Analytics of quality data has always been a differentiator for businesses, including sport and in hockey where the NHL has been drafting players into the league since 1963. However, thanks to AI, enhanced tools, specialized agencies, and more, it is now efficient, affordable, and reliability.
There was a time, not so long ago, when hockey statistics didn’t go much beyond goals scored, assists, saves, goals allowed and penalty minutes. How quaint that now seems.
Then we moved into an era where we considered things like plus/minus, shutouts, and save percentage. A bit more insight into player/goalie performance but still independent, or univariate as some analysts would say, measures that don’t consider teammates, team strength, time in game, etc.
Today, data management for player performance has become a huge part of how the game is understood and a primary responsibility of team general managers, coaching staffs and backroom analytics crews. In essence, the addition of advanced math calculations, VR and AI has made player selection, coaching, drafting and talent management in hockey much more intriguing. We are in an era of multivariate, live, and data-drive analytics. Here is a great article if you'd like to read more.
“Hockey analytics has matured quickly over the past few years,” said Rodney Paul, Chair and Director of the Sport Analytics Program at Syracuse University’s David Falk College of Sport. “We’ve moved well beyond the early Corsi and Fenwick era into a much richer, context-driven understanding of the game. With player- and puck-tracking data, teams can now evaluate spacing, speed and decision-making in real time, not just shot totals after the fact. That shift has pushed analytics from descriptive stats toward predictive tools that help front offices, coaches, and development staffs make smarter decisions about systems, roster construction, and player usage. It’s no longer just ‘nice to have’ information; it’s embedded directly into hockey operations.”
Paul’s insight doesn’t just cover what’s going on in the NHL, AHL and ECHL, it’s also creating change at the collegiate and high school level, not to mention junior hockey, development teams, women’s national teams (Olympics and World Cup level) and Para (sled) hockey. Heck, even adult hockey leagues track and publish statistics via sophisticated apps.
A great example is the current Milan-Cortina Olympic tournament where men’s and women’s national teams are playing for medals in front of massive global audiences. Months ago, with much less fanfare, the leadership of each country’s teams had to make the challenging decision of selecting their Olymipic teams. For some countries their pool of elite talent is small and the decisions could be made with old school analytics.
But for countries with deep pools of talent and possible line management challenges, analytics now determine which players play best together and which combinations might perform best against opponents.
We asked Dr. Paul to tell us more about his program, a place where future hockey analytics wizards are training.
“At Syracuse, we’ve tried to be part of that next wave by combining academic research with hands-on student projects through our Hockey Analytics Club and Sport Analytics program,” added Paul. “Our students build models on everything from prospect forecasting to in-game strategy and roster efficiency, often working on real team-style questions rather than textbook exercises.”
And it isn’t happening just at in upstate New York. “I saw this same momentum internationally at Linköping University, where I spoke last year at their hockey analytics conference,” said Paul. “The research community there is doing terrific work with tracking data, machine learning and performance modeling. It reinforces how collaborative the field has become. Universities and teams are learning from each other. That connection between research and practice is what’s driving the next breakthroughs in hockey analytics.”
For NHL teams, where invested resources in analytics continue to rise, the analytics are the most developed in the hockey world. Granted, the statistics created in Milan-Cortina won’t count in the NHL’s books, but we’re confident, there will be a large army of data scientists tracking the 2026 Winter Olympics in order to plan, and evaluate future NHL decisions.
In our book, Business the NHL Way (University of Toronto Press), we outlined NHL business success since the early 1990’s. Notably, the use of technology was one of the key factors we identified and concentrated on.
Of particular note, and supported by the growth of data analytics outlined by Dr. Paul, was our identification of seven reasons the NHL continues growing despite numerous environmental and political challenges.
One of the seven is the league’s Ability to Adapt and Change which we defined as follows: “Having moved through more than 20 percent of the 21st century, League executives have needed to recognize the ever-pressing move toward “digital bodies,” artificial intelligence, virtual reality, data analytics, surveillance technology, modern policing, mental health therapy and evolving athlete rights.”
It’s a short way of indicating the NHL (and all of hockey) is prioritizing data analytics.
Norm O’Reilly is the dean of the University of New England’s College of Business and Partner with the T1 Agency. Rick Burton is the David B. Falk Emeritus Professor of Sport Management at Syracuse University and co-host of The NIL Clubhouse on Spotify and Apple. They are co-authors of Business the NHL Way.