises. Nevertheless, this status quo has slowly but surely started to change over the last decade, and more and more General Managers are now in the spotlight. Most sport fans have already heard about one of these names: Theo Epstein (Chicago Cubs, MLB), Daryl Morey (Houston Rockets, NBA), Paul DePodesta (Cleveland Browns, NFL), John Chayka (Arizona Coyotes, NHL), Jon Daniels (Texas Rangers, MLB), David Stearns (Milwaukee Brewers, MLB) or Farhan Zaidi (Los Angeles Dodgers, MLB). What do they have all in common? They are all running professional sports clubs, they have all been appointed as GM in their mid 20s or early 30s, they are all graduates from top Institutions such as Harvard, Yale, Cornell, Berkeley or the MIT, and finally, they all have a background related to data analysis (computer science, statistics, economics, etc.). They have come to be known as data nerds.

But what’s so special about these so called data nerds running sport franchises? Is it just a trend or the premises of a crucial shift? Are they just “idiots who believe in analytics […] and who had no talent to be able to play” as Charles Barkley declared in February 2015? Or are they the next kings of sport?


In 2003, Michael Lewis shook the world of baseball with his book Moneyball, The Art of Winning an Unfair GameMoneyball explains how Billy Beane – the Oakland A’s General Manager – built one of the most powerful MLB team after losing three key free agents during the 2001-2002 offseason. The following season the A’s won 103 games, having the best record in the league with the New York Yankees. The most impressive fact is that not only Billy Beane had impressive results after such a difficult offseason, but also he managed to accomplish these only with the 28th payroll in the league (!) with $39.6 million that year – 4 times less than the New York Yankees with $126 million. But Billy Beane had a winning card up his sleeves named Paul DePodesta.

“Before the publication of Moneyball, the market inefficiency based on players who get harder hit balls and walks was wide open! […] Moneyball created a dramatic paradigm shift in this industry.” -Pr. Andy Andres, Sabermetrics Expert, Talks at Google, May 2016.

Billy Beane recruited Paul DePodesta – a 26-year-old, Harvard graduate in Economics – as his assistant General Manager back in 1999. Paul DePodesta helped the A’s build a winning team out of underestimated – and then affordable – players. To find such opportunities, Paul DePodesta and Billy Beane based their work on Bill James’ theories – who has long been the most underestimated sport’s analyst. Bill James started writing his Baseball Abstract back in 1977 when he was just a security guard at a pork and beans cannery. His books were revolutionary at the time, containing lots of in-depth statistics compiled from James’s study of box scores, but no-one within the baseball world wanted to hear what he had to say.

“When I started writing I thought if I proved X was a stupid thing to do that people would stop doing X.  I was wrong.” -Bill James in his 1984 Baseball Abstract book.

It’s only after Moneyball’s worldwide success that Bill James received the credit he deserved. Moneyball acted like a tsunami into the baseball world, ravaging old beliefs about baseball metrics solely focused on batting average and “gut feeling” recruitment. Moneyball influenced many – if not all – MLB teams, but it also created a successful case study, inspiring sports teams far beyond the baseball world.



Indeed, Billy Beane’s success paved the way for a new approach taking root on a data-driven decision making. Even if the A’s didn’t win the World Series, it didn’t take long for another team to achieve it thanks to a similar approach. The Boston Red Sox hired Theo Epstein as their General Manager in November 2002, after that Billy Beane himself turned down the offer. The 28-year-old Yale graduate with no playing or coaching experience became the youngest General Manager in MLB history. Within a few years and thanks to good signings and ingenious trades, he managed to break an 86-year-old curse by winning the 2004 baseball World Series, before winning a second title in 2007. He even took up a bigger challenge by ending the Chicago Cubs’ 108-year title drought in 2016.

“The Red Sox winning under Epstein in 2004, and again in 2007, was like Genghis Khan sweeping across the steppes to conquer Asia, using military tactics that his enemies had never seen before — particularly a huge edge in intelligence operations, what we might now call “data” — and had no idea how to defend against. The Cubs winning in 2016, on the other hand, is more like Napoleon’s Grande Armée conquering Europe, using the same basic military theories as everyone else, but with more discipline and more skill.” -Rany Jazayerli, blogger at The Ringer.

Data analytics has become so crucial for baseball teams over the last decade that it’s more and more difficult to find competitive advantages. Now every team use the same theories and it’s not anymore about which team is using data analytics, but more about which one is not. Baseball is not the only sport where the early adopters won titles which then helped set new standards in their own sport. Indeed, in 2010 Mark Cuban and the Dallas Mavericks used advanced analytics in order to improve their decisions on and off the court, which resulted in the 2011 NBA title against the Miami Heat. In 2014, Germany won the soccer World Cup in Brazil thanks to analytics as well, which helped them identify weaknesses in their opponent’s tactics. More recently the Golden State Warriors shook the world of NBA by taking advantage of the 3-point market inefficiency, and by drafting two of the most talented 3-points shooters in the league with the Splash Brothers: Stephen Curry and Klay Thompson. They won the 2015 title thanks to the incredible Stephen Curry behind the 3-point line, and also broke the legendary 1995-1996 Chicago Bull’s 72-10 regular season record in 2016 with 73 wins.


