What Does “Hockey Analytics” Even Mean?

Josh Younggren | June 4, 2021


The debate surrounding the value of “analytics” in sports has existed for about as long as “sports” have existed (don’t fact check me on this I’m just guessing), but boy oh boy did the Toronto Maple Leafs game 7 loss to the Montreal Canadiens re-ignite that debate, again. Now, I’m not going to act like I don’t have opinions on this or am approaching this as a naive bystander:

I clearly have my “hypothesis” about how, especially recently, these debates start and what the general motivation is from those who seem to start them. But, this particular time, the criticism directed at the two specific teams here is kind of silly:

People love to criticize Pierre McGuire, but that’s not what this article is about (Twitter kind of took care of that already if that’s what you were hoping this was about). Along with several other people in the discourse, Luke and I thought it was both odd and amusing that this round of the debate focused on the Leafs and specifically the Oilers. A smaller portion questioned how much of an “analytics” team each of these two teams actually are. This got me thinking about the idea of “analytics”, which teams actually are the “analytics” teams in the NHL, and what that actually means.

However, after a night or two of trying to write that article, it became clear this particular endeavor was a combination of too much subjectivity, a few areas I’m not well-versed in (prospects and drafting), and a clunky idea that didn’t really materialize the way I wanted (maybe someone else can do that) as to warrant that article unfeasible. I will say that the “analytics“ teams I did arrive at were a combination of the Avalanche, Hurricanes, Lightning, Bruins and maybe the Leafs. So, for anyone looking for that article, I suppose this summary will have to do.

But it did get me thinking about the idea of “analytics” and how people use this word in the NHL to mean basically anything that can be broadly categorized as relating to any aspect of the sport that isn’t watching, scouting, or playing the game of hockey. In the “early” days of modern hockey statistics (~5-10 years ago), the word “analytics” made more sense as only a few teams were even doing anything that could be called that. A few bloggers and writers were hired by a small group of teams in the mid 2010s as the idea of evaluating hockey with more comprehensive data started to gain traction both in the league and among writers and fans (specifically the still somewhat new RTSS data the NHL started tracking in 2007). But even then, what did it really mean? The idea of questioning the word isn’t new. Andrew Thomas, one of the smartest and most important individuals in all of hockey analysis, coined a concise criticism of the term years ago that he re-iterated here:

This more or less summarizes the entire issue a lot of people (including the two of us) have: the term “analytics” in most contexts in the NHL is rarely useful. At this point, even people who don’t want to use it do just so others know they’re part of whatever discourse happens to be occurring on any given day. Hell, it has its own Wikipedia page. It’s similar to how none of us really like the term “Corsi” but still use it because it’s the accepted name. Unlike baseball, which somehow came up with a fairly catchy umbrella term for their own analytical evaluation of the game, hockey seems to just have “hockey analytics” as the name of whatever it is we do.

Oxford defines the word analytics as “the systematic computational analysis of data or statistics.” That seems reasonable, right? If one just wants to differentiate very high-level aspects of a given subject, I don’t see any issue with that. The problem arises when debating the effectiveness and value of data, numbers, or any kind of numerical analysis in the game of hockey. Or rather, when someone says the Leafs and Oilers are “the two teams that have been built through analytics and they are both on the outside looking in”. What the hell does the word “analytics” mean in this context? No one actually knows. It means whatever anyone wants it to mean. Based on the definition above, it could range from simply eyeballing the NHL points leaderboard to an ensemble of deep neural networks trained to analyze the gravitational impact a player has on their opponents when in the offensive zone.

But when we’re talking about how teams utilize (or don’t utilize) data and statistics – especially in the form of criticism since we are talking about hockey and not something else – it’s quite unhelpful and mostly circular to just say “analytics” in your criticism or even your praise. It’s unclear, indirect, and actually a sort of dog whistle to those who want the idea of data and stats in sports to go away. To put it more simply, I’ll just cite Micah here:

Whenever the term comes up, at least in the last few years, it seems the kernel is inevitably rooted in distrust and ill-intention. It seems obvious that we, as a community at this point, should just ignore the debate and let it pass.

The actual issue, however, is a little more complicated than just “don’t use the word” or “ignore it”. NHL teams’ “analytics” departments are often unclear, and the use of anything that could be categorized as analytics is cloudy at best. Shayna Goldman has been tracking analytical hires in the NHL for several years, and while this list has definitely grown, it’s still not comprehensive:

This is what’s “public knowledge” – it’s what we can more or less collect from various public sources. And a lot of these individuals have management roles, or scouting roles, which may not even fall into the category of “analytics”. Numerous others have roles that are more of a “have to search LinkedIn to figure out” kind of deal that isn’t really public knowledge (they’re not listed here). The people who work for teams is not the only aspect. In the original article I was going to write, I defined criteria to rank how “analytics” teams actually were. This included drafting, contracts, player utilization, roster construction, and even an idea that I could try to somehow evaluate systems. You can see why that article failed. It’s nebulous. There isn’t a clear definition when one actually tries to pin it down (or at least when I tried).

It’s not the public’s fault or even major media figures (when the intention isn’t shallow naive criticism) for falling back on the term when debating how useful “analytics” as a field is in this sport. To be honest, I think it’s kind of hard to argue whether or not “it” has been useful when one looks at team success over the past decade. Even back when “analytics” was in its infancy, the Kings and Blackhawks were doing things that all the numbers seemed to agree would lead to success (even if it maybe wasn’t directly a result of the more modern approaches). Even more today, the Avalanche, Hurricanes, Lighting, Bruins, and Maple Leafs among others have all brought together groups that utilize data-driven methods and statistical analysis to their advantage, whether it be in their analyst roles or coaching staff or front office. The best of them seem to have have the entire organization on the same page. Not to mention the increase in NHL hires in analytical positions. The use of data and statistical analysis in hockey ins’t going away anytime soon, especially if the above mentioned teams (sorry Leafs) continue their dominance in the playoffs.

So where does this leave us? What does “analytics” mean? Well, as stated earlier, it can mean whatever anyone wants it to mean. More often than not, though, it’s the “fight-y word” (Micah Blake McCurdy, 06/01/21). It means it’s time to identify your comrades, grab your shovel or whatever tool you can find in your storeroom or garage, and start swinging. But that’s hardly useful at this point. The Leafs and Oilers are completely different teams, and “analytics” as a binary idea didn’t lead either to their ultimate demise (for both, ironically enough, it was more due to sheer luck than anything else). I know major media people won’t stop using the term when they criticize teams, but I really think even if one is praising a team, it’s unhelpful to talk about “analytics” without defining what you mean by that.




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