While sports analytics is hardly a new idea, how it is being used in hockey has changed dramatically. Coaches no longer have to wait for newspapers to print out statistics. Fans are not left to wonder how their favorite players are truly excelling. What has brought about this change? Big data.
Big data has taken all forms of sports analytics by storm, but we are only now really beginning to see what it can do for hockey. Data tracking technology has taken the work from the analysts, giving you statistics in real time. This is what we know.
What KPIs Do Hockey Decision-Makers Track with Big Data?
To begin with, let’s look at what information is being tracked in hockey analytics. Analysts are looking at how much a player is on the ice, what the ratio of face-off wins is, the types of shots that each player prefers, and the effectiveness of each player out on the ice. Prior to the introduction of big data, coaches and managers were leery about stats, simply because they were highly prone to human error.
Since automated tracking appeared only this year, information has changed. Pucks are now being fitted with tracking chips that can collect the information on the puck, including its speed, direction, and movement around the ice. Likewise, players now can have chips within their sticks and their shoulder pads which also takes in the player’s speed, position, time on the ice, and even just how he moves.
Looking at how many metrics make up the data collected in hockey, it is important to recognize that the type of chips used previously had points around 200 times per second in the one player’s gear. If you think of a chip being on every player on the ice, as well as the puck, the amount of data points is extensive. It has been shown that over the course of 60 minutes of play, there are over 9 million events being logged in each category.
That amount of data is beyond what a single analyst can review quickly, so the NHL has turned to using AI to pull out the data that is deemed more important. The AI can be programmed to look for certain movement or plays, finding out where plays went wrong. The best part about the AI is not that it can sift through the information, however. It can sift through the information completely neutral and unbiased, accruing information that coaches and managers alike can trust.
How Does Hockey Benefit from Big Data?
While it is obvious how this information can benefit a team, it is not just a team that can learn from big data.
- The Team: Let’s start with the most obvious answer. The team’s coaches can use this to make better plays and use players more effectively. The players themselves and see where they need work as individuals. The managers can also see which players are not working well with the rest of the team and what changes they might need to make in the future.
- Brands: Even hockey brands can benefit from the information that has been collected. Looking at what a player is using for gear can tell brands what is and is not working for them. For example, CCM might discover that the Bauer Supreme 2S are the best hockey skates on the ice and alter their next line to be more comparable.
- Media: Newspapers, television broadcasts, live streams, and even social media can benefit from big data. They can take the information from the system and compare it to what they have seen on the ice—all still in real time. Sharing information with fans as they are watching it will naturally be a boon to the industry, keeping viewership up.
These are all factors to keep in mind.
Big Data Has a Huge Impact on the Hockey Profession
The information that is coming out of big data might seem like a lot to take in, but with the right analytics, it is only going to improve the NHL, the perception of the game, and how the game is played.