here was a time when Ian Fleming (LAW ’15), just for fun, typed up contracts for his favorite baseball icons—this many dollars for that many years. He’d pick a team and peg every player.
He was eight years old.
“Even from a very, very young age, I wanted to be a general manager as much as I wanted to be a professional athlete. Which is weird,” Fleming laughs.
But those exercises on his old Windows 98 computer system led to ambitions.
Fleming eventually got a law degree, taught himself to code at night, and turned his wonky side hobby into a general manager position in the largest professional women’s soccer league in the United States.
“If you’re doing the things you need to do, you will win more often than you lose,” Fleming says. “And I’d much rather my stress be related to something I enjoy.”
It took Fleming some time to find his way.
Born in the small town of Warwick, New York, Fleming was a good enough soccer player to earn a scholarship to play at Limestone University in South Carolina—until a herniated disk put a stop to his career. He went on to receive a B.S. in economics and to work for an investment firm before pursuing a law degree at Chicago-Kent College of Law.
Fleming started his career at a financial law firm in Chicago, doing project work. He soon got into business analysis, which at least tapped his penchant for equations.
But it wasn’t enough.
As soon as he came home, Fleming kept his attention focused on statistics: not those of businesses, but of sports teams.
“It was every night until bedtime,” says his wife, Hélène Balcerac. “But he had no choice. [Sports analysis] was what he wanted to do.”
Fleming started analyzing hockey players, specifically goaltenders. There was a common perception about goaltending in the industry, specifically that, in some ways, it defied reason—the position is incredibly difficult to understand, much less apply any semblance of logic to.
But there was a glut of analysis about other positions—so he chose to try and define the illogical.
“You look at basic stats that have existed for decades. Save percentage, that’s the gold standard,” Fleming explains, referring to the percentage of shots that a goaltender denies. “But the thing that was being missed is that not all shots are created equal.”
He took that save data and broke it down by player. Then he broke it down even more: how many of those saves were “high danger” saves, based on where the shot was taken from? What about quality of teammates and quality of opposition?
He’d then predict the number of goals against each player for every 48 minutes—the average amount of time they spent at the net when the opposing teams were at equal strength—and compared it to the league average.
Fleming charted his stats and explained them. He built a website and chocked it full of data visualizations that would later comprise his portfolio. His analysis started to gain traction. His Twitter following grew from 20 to 2,600. Soon enough, hockey coaches reached out to talk about their goalies.
Fleming’s wife bet him that within three years he’d be a general manager.
“The odds of that happening are so slim,” he told her.
The San Jose Earthquakes, a Major League Soccer team, was the first sports team to hire Fleming. As senior manager of business intelligence and analytics, he analyzed the maneuvers of fans, not players. Instead of on-the-field stats, he tracked ticket sales, season ticket renewals, parking flow.
In 2019 Frank Arnold, vice president of administration for the Houston Dynamo and Houston Dash professional soccer teams, hired him away from San Jose.
“He brought a level of sophistication that we hadn’t had previously: an understanding of statistics and predictive modeling,” Arnold says.
When Fleming told Arnold about his passion for analyzing athletics, rather than stadium flow, Arnold listened. He introduced Fleming to the Dash’s head coach.
“We struck up a relationship,” Fleming says of the coach. “At times, he’d shoot an email, ‘I’ve got this trade offer, you have any thoughts?’
“My process has changed dramatically from the time I did that website,” Fleming adds. Instead of using league-aggregated data, he now aggregates raw data from the soccer field—passes, completions, distances the ball traveled, performance when players are up or down a goal—to build models to isolate the effectiveness of each player.
In 2020 the Dash won the National Women’s Soccer League’s Challenge Cup, the first trophy the team had ever won as an organization. While Fleming says the win was “certainly not something I’d take any glory for,” it didn’t hurt his prospects when he made the short list for the general manager opening with the Orlando Pride, another NWSL team. He started as the Pride’s general manager in December 2020.
The Pride finished dead last in the league in 2019; 2020 was lost to COVID-19. Under Fleming in 2021, the team had the best start in its history: a seven-game undefeated streak of four wins and three draws. After that, some of his players left to compete in the Olympics, and the team faltered some, ultimately finishing 7-10-7 and five points shy of a playoff berth.
Despite the ups and downs, Fleming is confident that his penchant for predictive analysis gives him an edge.
“We don’t have a lot of data scientists in the league. But having a data scientist as a GM, you have someone who can scout and measure opposition in ways no one else does,” he says. “I’m building a team just like I wanted to do when I was little. ”