Moneyball on Ice: Canadian Startup’s GPU-Powered Hockey Analytics Aims to Give Teams an Edge

Baseball has the now legendary story of Moneyball, with the under-financed Oakland A’s outdueling big money teams by using sabermetrics to analyze player activity.

To match a game as fast and fluid as ice hockey, Toronto-based startup ICEBERG is aiming to write the future of the sport using analytics powered by computer vision and infused with AI.

The slightest competitive advantage can mean the difference between winning and losing in professional sports. It’s why powerful analytics tools for athletic teams, particularly in baseball and soccer, have become so widespread.

Ice hockey has missed out on the analytics revolution, largely because its speed and complexity have made a nuanced understanding of the data behind player positioning and activity all but impossible. However, Alex Martynov, CEO of ICEBERG, sees this as a golden market opportunity.

“I always felt that data was a crucial part of every industry, and I was amazed that baseball and soccer were into the data, but hockey and other sports were very far behind,” said Martynov.

In 2014, Martynov left his job as an analyst for a bank in Toronto, and began applying his analytical and statistical skills to the sport he loved so much.

With a seed investment from his father, and the help of a few hockey-crazed developer friends in Russia and Canada, Martynov set out to use a combination of computer vision and machine learning to start collecting and analyzing data from hockey games.

A Million Data Points Per Game

ICEBERG’s name alludes to the mountain of data hidden under commonly tracked stats such as goals and shots on goal.

The company’s initial five-person team developed algorithms that use video from three cameras that form a panoramic view of a hockey match. This turns a 60-minute game into more than a million data points — and a trove of intelligence — that teams can analyze to better use players and craft game plans.

The process starts with computer vision that’s used to track objects (such as players, opponents and the puck), identify players based on the color of their jerseys and the numbers they wear, and to detect the location of the puck — itself no small task given it can travel at speeds greater than 100 mph.

A machine learning algorithm then logs events — such as shots, passes, body checks and simple possession of the puck. With these, it creates a database that it interprets for its roster of clients, which thus far consists of some 20 teams spanning multiple hockey leagues all over the world, including the NHL’s New York Islanders.

Power of Cloud-Based GPUs

Clients access the visualized data via a cloud-based portal. NVIDIA GPUs running on the Microsoft Azure cloud platform handle the heavy-duty calculations and train the neural networks that power ICEBERG’s algorithms.

“GPUs are much better at handling such work,” said Martynov. He notes that GPUs deliver significantly lower latency than CPUs, which ends up saving the company money. GPUs are also used for inference. The company now employs 25 people, split between its Toronto headquarters and a Moscow R&D facility.

The company started testing cuDNN six months ago as it looks to ramp up the sophistication of its machine learning model and train its neural networks to better identify events from coordinates, something Martynov said is “not a trivial thing.”

Using its service, teams can better understand all sorts of game factors, such as where offensive players are most deadly with their slapshots, where goalies tend to let the puck by most frequently and when a player’s stamina starts to flag during their shift.

Big Plans for Nascent Tech

While the technology isn’t being used during this week’s Stanley Cup Finals pitting the upstart Nashville Predators against the defending champion Pittsburgh Penguins, Martynov hopes to analyze such high-profile events in the future.

He also aims to apply ICEBERG’s technology to other sports where he believes it can improve on the analytics tools currently in use.

Soon, Martynov also hopes the company can make its analytics visualizations available for broadcasters. Not only could they call the game with more sophistication, it could help hockey attract casual fans who have a hard time following the fast-paced action on television.

He said that with partners like NVIDIA and Microsoft behind it, there’s no limit to what ICEBERG can accomplish.

“The technology is progressing so fast,” said Martynov, “that it allows us to do really cool things in such a short time span.”

Watch ICEBERG’s tech in action in this video:

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  • Jason

    One of the most funniest blogs, I’ve read in a while, LOL!
    “Martynov set out to use a combination of computer vision and machine
    learning to start collecting and analyzing data from hockey games.” – Yeaaaahh right… Machine learning?!? LOL ha ha! More like a bunch of Russian students, locked up somewhere in the middle of Siberia, collecting data by hand and doing everything manually. Well, this looks to me like a big scam.

  • archenroot

    I am working on something similar and actually this is already possible. Although my experiments doesn’t look that cool. Manually you just need to train the network with your specific data set of images. Then assignment between numbers = players is done before match start.

  • tonchen

    perfect