Big data analytics and pro sports are no longer strangers to each other. Nowadays, players wear accessories like the Adidas miCoach, a device that tracks everything a player does during a game from registering the heartbeat of players to the amount of steps taken. For a long time, people argued that hockey was resistant to the kind of data analytical models that were used in for example baseball. Hockey was too fast of a game compared to baseball with its stately pace. Players go on and off the ice about every 30 seconds while the game is still in play. If you thought data analytics weren’t coming to the NHL, you were wrong. (Olavsrud, 2015)

The National Hockey League, or NHL, is the biggest league of hockey in the world. It consists of 30 team based all over the United States and Canada and big money is made with starting salaries of 900.000US$ to the maximum of about 15.000.000US$. The general managers of NHL teams use data analytics for different purposes.

The first reason for data analytics is the players’ on ice performance. The first major system used is called Corsi, named after former goaltending coach of the Buffalo Sabres Jim Corsi. This so called Corsi is essentially the number of shot attempts (it is the sum of blocked shots, missed shots and shots on goal). This stat should indicate the approximate puck possession of a player. This stat show for example how well an individual player is doing, the higher the number the more your team possesses the puck, which usually leads to scoring chances. It can also be used the other way around, by showing how many shots your team gets against, when a player is on the ice. (Olavsrud, 2015)

Another reason for analytics is during contract negotiations with players. When the contract of a player expires, the general manager and representatives of the player get around the table to discuss new the new contract. Both parties are trying to convince the other party that they belong a certain contract, based on data. (Brousell, 2014)

Most recently, the youngest general manager in NHL history, John Chayka, was hired at just 26 years old. Once a promising young winger, whose career ended because of a back injury, Chayka focussed on data analytics for hockey. He began at a hockey school registering all data by hand. As time went on Chayka was introduced to multiple NHL teams and players who were impressed by his skills. Right now Chayka is general manager of the Arizona Coyotes, that he wants to make a data analytic franchise. Everything, from contract negotiations to on ice performance, will be analysed. (Sportsnet, 2016)

The revolution of using data in hockey has taken a huge step last year. I think more team are going to focus on using big data for every aspect there is in hockey. Only time will tell.

References:

Brousell, L. (2014) ‘8 Ways Big Data and Analytics Will Change Sports’ [accessed 5 Oct 2016] http://www.cio.com/article/2377954/data-management/data-management-8-ways-big-data-and-analytics-will-change-sports.html

Olavsrud, T. (2015) ‘NHL seeks to grab fans with data analytics’ [accessed 5 Oct 2016] http://www.cio.com/article/2901264/data-analytics/nhl-seeks-to-grab-fans-with-data-analytics.html

‘Coyotes’ John Chayka: the NHL’s answer to Mark Zuckerberg’ (2016) [accessed 5 Oct 2016] http://www.sportsnet.ca/hockey/nhl/coyotes-john-chayka-nhls-answer-mark-zuckerberg/