It's a revolution that has, in many ways, already taken place in North America's other top professional sports.
In baseball, Bill James, Billy Beane and Moneyball – the film version of which was released in theatres this weekend and stars Brad Pitt as Beane – helped bring in-depth statistical analysis out into the open to the point that it's now simply part of doing business for most teams.
In the NBA, the Houston Rockets turned to former MIT analytics professor Daryl Morey to be their general manager, one of more and more hires in that vein in the basketball world.
And in the NFL, teams such as the New England Patriots have long used advanced statistics to dig up the best players for the lowest price, giving them an advantage under the salary cap.
Hockey, however, has always been behind the curve.
Which is where people like Gabriel Desjardins are coming in.
With a day job as an engineer with a semiconductor firm in Silicon Valley, Desjardins's background is unlike anyone's in hockey. But with teams looking for a unique edge, he's being called upon regularly when NHL teams have a decision to make.
While his name isn't listed on any team's directory, the 34-year-old, originally from Winnipeg, is now on the payroll of three teams as part of a new push for more statistical analysis in the league.
More than 30 years after James made a name for himself as baseball's leading numbers guru, Desjardins has built a reputation that will likely see him catch with a team full time – just as James did as a senior adviser with the Boston Red Sox in 2003.
It seems only fitting that Desjardins grew up reading James's work and began applying it to the NHL about a decade ago.
“He's somebody who changed my thinking about baseball,” Desjardins said. “And ultimately he made me change my thinking about hockey, even though he doesn't know anything about hockey.”
In some cases, that meant borrowing directly from James, such as when Desjardins used his “minor league equivalencies” concept to project how goals and assists in junior or minor pro leagues translate to the NHL.
In others, Desjardins developed completely new statistics specifically for hockey, including ratings for the quality of players' teammates and opponents when they're on the ice.
Other metrics, such as Corsi – which was originally used by Buffalo Sabres goaltending coach Jim Corsi to measure the workload netminders were facing – gauge how often players are in possession of the puck by counting every shot directed at either net (including those that miss the net or are blocked) while they're on the ice.
In contrast to traditional statistics such as goals and assists, advanced statisticians believe these new numbers offer greater insight into which players are performing well in important areas of the game, including defensive play, puck possession and ability to play against other team's top lines.
This knowledge, in theory, allows teams to better determine what players should be paid – that is, their true value under the salary cap – and find players who may be overlooked or underrated by their own organizations.
Desjardins's work at behindthenet.ca has gained a large following in the past several years, but NHL teams have only recently begun to reach out to him for help. At a cost of up to $200 an hour, he now dedicates roughly eight hours a week during the season to the endeavour.
Which teams he works for and what, precisely, he does for them, however, remains behind closed doors, as he's sworn to confidentiality as teams try to keep quiet any work they do in what is very new territory for the league.
What Desjardins can say is that some of his recommendations led directly to teams pulling the trigger on major deals last season.
“I've seen people use Corsi to make trades,” he said. “I'll put it that way.”
The secrecy surrounding analytics in the NHL extends well beyond Desjardins.
Of all the teams contacted on the subject, only the Pittsburgh Penguins were willing to talk openly about their increasing use of advanced statistics, which began in earnest last year through a partnership with a group called The Sports Analytics Institute.
“This was the first year, this past season, that I felt we were really onto something,” said Penguins director of player personnel Dan MacKinnon, who has become the team's point man in the area. “We're getting some powerful insight into things that you just can't track with the naked eye or traditional statistics.”
MacKinnon estimates that only five or six NHL teams are doing considerable work with analytics, with another half dozen beginning to “kick tires” and investigate some of the concepts involved.
The rest of the league, he said, isn't going this route because “they feel hockey doesn't lend itself to analytics.”
MacKinnon pointed to the Sabres' recent creation of a small analytics department as a sign of where things are going, adding that he wouldn't be surprised if more and more people like Desjardins are brought into the fold to offer a different perspective.
SAI's model has introduced the Penguins to in-depth shot location analysis and goal scoring probabilities – using a statistic called predicted goals scored – that they make available after every NHL game.
What was initially a tough sell (and remains one to many teams) has become a tool some on Pittsburgh's staff use on a daily basis.
“Instead of explaining it to people, we put it right in front of them,” said Kevin Mongeon, an economics professor who developed SAI's metrics with analytics specialist Mike Boyle. “And said, ‘Here it is.' They looked at it, and there's a little bit of variation game over game, but after 10 games, they went, ‘Holy crap, I can't believe I've been doing my job without this.'
“The coach in Pittsburgh, we went down after a game one time, and he said ‘Did we out PGS them?' They used it as a verb after a while. But it took a while to get there.”
Desjardins met that resistance firsthand when he began meeting with teams, recalling one recent sit-down with an NHL general manager who wrote off his work as merely “doing arithmetic.”
He believes most teams still have a long way to go in terms of embracing what advanced statistics can do for their organizations.
“You can see that there are a lot of decisions made every year – Philadelphia getting [Ilya]Bryzgalov, for one – that pretty much any analytics department would, 100 per cent, advise you against,” Desjardins said, referencing the Flyers netminder's $51-million contract as an example of inefficient spending.
“As high as player salaries are, if you hired a very skilled analytical consultant from industry, paid them $150,000 to $200,000 a year, and he sits there and works on things all year and comes up with recommendations, what are the odds that he's not going to be able to find you a player that you can sign for $200,000 less his value or less than you otherwise would have paid?
“It's pretty obvious that there's some value in there.”
MacKinnon agrees and sees that as the way things will be done in the future as the NHL finally follows in the footsteps of other major pro leagues.
He hopes that by shedding light on what the Penguins are doing more teams will get on board and the data available will improve.
“There's no doubt in my mind,” MacKinnon said. “Ten years from now, every team will be using something like this.
“For me to make the best recommendation possible to [Penguins general manager Ray Shero] I'm using this as a powerful tool and he's asking what it's told me.”