For most people, technological advances mean nothing more than just the latest buzzwords. Most laymen will not understand how these technologies work.
But apply these technologies in the things they use or to aid activities they do, then they appreciate it a lot more.
This is the case with analytics. Most people will have heard of it, but they may never fully understand it until they see it in use in something that they know. For example, in sports, such as hockey or baseball.
Analytics have been used in sports for a very long time
It was in the early part of the 1970s when sabermetrics came out. This was an early form of using analytics in professional baseball.
Sabermetrics is simply the use of statistical analysis to baseball logs and records. It is used to compare or evaluate an individual player’s performance.
It will take decades before the rest of the professional sports world will follow. Baseball statistics lent itself well to analytics. There are specific start and stop points. Sabermetrics was done manually.
Soccer, basketball, and hockey are now using analytics. This is because tracking technology has made it possible to get automated data rather than relying on information that was manually gathered.
Chaotic? Yes, but hockey may be quantified.
You might think that it’s impossible to quantify hockey. After all, this is a game of speed. Players can skate through the ice at 35 miles per hour, way faster than the speed limit for cars in school zones.
Don’t even talk about pucks, which can go as fast as 100 miles per hour. Surely it’s going to be very difficult to turn the performance into numbers and then analyze the data objectively?
The National Hockey League is not listening to that and is moving forward with using technology in sports. It’s the era of big data after all.
But while the NHL is only now putting this in place, we can already expect to see how these changes will affect the sport as early as the 2019-2020 season.
What is in the works?
The NHL has statisticians that use the Hockey Information Tracking System to record data about a player, such as his time on ice, face-offs, and a host of other data. The data is collected manually and it is fed to the application by a number of people.
It is time-consuming and not to mention needs a lot of manpower to do. You can also imagine the margin of error for this type of system.
However, professional hockey has been working on a way to automate the data tracking. They now have tracking chips embedded into hockey pucks and players’ uniforms. The NHL also placed sensors around the rink.
No matter how fast the movements are on the ice, the sensors and chips will be able to capture everything. You are able to get information, such as distance, direction, and several other metrics. In short, hockey just became easily measurable.
How much data does that involve? To give you an idea, the chips in the puck and players’ uniforms will generate around 200 data points for every second for each player. There are five skaters and one goalie for each team and the game lasts for an hour.
That translates to around 9.4 million logged events for one NHL game. That’s how much data we are talking about.
But all that data is not enough
While data collection in itself is an achievement, it’s not useful to you if you do not analyze it, as with other types of data.
The NHL adds to the wow factor by using artificial intelligence to do just that. AI will be able to identify plays when it crunches the data. For instance, it can tell if the goalie was out of his position or if the score was because of a two-on-one rush.
What’s more, AI analytics will learn more and be a better analyst as more data is fed into it. You also don’t have to worry about human bias and error.
Analyze each move, even in the fast-pace game of hockey
Hockey has made inroads as far as analytics is concerned. It has come a long way from manually tracking statistics such as goals and assists.
The analysis was done on these statistics in an effort to know more about the player’s and the team’s effectiveness on the ice.
Today’s analytics, however, allow you to track different types of information and statistics in real time. You are able to track more statistics, as well. You can expect that this analytics coup will bring you new insights that were impossible before.
You are no longer limited to just the assists and goals. You can now measure how much fatigue can affect a player’s performance, top speed, and reaction time.
You will have all these details at your fingertips. It can show you what’s wrong and what’s right with the players and where they can improve.
What does it mean to different stakeholders?
The new insights from this data will help all stakeholders. For instance, hockey teams will have better insights into their strategies. No more relying on gut feel and hunches. The results for the team can be quantified, which can be used to fine tune plays and strategies, and even uncover talents among team members.
Meanwhile, broadcast networks can use the real-time data to provide better information to their viewers. You can also add value to replays by adding more information after all the data has been processed.
Fans, on the other hand, can gain a better understanding of the game. Seeing all that data processed and presented into something that is easy to understand, they will be able to see what happened, why it happened, and how it came to be.
When they understand the game better, fans become more engaged. Engaged fans are a goldmine for sports teams.
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What we’re seeing now is just the beginning for the use of big data for the NHL. Because they use big data analytics and artificial intelligence, the insights that they are able to produce will get better over time. It’s an exciting time for professional sports, thanks to big data.
Photo courtesy of the NHL.