As data analytics have become more and more necessary to find and exploit market inefficiencies, some people are forgetting that it’s not sufficient to win titles. Indeed, the important part is not how accurate your statistical models can be, but it’s about asking the right questions under good assumptions and if your outputs drive actionable recommendations, then they can deliver results.


A very good example is the case of Charles Reep who, back in the 1950’s, was one of the very first to work on soccer data analysis. Unfortunately, despite having crunched the data into an accurate conclusion which was most goals in soccer come off of plays that were preceded by three passes or fewer, Reep made a terrible mistake which helped ruin decades of English soccer according to the FiveThirtyEight. Reep’s conclusion was known as “not more than three passes” which was supposed to be an efficient tactic, and which defined the English Soccer for a very long time with its myriad of long balls straight into the box. However, what Reep got wrong was the very simple idea that most plays in soccer are of short lengths, and if you look at the probability to score after those actions, it’s actually lower than if you keep control of the ball for more than three passes. Actions of more than three passes are in fact more efficient to score goals, which is the exact opposite of Reep’s conclusion.

“Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.” -Nate Silver, The Signal and the Noise, September 2012.

It’s not only what you can learn from data that needs to be treated with care, but also what you can’t learn from it. In 2008, the Houston Rockets drafted Joey Dorsey during the NBA Draft, following the advice of their predictive model. Daryl Morey, the Rockets GM remembers: Joey Dorsey was a model superstar. The model said that he was like a can’t-miss. His signal was super, super high”.


But Joey turned out to be a weak NBA prospect and his draft revealed big gaps in the Rockets’ predictive model. Joey was a lot older than the other NBA prospects (24 years old), and had only played against weak opponents during his college career which boosted his ratings in the model. But not only had the Rockets drafted a weak prospect, they also missed one player who later became one of the best players of the 2008 NBA Draft: DeAndre Jordan. DeAndre had been a sensational high school player but he didn’t play well during his unique College year. Jordan committed himself to Texas A&M and Coach Billy Gillispie, but Gillispie left to coach at Kentucky, and Jordan ended up having a disappointing year: he played only 20 minutes per game, averaging 7.9 points and 6 rebounds as a part-time starter. As he was not a good College player, Daryl Morey had no way to identify his true value through the data, and it’s the exact reason why he later adapted his decision-making process.


As described by Nate Silver – the statistician guru and founder of the data journalism website – during the 2014 MIT Sports Analytics Conference“Nerds are not magicians with their formulas, they are just better at decision making”. The truth is that data analytics and algorithms have a tendency to outperform human intuitions in a wide variety of circumstances. In his last book, The Undoing Project,  Michael Lewis explains how Daryl Morey is using behavioral economics in order to correct his scouts’ data from cognitive biases. One of the most classic one is the confirmation bias: when a scout would settle on an opinion about a player and then arrange the evidence to support that opinion. The endowment effect is also really important in the world of sport; the hypothesis that people place more value on things they own. In practice, it can prevent teams from accepting valuable deals because they cannot properly value their own players.

“A nerd is a person who knows his own mind well enough to mistrust it.” -Daryl Morey, GM of the Houston Rockets.

Data nerds are really good decision-makers because they have the ability to understand the limits of their analyses, as well as the limits of their own minds. In fact, data nerds are really pragmatic and but also flexible. In January 2016, Paul DePodesta hit the headlines for his impressive flexibility.  After being part of the Moneyball success story in 2002, he became General Manager of the Los Angeles Dodgers at the age of 31 in 2004, and then moved successively to the San Diego Padres and the New York Mets. The headlines in January 2016, however, was not about a move to another MLB team, but the announcement that he would enter the NFL as the Cleveland Browns’ Chief Strategy Officer. Paul DePodesta intends to implement a new decision-making process informed by “60 percent data, 40 percent scouting”, instead of the NFL’s current “70 percent scouting and 30 percent data”. By contrast with the MLB, which is now heavily analytically-driven, the NFL seems to be lagging-behind and Paul DePodesta may be marking a milestone in the world of football more than a decade after changing the world of baseball.


“He’d set out to be a card counter at a casino blackjack table, but he could live the analogy only up to a point. Like a card counter, he was playing a game of chance. Like a card counter, he’d tilted the odds of that game slightly in his favor. Unlike a card counter—but a lot like someone making a life decision—he was allowed to play only a few hands. He drafted a few players a year. In a few hands, anything could happen, even with the odds in his favor.” -Michael Lewis about Daryl Morey, The Undoing Project, December 2016.

Indeed, sports nerds are not magicians as data analytics is not an exact science; they are more like card counters because they can significantly improve the odds of their team to win titles. Data nerds can also prove their ability to shock the world of sport, such as other main protagonists did before them. For instance, Kareem Adbul Jabbar changed the world of basketball with his Sky Hook, which he developed because the NCAA changed the rules to forbid dunking because he was considered too dominant a player. Hal Mumme – a Legendary Football NCAA coach – also revolutionized his sport with his air raid offense. Data nerds can now be added to this list; they may not be the next kings of sport per se (it’s becoming more and more difficult to find an edge with data analytics) but their decision-making expertise have made them serious contenders for the throne.

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Author’s note: This is a personal article. Any views or opinions represented in this article are personal and and do not represent those of people, institutions or organizations that the author may or may not be associated with in professional or personal capacity, unless explicitly stated.