There is a popular misconception that the more chaotic a sport is, the less worthwhile it is to approach it with an analytical eye. Team sports are fluid, every action is affected by countless others and very few events can be assessed properly in isolation. An extreme example would be underwater hockey, where the nature of underwater competition coupled with the relative chaos of team sports makes analysis of play difficult. Underwater hockey is difficult to assess with the naked eye, and it’s the coaches’ job to do so and draw as much insight as possible.
Why, then, would data collection and analysis be able to tell a coach anything he doesn’t already know having watched his team play every day? There is a strange residual mentality in sport that nothing beats intuition, particularly that of the manager or chairman, and a skepticism around data science’s place in some sports still prevails. It’s difficult to see the mentality as anything but defensive; the cult of the manager is something most in the position would want to protect. To be able to accurately map the movements of an entire underwater hockey team would, of course, be helpful.
In any sport, player performance data can be used to supplement, if not drive, decision making around team selection and tactics. Sports analytics is imperfect - plenty of unnecessary data is collected and there are some things in competition you just can’t account for - but essential, which is why ice hockey’s apparent pushback against the numbers game is strange. While there’s a great deal of noise around analytics as the ‘future’ for the NHL, anecdotal evidence from inside the sport suggests it’s some way behind sports like soccer, football or baseball in picking it up.
Former Montreal Canadiens analytics consultant Matt Pfeffer believes ‘analytics hasn’t reached maturity in the NHL yet,’ having been let go by the team following a very public disagreement over a transfer. Pfeffer was always against his team trading star defenseman P.K. Subban and, when it became clear the Nashville Predators’ Shea Weber was to be the return trade, Pfeffer made his feelings known to the board. He claims this disagreement wasn’t the direct reason for his subsequent lack of contract renewal, but his unyielding faith in the numbers clearly sat awkwardly with one or a number of the management.
‘The person I reported (director of legal affairs/capologist John Sedgwick) to liked my work and the methodology behind it and believed in it,’ Pfeffer told The Hockey News. ‘And there were others inside the team that didn’t believe in it and maybe had their mind made up about advanced stats. I think there’s been a bit of pushback from people in the NHL recently about this kind of stuff.’ Pfeffer has since seen his opinion vindicated by Weber’s good but not-that-influential performances. Subban’s goal differential is a rare 3.14% where Weber’s is 0.18% - essentially the Predators are as good with Weber on the ice as they are without him. If the Predator’s had put their faith in the numbers, they’d still have one of their most influential players of recent years.
It’s not that hockey cannot or should not be analyzed in the same way that football or soccer is because of some kind of inherent inability to be quantified; the NHL is just not as far down the track as other analytically advanced sports. Hockey is still in the experimental stage, clouded by skepticism and misconception over what data analysts are actually looking at with regard to team and individual performances.
Some teams are already using advanced analytics, Corsi is a flawed but now ubiquitous system, and it’s only a matter of time before hockey catches up with it’s peers off of the ice. Pfeffer’s story may be the most high profile of its kind, but the mentality that led to his being let go rarely exists in isolation; the NHL is currently more skeptical of analytics than it should be.