By Sunil Agnihotri

Applying Everett Rogers’ Diffusion of Innovations theory to understand the adoption of hockey analytics

By Sunil AgnihotriAs fans, we all watch, follow and engage with the game very differently. Hockey analytics really is a supplement to our experience with the game, much like gambling, fantasy league and video games. What a person pays attention to during a game depends on their own experience, including their biases and preferences.

Aside from the information it’s creating and the impact it’s having on the game, hockey analytics is first and foremost a method of engagement with the game. Fans are far more than passive consumers and have used the communication technology available to fully immerse themselves in an active, participatory culture.

Having said that, hockey analytics is an innovative way to understand the game as fans try to detect some sort of meaningful patterns. Again, it’s not for everyone, but the fact is analytics, especially the work fans and bloggers are doing, can possibly change how the game is being played.

And like any innovative idea or product, it tends to go through a process to become adopted by the masses. Everett Rogers’ Diffusion of Innovation theory (1964) in particular, provides some context to the current dissemination of hockey analytics.

A summary of the Diffusion of Innovation theory from UTwente:

Diffusion research centers on the conditions which increase or decrease the likelihood that a new idea, product, or practice will be adopted by members of a given culture. Diffusion of innovation theory predicts that media as well as interpersonal contacts provide information and influence opinion and judgment. Diffusion is the “process by which an innovation is communicated through certain channels over a period of time among the members of a social system”. An innovation is “an idea, practice, or object that is perceived to be new by an individual or other unit of adoption”. “Communication is a process in which participants create and share information with one another to reach a mutual understanding” (Rogers, 1995).

Five Stages

There are five stages of the adoption process: Knowledge, Persuasion, Decision, Implementation and Confirmation.

With Kyle Dubas being hired by the Maple Leafs last week, and the club being in one of the biggest hockey markets, hockey analytics is being discussed by many involved in the game. I’d suspect that a lot of people are still in that Persuasion phase. But even if an individual decides that hockey analytics has some value, they may not ever apply or implement the concepts in any way. Again, engaging with the game varies from fan to fan.

It’s worth noting here that although NHL clubs have likely used some sort of analytics, involving tracking data and finding meaningful patterns, it wasn’t until around 10-15 years ago that fans began publicly developing and sharing concepts pertaining to hockey analytics. One blog in particular, Irreverent Oiler Fans, has archives dating back to 2006 discussing hockey analytics and some its foundational concepts.

Factors

Rogers’ outlines five key factors that influence an individual’s decision to adopt or reject an innovation: Relative Advantage, Compatibility, Complexity/Simplicity, Trialability and Observability.

I suspect that many are still factoring how much of an improvement hockey analytics is compared to the traditional hockey stats. Today, puck possession and player deployment have become more valuable ways to measure player and team performance rather than, say, plus/minus. Those have always been important for coaches, but possession and deployment stats were never readily available. But with websites like Extra Skater, and prior to that Behind the Net, fans are getting data that hasn’t available anywhere else. On top of that, fans were able to publish and discuss their interpretations of the stats on message boards and blogs.

For some, the only thing that matters is the final score, and that’s fair. For others, especially those that like to extend the conversation surrounding the game, it’s important to find a rationale explanation of the games’ outcome. Again, the adoption of hockey analytics depends on the individual and what their engagement level is with the game.

Rogers’ theory also explains, to an extent, why hockey analytics is receiving so much resistance. Had an innovative idea such as Corsi or Fenwick been extended, produced and made public by a successful general manager or coach, the stats would likely have been welcomed with open arms. Innovative ideas, especially those that enhance or increase fan engagement (think video games, fantasy league and gambling), have typically come from either the NHL itself or major corporations like EA Sports. Because these advanced stats have been developed by hockey fans, there is a great deal of trepidation when accepting things like Corsi and Fenwick stats.

Rogers’ theory could also provide guidance to those that are looking to promote and grow the field of hockey analytics. For instance, simplicity is key according to the theory, so finding new ways of communicating the basic concepts in an easy to understand format would be recommended. Visual diagrams or interactive tools may be more useful that simple text. A way for individuals to test the concepts of hockey analytics, as outlined the Trialability factor, could also persuade those new to analytics. And finally, because of the social system’s importance to innovation, it’s imperative to maintain an online environment that is open to information exchange and dialogue.

References

Rogers, Everett M. (1962). Diffusion of Innovations. Glencoe: Free Press