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Analytics captivate sports fans… or puts them to sleep

Near the end of a panel discussion about the value of analytics at Friday’s U of T Sports Industry Conference, a delegate with a detailed knowledge of statistics had a question.

It was long, and revolved around concepts like variance and standard deviation, and while the panelists got it, the giggling and groaning among audience members hinted that they either didn’t understand or didn’t care. Even though they’d shown up for a discussion on analytics they weren’t ready for such technical talk.

And, it highlights a dilemma among sports analytics experts. The topic is increasingly important, hence the emphasis on it by teams like the Houston Rockets and Philadelphia 76ers. But with its emphasis on advanced statistics and mathematics, it remains difficult for many fans to decipher.

U of T engineering student Valentin Stolbunov hopes the school’s Sports Analytics Group can help bridge that gap.

He helped found the group in September, and it has quickly grown to more than 20 members. Currently they’re working with the Blue Jays on questions dealing with the value of platoons, and how minor leaguers will perform in the majors.

“It’s a super tough problem and a million-dollar question. Billion, maybe,” says Stolbunov, a third-year PhD student. “We’re willing to work on these problems and make connections because we enjoy this stuff.”

Still, debates percolate over whether devoting resources to analytics can markedly influence the only metric that counts.


NBA legend Charles Barkley dismissed analytics as “crap”, and was criticized for it.

Last month at MIT’s annual Sloan Sports Analytics Conference, critics like Kyle Wagner of Deadspin wondered out loud whether the biggest victory for proponents of analytics has been convincing the sports public that their work really matters.

Either way, a renewed focus on obscure numbers and predictive stats has affected everything from how teams draft to how they staff the front office.

“If this was five years ago I probably wouldn’t be sitting here,” said NBA basketball analytics director Jason Rosenfeld during the panel. “I probably wouldn’t have a job in sports.”

One member of the U of T’s Sports Analytics Group has already accepted a job with the Oakland A’s, which jumpstarted the movement by implementing its “Moneyball” strategies in the early 2000s.

Stolbunov sees the field of analytics expanding, both in terms of the sports it covers and the talent it attracts. He says his group is composed mainly of engineering and math students, but can grow to accommodate students with design backgrounds who can help display the group’s findings. And he thinks individual Olympic sports are overdue for an analytic overhaul.

In some ways, pro football is already there, says New York Giants assistant GM Kevin Abrams. He admits hand-timed 40-yard dashes, while the NFL’s highest-profile “advanced” stat, are an inefficient way of measuring an athlete’s speed. But he points out the league has long relied on the predictive value of obscure stats like hand size and arm length.

“We’ve always had it (but) we just didn’t call it ‘Analytics’ the last three decades,” Abrams told the conference. “Now we do.”


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