There is a change in the NHL landscape this season—not from realignment or rule changes or a new collective bargaining agreement, but rather from the rise of hockey analytics. It has been the talk of the league this past off-season as many NHL teams have assembled teams of analysts in the hopes of gaining a leg up on their competition. In response, NHL broadcasters (TSN and Sportsnet included) have scrambled to amass their own bench of analysts, taking advantage of the hype and bringing so-called “advanced stats” to their millions of viewers.
Now, if I lost you at the word “analytics,” you’re certainly not alone. As someone who works in the field (i.e. web analytics), I’ve come to realize that the word “analytics” alone doesn’t mean a lot to most people—It usually elicits a blank stare. So to the average hockey fan, I doubt “hockey analytics” is any more meaningful.
Analytics is simply the process of using data to make better decisions. We, of course, make decisions in every aspect of our lives, and we are routinely affected by the decisions of others—businesses, governments, and even sports teams. Increasingly, such decisions are being influenced and hopefully improved through analytics.
Amazon, Facebook, and Google use analytics to recommend what you should buy, read, and watch, in order to provide you with a better online experience. Cities and municipalities use analytics to optimize traffic flow and reduce gridlock. Healthcare workers use analytics to predict and prevent the spread of disease.
If data is the input, analytics is the machinery, and the output is a stronger, smarter, and more efficient society.
In the world of sports, baseball has likely developed the most advanced practice of analytics. If you have seen the movie Moneyball, you are already familiar with how statistics can be used to better evaluate players and build a better team. By its nature, baseball is highly conducive to being quantified. Baseball is a game of “discrete” actions that, for the most part, can be easily measured. If you wanted to know, for example, Derek Jeter’s batting average against left-handed pitchers with two outs and two men on base in the 9th inning of games on a Saturday in July with the sun at a 60 degree angle, you could probably work that out relatively easily from the available data. In contrast, more “fluid” sports like soccer, basketball, and hockey are less conducive to being measured at this level of detail.
However, improvements in technology are now making it easier to quantify even the quickest and most fluid sports, like hockey. Sensors now allow every player’s position to be continuously monitored and recorded. We can track individual passes, shots, saves, and hits. All this data will allow more in-depth analysis of the contribution of each player to their team’s success.
The well-known stats in hockey are actually quite basic—goals, assists, shooting %, blocked shots, saves, goals against average, etc. In many cases, you can go a step further and breakdown a player’s numbers against certain teams, in certain periods, or in certain situations. But much of the difficulty in hockey lies in quantifying the team dynamic. How much of a goaltender’s performance is related to the strength of the defence in front of him? How much of a player’s scoring ability is due to that of his linemates? (In the business world, we would call this attribution modelling—evaluating the contributions of multiple factors leading to success.) These questions and more can only be answered with more data and more analysis.
Will analytics become a substitute for knowledge and experience with the game of hockey? Certainly not. Analytics is just a tool. It will only prove successful if used correctly. NHL teams will require the technical ability to capture the right data, the analytics skill to perform meaningful analysis, and the hockey expertise to ask the right questions, interpret the results, and take action. The teams that do it first and do it best are going to have an unseen advantage on and off the ice.
Can analytics help teams make better draft picks by uncovering underrated talent? Can it help coaches create better matchups or line combinations? Can it help players get out of slumps by highlighting what they may be doing wrong? The possibilities are intriguing. Analytics has the potential to change how every NHL team operates, from the locker room all the way up to the board room.
Let’s be clear, there will always be an element of randomness to hockey. The unpredictability of sport is what makes it exciting to watch. But if analytics allows NHL teams to attribute even 10% more of their performance to controllable factors, it could be enough to give them a distinct advantage.
Will this season be the start of one team’s data-driven journey to the Stanley Cup? I’m excited to find out. Let’s drop the puck!