Hockey analytics has become an important component of the participatory culture surrounding the game. Anyone within the hockey community, including fans and league managers, can use numerous tools and techniques to detect patterns in the data available, in order to follow and understand the game. The NHL as well as mainstream media websites provides ample data for people to work with, while others “outside” of the game, including fans and independent organizations, can develop their own data and methods to complete analysis.
Hockey analytics can be done by anyone with a computer and basic software, depending on how large of a dataset is being examined. The analytic models are dependent on an individual’s or communities’ creativity and rationale, so effective measurement of performance are up for debate and development. Numerous examples of extensive correlations, ranging from simple to complex, can be found, with the vast majority of these analyses open to feedback and collaboration. For example, fans can find a correlation between a team’s scoring chances and the quality of the shots they take (NHL Numbers) . Another example is the correlation between a player’s presence on the ice in relation to the success of the rest of his teammates on the ice (The Copper & Blue). Depending on the goals of the individual or community that analyzes the data, this could be tracked over time to make comparisons and to validate findings.
Currently, data analytics is an emerging trend that is gaining prominence in a wide array of fields. Sales, marketing, healthcare, transportation and construction are just a few of the industries relying more and more on data to understand their environment and to make the right decisions. Governments, such as the City of Edmonton, have even begun publishing massive public datasets, available for anyone to use either for their analysis, or to develop new technological tools or services.
Along with the data collected and supplied by organizations such as the City of Edmonton or the NHL, an extensive amount of data is being supplied by individuals themselves. Mobile technologies, along with countless applications, have given individuals the ability to generate data about themselves and their own behaviors. This data is released both unintentionally, such as when mobile gaming applications receive access to the users web browsing history , as well as intentionally, such as when individuals publish their location when using Twitter or Facebook. This intentional release of data has become a popular activity amongst users from various demographics and backgrounds, as they track their own activities and goals. In her book “The Virtual Self: How Our Digital Lives Are Altering the World Around Us”, Nora Young (2012) uses numerous examples and case studies to not only highlight this emergence of self-reporting, but also the societal ramifications of this behavior.
“For all its pleasures and benefits, digital life fundamentally time-shifts and place-shifts us out of the here and now. It is precisely this disembodied, distracted, digital life we lead…that is creating the urge to document the physical body” (Young, p. 3).
While Young’s focus is on the potential application of personal data to benefit the needs of society as a whole, including Government and communities, one could also envision how this personal data can be used for specific purposes, such as hockey analytics.
The next generation of hockey players
The reason this self-reported data is important to hockey analytics is because of the young class of players entering minor and professional leagues. A rising number of players are using web technology to interact with fans and promote the game, but also to maintain their own personal networks and participate within their own online communities. In doing so, these players are leaving behind a digital trail of their online activity that develops this “virtual self” Young (2012) describes. Some of their activity is from before they even became popular professional players. This trend of publishing personal data, whether intentionally or not, is becoming common, especially for those who are “born digital” (Palfrey & Gasser, 2008). Those who are born after 1980 have developed in the digital age, according to the authors, and have a different understanding of identity, privacy and information than their predecessors. This generation is more comfortable exchanging their data for services to simply track their behavior or to construct their online identity.
Future of hockey analytics
Based on the current technological environment and various traits of the hockey industry, there are strong indications that the data players release themselves will have a significant impact on the future of hockey analytics. This linking of real data, or that created from actual hockey games, to the “virtual self”, or data published by individual hockey players, is a strong possibility.
For one, the amount of attention young players receive, starting from an early age, demonstrates the demand the hockey community, including fans and the NHL, have for information. Young players are being tracked and analyzed to find information about their personal backgrounds, other interests or academics as well as their stories of making it to the NHL. By the time they reach the NHL draft, their statistics and back stories are developed and ready to be analyzed. Second, hockey analytics is developing at a significant rate due in part to a growing online community working together that develops new ideas and research methodologies. If a correlation between two or more variables is suspected, based on some sort of reasoning, this community has demonstrated their ability to either search for the data or somehow find a way to generate that data. Third, analytic tools and mobile technology are becoming easier to use. For hockey players, this means their personal data is more and more readily available. For the analytic community, this means data is easier to acquire, share and utilize for various purposes.
Today, a player’s performance can be tied to a number of variables, typically available from game performances and results. In the future, a player’s personal workout schedule, practice regime or data outside of game results could potentially be correlated to their on-ice performance. It may seem farfetched now, but as more and more creative web and mobile applications get released, combined with the demand of data and information by the hockey community, hockey analytics will evolve and utilize the personal data published by this new generation of hockey players.
Micarelli, P. (2010, December 3). [Image]. Drawing the Internet. The Monkey Buddah. Retrieved from http://monkeybuddha.blogspot.ca/2010/12/drawing-internet.html
Nowak, P. (2012, June 8). The Virtual Self: Nora Young on digital self-tracking. Canadian Business. Retrieved from http://www.canadianbusiness.com/blog/tech/87106–the-virtual-self-nora-young-on-digital-self-tracking
Palfrey, J. and Gasser, U. (2008). Born Digital: Understanding the First Generation of Digital Natives. Basic Books: USA.
Young, N. (2012). The Virtual Self: How Our Digital Lives Are Altering the World Around Us. McClelland & Stewart: Canada.