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How data is revolutionizing hockey

By Steve Cavolick

As an Analytics practitioner for over 20 years, I am fascinated with how companies are leveraging their data to create value and become transformational in their industry. We all know that companies that are analytics-driven are more likely to acquire customers, be profitable, and retain customers than those who aren’t.

But what about organizations that aren’t companies? How can they use data to transform their business? The National Hockey League is an example of an organization that is not a company, but sees data leading them into the next generation of team performance and fan experience. Consistently ranking #4 in popularity among the major sports in America, the NHL believes using data will make viewing the sport less passive and draw more fans to the game through related activities such as fantasy sports.

“I’m so happy with the fact that all of this technology will give all of our fans an opportunity to get closer to the game,” NHL commissioner Gary Bettman told The Athletic in this article.

The new system is based on sensors in pucks and players’ jerseys that will record and send information up to 200 times per second. With the monitoring of individual player movements and performance, concerns around ownership, usage, and security of the data arose. Teams had questions around access to the raw data, how it could be delivered in real time, and in what format the data will be shared so it can be used. The NHL Players Association was concerned that the data on personal performance could be used against them in future contracts, but the league and players agreed that no biometrics would be captured and that none of the information generated could be used in contract negotiations and arbitration.

The benefits to teams in using league-wide data will be three major areas:

  • Athletic Performance Monitoring: Understanding how a player exerts himself in games over time can help teams determine the physical load each player can tolerate and help with minutes distribution within a game.
  • Having Context: It will be possible to draw up plays and simulate how the other team’s defensemen will react, for example.
  • Rosters: Using data to determine who is the best (and worst) fit for a team and tactics. This will help teams construct rosters and become more targeted in free agent signings.

As in any organization that is new to analytics, there are some people that see the value of the data wave and are hiring new people to expand their analytics teams, while others are not quite believers yet. One GM said, “I’m not anticipating hiring a bunch of people. I think you’ve got to figure it out. It’ll be a process of learning – ‘How is this going to help us? What am I going to do with it?’ Until it comes out, I think for me, it’s premature to be jumping in.” This quote illustrates a universal tenet in analytics, which is the importance of having a strong use case and that can be tied back to improvements in the team or company.

Teams will benefit, but beginning next year, fans will also be able to leverage the data to augment the way they watch and participate. For those watching on TV, live stats above each player will display exact speed in MPH, time spent in the offensive zone, total feet skated, and scoring. Fantasy hockey players will receive a video when one of their players scores within seconds of it happening. Mobile sports betting apps will leverage this data to support real-time proposition bets, and create probabilities of team success based on in-game injuries and lead changes.

The coolest proposed use of the player data so far entails using the player data in real time to create a virtual, real-time version of the game. Do you want to watch the game as your favorite player sees it? Or the way it looks to the coach from the bench? Since each player is basically a data point, technology in development will change the angle of how you watch the game. This will put fans right in the game as it unfolds.

Whether you are providing player stats in real-time or trying to reduce SG&A as a percent of revenue, the success of your analytics initiative depends on your ability to source, cleanse, secure, and visualize your data. That’s the LRS sweet spot. Let us help.

The LRS Big Data and Analytics team has over 20 years of experience in statistics, information management, predictive analytics, and data warehousing. If you are interested in understanding how we can help you find value in your data with advanced analytics, please fill out the form below to request a meeting.

About the author

Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.