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What Is Ice hockey Data?

Ice hockey is one of the most popular sports in the US and the numbers indicate its popularity will continue to grow. Ice hockey data, therefore, is used by increasingly large numbers of people, from fans to coaches to advertisers.

In addition to detailed information about ice hockey players and team performance, ice hockey data tracks match viewership and fan affiliation for commercial and team use.

Where Does Ice hockey Data Come From?

Most ice hockey data comes from official channels like the NHL (National Hockey League) and sports media. Local leagues, coaches, and players also generate a lot of data, though much of it will be proprietary.

Wearable technologies have in the past few years become important sources of player performance data, as well. These wearable sensors track player movement in real time and export the information to software systems for analysis.

What Types of Columns/Attributes Should I Expect When Working with This Data?

Naturally, the most common type of ice hockey data attributes concern players and teams. The data collects a wide range of information, from player position and time on the ice per game to the number of shots, blocks, and penalties.

Another common data metric is match viewership numbers. Team and league financial futures depend on this information and related data, like viewer demographics, help teams increase their viewership numbers.

What Is Ice hockey Data Used For?

Coaches and players are the main users of this data. They design training programs, prepare for games using play-by-play data on their rivals, make drafting decisions, and so on.

Ice hockey fans use a great deal of data, as well. They discuss team performance, place bets, play fantasy leagues. Many fans interested in playing like their favorite athletes use training data and wearable technology to raise their own performance level.

Finally, of course, companies use ice hockey data to advertise to the sport’s fans.

How Should I Test the Quality of This Data?

Essentially, a good ice hockey dataset fits its intended purpose. In other words, an amateur athlete needs a different set of data than an activewear brand looking to attract fans.

Secondly, as with any dataset, users must ensure the data is complete, accurate, and clean before trying to use it to support any action or decision.

Interesting Case Studies and Blogs to Look Into

Towards Data Science: NHL Analytics with Python
Hockey Graphs

Tangible Examples of Impact

However, behind the curtain, each NHL team will get what SMT is calling a “fire hose of data.” Players will produce 200 data points a second and the puck will register 2,000 data points a second.

“This data is fundamentally different. Instead of just looking at specific events, it’s giving you the locations and the trajectories of all players on the ice and the puck, at every moment in time. It lets you look at things that you would never even be able to begin to explore with data otherwise, things like positioning of defensemen and goalies, how much space is being created by particular players.”

Pittsburgh Post-Gazette: Meet the man leading the Penguins into a new era of hockey analytics

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