Andy Potts Andy Potts
KHL press office KHL press office
exclusive for khl.ru
Just a month after the All-Star Game in Kazan, smart pucks and chips were tested in a championship game for the first time as Jokerit wrapped up its home campaign.

In January, the All-Star Game showcased some unique technology: the players representing the four divisions took to the ice in uniforms that featured microchips ready to record the speed and distances achieved by the skaters. In addition, there were similar chips in every puck. At the end of February, that technology was put to the test in competitive games during Jokerit’s final homestand of the regular season.

Real-time data from the chips was displayed on the media cube at the Hartwall Arena and also featured in TV broadcasts from the games.

“It’s clear that these stats open up new opportunities for us in terms of broadcasting games and giving viewers more interesting information,” said Viasat’s Iiro Harjula. “As broadcasters, we are interested in getting as much detail about the players as possible to help us confirm our judgements of their speed and power. In addition, it helps to uncover the potential in some players. We hope to be able to use this system in every game next season.”

It’s a fully automated system. Aside from the chips in pucks and jerseys, sensors are fitted around the arena to harvest the on-ice data. It’s reliable and energy-efficient: the chips themselves weigh a couple of grams and have sufficient power to work for the whole season without recharging. The whole analytical platform is fully automated and the set-up is scalable. No matter how many teams play in an arena, they can all benefit from the technology. This means KHL clubs can generate the same level of data for all their teams, from the top flight down to the youngest kids rosters.

The system’s potential benefits to coaching staff are enormous. This data enables them to adjust their strategies in real time, based on what is happening out on the ice. The shot maps can reveal which opposition players block most shots, and from what positions; this helps to fine-tune the work of individual lines and deploy specific groups to better effect. In addition, coaches can immediately highlight errors at the end of each shift, using models of every passage of play. Each player can get personalized email reports, created and sent after every game or practice. Meanwhile, beta testing is already underway to synch the game model to TV footage. The technology should be launched by the start of next season.

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“We were delighted with how quickly the KHL and its partners got everything set-up for the trials in Kazan and Helsinki,” said the system’s developers. “Our AI is evolving all the time, we are constantly adding new features: we want our system to offer more in-depth information about what is happening, why it happened and what can be done differently. We are currently testing a transmission map that shows the accuracy of each player’s passing and the distance he travels to reach the puck. Another priority for us is to create a 3D map of the shots. We are also working on biometric data and the possibility of tracking player fitness. One of our big challenges is synching all this with video to give more options to broadcasters.”

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The technology isn’t just for players. Referees will also wear the chips and the officiating department will be able to assess the mobility of refs and linesmen as well as identifying their positions during key incidents.

“Getting this information in real time is a game-changer,” said Viasat commentator Antti Makinen. “For me, the biggest surprise was seeing that Marko Anttila was the fastest skater in the game against Dinamo Minsk. This kind of detail can changes attitudes towards players. This system offers far more than traditional stats. Its value to TV broadcasts is obvious, as its the interest for fans and everyone else at the game.”

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At the time of the All-Star Game, we looked at the heat maps that show puck possession, the images that locate every shot and the shift-by-shift analysis of each player’s contribution. But the system offers another possibility: momentum data tells the story of the game, showing which team held the initiative at different times and what influenced every change.

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Jokerit – Spartak

The extra stats from the Spartak game, which Jokerit won 5-4, show that the Finns were much more clinical in taking their chances. Spartak controlled the puck and won more face-offs at both ends. However, the momentum charts show that two of Jokerit’s last three goals came against the run of play, at a time when Spartak had the initiative on the ice.

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Jokerit – Slovan

Against Slovan, Jokerit enjoyed a huge statistical advantage which was reflected in the 7-1 scoreline. The visiting players were forced to skate much harder, and three of them completed more than 6km in the game. When the system is applied across all KHL games, these stats could become a key part of assessing player fitness.

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Jokerit – Dinamo Minsk

Dinamo won the final game of the regular season 5-3 and the stats show that the Belarusians succeeded due to clinical finishing. Jokerit held the initiative for much of the game but Dinamo was better at converting its chances. Among the player data, it’s worth noting Teemu Pulkkinen’s 153 km/h shot and the speed of giant Finnish forward Marko Anttila.

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With this pilot project successfully completed, the stage is set for the KHL to become the first hockey league in the world to digitize every game in real time. When the system is fully implemented throughout the league it will offer new opportunities for everyone: players can explore detailed analysis of their sporting development, coaches can refer to even more information in real time, the fan experience is enriched – in the arena and on TV – by more detailed data, and commercial partners can create new offers and advertising opportunities.

Andy Potts Andy Potts
KHL press office KHL press office
exclusive for khl.ru

Related clubs

Dinamo (Minsk) Dinamo (Minsk)
Jokerit (Helsinki) Jokerit (Helsinki)
Slovan (Bratislava) Slovan (Bratislava)
Spartak (Moscow) Spartak (Moscow)

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