By Sunil Agnihotri
Hockey analytics is an excellent example of fans getting immersed in the game and changing the way they consume professional sports. Along with watching games, and following the narratives that surround teams and players, fans can use various software applications to apply their own ideas and models to analyze the game.
Hockey analytics is also gaining prominence among professional hockey teams to make key decisions regarding player acquisitions and team strategies. The continued growth of the MIT Sloan Sports Analytics Conference, which is attended by teams and managers from various sports, as well as academics, indicates the growing importance of data analytics in the professional sports industry.
Questions have arisen recently about why some NHL teams are not conducting any hockey analytics, as well as why some teams refuse to get into too much detail about their current analytic methods (Friedman, 2013). Questions have also arisen as to why hockey analytics have not reached mainstream status on television broadcasts, such as Hockey Night in Canada (Dowbiggin, 2013).
To answer these questions, the activity of hockey analytics needs to be dissected by first understanding its relationship to information and knowledge development as well the environment is requires to flourish and reach its potential.
Hockey Analytics for Information Development
The purpose of any data analytics is to develop and highlight useful information. Hockey fans, for example, can use the data from player performance and apply their own models to potentially uncover trends or patterns. This in itself is a relatively new power held by fans, as far too often, the information surrounding the game of hockey is influenced by the ideas and biases of those “inside” the game, such as hockey reporters, employed by major sports news networks, and those who coached or played professionally. These insiders provide the majority of content developed for mass consumption by dominating media outlets on television, on the web and in print.
Today, thanks in part to web technology and analytical software, fans are free to publish and share their own their own information uncovered by hockey analytics. The interesting part of this ability of fans is that their contributions have the potential to impact the information that surrounds the game of hockey.
The environment that supports data analytics must also be examined to understand the true potential of hockey analytics. Hockey analytics, best suited in a collaborative environment, involves the continuous development of content. Therefore, I would argue that hockey analytics is best suited in a “produsage” (Bruns, 2008) environment, which involves the following key traits:
- Open Participation, Communal Evaluation – Produsage environments are open to all to get a wide array of experience and contributions.
- Fluid Heterarchy, Ad Hoc Meritocracy – Leadership within the project depends on the contribution the individual makes. Those whose contributions are valuable to the project will elevate their status within the community.
- Unfinished Artefacts, Continuing Process – Rather than a finished product, the aim of produsage is to evolve and continuously improve the shared content within a community.
- Common Property, Individual Rewards – Individuals working within a produsage environment are motivated by their ability to contribute to a communal purpose. Produsage environments ensure that the shared content will not be exploited and will remain available to those who contribute to the project.
Once we accept that hockey analytics is a method for fans and those inside the game to develop the information that surrounds the game, and that the environment it requires to be successful includes the traits of a produsage environment, w e can begin to answer some of the questions that have been raised regarding hockey analytics.
Why aren’t more NHL teams using hockey analytics?
Strong information about their own team’s performance and the performance of others gives NHL teams a competitive edge. The NHL is a highly competitive league with millions of dollars at stake for team owners, so publicizing any analytic strategies may put a team at risk. So it really is no surprise that NHL teams remain highly secretive about the information they are trying to generate using hockey analytics.
It may also be true that some teams aren’t using data analytics to build information for key decision making. This may be because they have tried and failed, or just don’t have the right resources or environment to support hockey analytics. The key traits of produsage can serve as a primary check list for teams that are struggling to develop data analytic strategies. For instance, a team needs to have a team of people to develop ideas and models to do any data analytics.
Why doesn’t CBC supplement their television content with hockey analytics?
Hockey broadcasters such as CBC’s Hockey Night in Canada typically rely on quick, high-level, information to produce commentary about the game. Since television broadcast are one-way means of communication, for mass consumption, very little detail is given regarding player performance and team strategies. Narratives typically rely on popular statistics such as goals, assists, penalties and win/losses. I personally would like to see more in-depth analysis of statistics, but unfortunately the TV medium is not the right environment to develop hockey analytics.
For CBC to begin sharing the information developed by hockey analytics, it would be in their best interest to be involved with the actual analytics. Until then, they would probably steer clear of sharing information from, say fan developed information, as they may not fully comprehend how it was developed. It would also be in CBC’s best interest to perhaps provide data or data analysis on their websites, a more interactive means of communication, to stay on top of hockey analytics. Until CBC gets more familiar with hockey analytics, and meets their viewers in a more collaborative environment, there is a good chance they’ll continue producing the same clichéd narratives they’ve become accustomed to.
What gets lost in the discussion of any hockey analytics topic is the fact that all of the metrics, the methodologies, the applications to real-life scenarios, and so forth, are all part of developing information about the game. Too often in the past, “knowing” the game was left to former players and managers. Sports journalists, because they wrote about the game and had close relationships with players and managers, were then assumed to have knowledge about the game. Today, because of the abundance of content online, and the tools for anyone to participate, almost anyone can play a role in the development of information surrounding the game of hockey.
Bruns, A. (2008). Blogs, Wikipedia, Second Life and Beyond. New York: Peter Lang Publishing.
Dowbiggin, B. (2013, February 18). Networks miss the story on Manny Malhotra. The Globe and Mail. Retrieved from http://www.theglobeandmail.com/sports/dowbiggin-networks-miss-the-story-on-manny-malhotra/article8785464
Friedman, E. (2013, March 6). NHL teams ready to embrace advanced statistics. CBC Sports. Retrieved from http://www.cbc.ca/sports/hockey/opinion/2013/03/nhl-teams-ready-to-embrace-advanced-statistics.html
3 thoughts on “Assessing the State of Hockey Analytics”
Reblogged this on SuperFan 2.0.
Thanks for the post Sunil! You raise a lot of really interesting points about advanced stats and hockey culture. I actually used the example of advanced stats bloggers in my class recently as an example of a Jenkins’ new knowledge community, and also brought up the produsage environment as well, so this is very timely for me. I definitely agree that it’s a fascinating fan-driven, new media-based movement that is having an impact on the way in which hockey teams operate.
Pingback: Hockey Analytics and Me | The SuperFan