The 2022 MIT Sports Analytics Conference held a panel titled, “Data, Snipe, Celly: The State of Hockey Analytics.” It was moderated by Greg Wyshynski and the speakers were Dominic Moore, Namita Nandakumar, Meghan Chayka, and Brant Berglund. This post will highlight some key ideas brought up in the panel and look at the future of hockey analytics.
Watching Tom Brady throw a touchdown on TV is a pretty common occurrence, and it’s pretty easy to see where the ball is traveling. Once the play is over, though, there’s no way to exactly where every player and the ball had been. That was the case until 2017, which is when the NFL decided to implant chips in footballs to expand data collection. In 2014, the NFL instated player tracking through quarter-sized chips as well.
Hockey Data in the Past:
In the past, sports like golf, football, and baseball have paved the way for sports data analytics. Hockey, with its continuous game flow, has lagged behind. This is largely because in other sports, individual plays are easy to distinguish from each other. In football, there are whistles and play-clocks to indicate when a play begins and ends. In golf, every shot is a new “play.” For baseball, each pitch is a new play.
Conversely, hockey is essentially one continuous play, pockmarked with faceoffs—the only tangible and trackable aspect of the sport for a long time. For this reason, some of the best data from hockey to this point is about faceoffs, as they occur when the play finally stops. Brant Berglund offered the analogy: “if football is like a bunch of short sentences, hockey is one long run-on.” He reinforced the idea of faceoffs being the focus of hockey analytics by introducing a faceoff win probability metric that was put into place recently. By taking the possibilities of which players are taking the faceoff, the algorithm could use prior data to determine a probability of winning for each team.
There’s a common story within the hockey community that one of the reasons Wayne Gretzky grew to be so dominant is because as a kid, he’d watch NHL games and draw where the puck went. The dark shaded areas would be places for him to take note of during his games. Until recently, this was pretty much the extent of puck tracking technology.
Looking Ahead:
Over this past offseason, the NHL implemented chips and cameras to revolutionize puck and player tracking technology. In fact, the All-Star game featured a digital blue ring around the puck when consumers watched it on TV—an example of puck tracking. Soon, perhaps the NHL will be able to create graphics like this:
One other topic the panelists touched upon was the idea of consumer engagement with the sport. Typically, people who aren’t avid fans won’t sit down and watch an entire hockey game and enjoy it. Meghan Chayka brought up the idea of little tidbits of data that would be interesting for a consumer. For example, with the development of faceoff probabilities, the potential for sports betting on it. Or the percentage of 6v5 conversions if a team needs a goal late in the game. These are little pieces of information that might make hockey more enjoyable to watch for the consumer. Berglund expanded on this, asserting that an increase in hockey betting based on advances in data would be instrumental in increasing consumer support for the sport.
While there have been complications with data analytics in hockey in the past, the sport is quickly gaining ground in this field, with the data available to teams at an all-time high. Making valuable insights like never before, the NHL is trending in the right direction for more consumer enjoyment.
Images:
https://wall.alphacoders.com/by_sub_category.php?id=146229&name=Hockey+Wallpapers&filter=4K+Ultra+HD
