Collaboration and Hockey Analytics

Source: WIkimedia Commons

Source: Wikimedia Commons

Data analytics is a collaborative exercise with the network (both operational and social) being a critical component of any analysis. The right environment has to be in place for people to contribute, develop and share data. To transform the data into information,  context is drawn from the network as individuals apply their backgrounds, experiences and ideas to push the development of a concept. Once the data transforms into information (and later knowledge), the network will distribute the information to those who can use it and develop it further.The importance of collaboration was highlighted at the Analytics, Big Data and the Cloud conference, which presented various topics related to data analytics such as health, productivity and community. One session of personal, and academic interest, was related to professional sports. A description of the session:

Team Building for Success – Sports has always been a numbers game, but today coaches and general managers who want to compete are increasingly reliant on analytics to build a competitive team. Learn what Moneyball didn’t tell you! Facilitator: David Staples – Edmonton Journal

  • “Sports Analytics for General Managers.” Rick Olczyk, Assistant General Manager – Edmonton Oilers
  • “How Player Agents Use Analytics to Find Talent.” Gerry Johansson, TSC Agents – SPORTS Corporation
  • “The Value of A Draft Pick.” Dan Haight, COO, Darkhorse Analytics and Managing Director, School of Business – University of Alberta

A quick recap of what the presenters talked about.

Rick Olczyk of the Edmonton Oilers emphasized the importance of data analytics for team personnel decisions, draft performance and salary cap issues. But he also said sports such as hockey and basketball are more difficult to track because of the unpredictable pace, as opposed to baseball. There’s also a human element that he says hockey managers must take into account when making any decision.

Dan Haight of Darkhorse Analytics gave a summary of advanced hockey statistics and how they are being used in professional hockey. A list of companies that provide sports analytics services such as Coleman Analytics and The Sports Analytics Institute was provided along with “consultants” such as Puck Prospectus, Gabriel Desjardins and Vic Ferrari. NHL teams use analytics for team evaluation and tactics, while fans, according to Haight, use advanced statistics for their fantasy leagues. After analyzing the draft performance of different teams, Haight utilized the goals-versus-threshold statistic to look at the probability of drafting an elite player.

Gerry Johannson of TSC then talked about how agents used analytics for player development and contract negotiations. One application they have available to them is an “agent query tool” that agents can use to see previously signed contracts. The process of contract negotiations involves three steps: analyze the leverage to see where the player fits on the team, analyze the statistics to understand the team dynamic compared to other teams across the league, and finally, prepare the player for negotiations. Across all analysis, agents rely on traditional statistics (i.e., goals, assists, ice-time, etc). Apparently, the NHL collective bargaining agreement does not allow advanced statistics to be used during player contract negotiations.

My research interest has been online fan communities and the information and knowledge generated, shared and developed within a collaborative environment such as blogs. So to hear a discussion of the advanced statistics without the mention of online fan communities being the real drivers of it was surprising. Olczyk did bring up the Oilers work to reach out to the online fan community, which includes a blogger. The Oilers also participated in the Startup Edmonton hackathon, where they release a large amount of data for people to work with and link to different data sets. Both projects, in my opinion, are a great start in collaborating with fans and tapping into new information and knowledge.

Any sort of data analytics requires extensive collaboration to achieve any value. Online fan communities are an excellent example as they have had tremendous success completing analytics because of the collaborative nature of blogs. It will be important for anyone looking to utilize advanced hockey statistics to recognize the value of online fan communities who are intrinsically motivated to continuously improve the information surrounding the game.

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