Going all in on ShiftChart’s new hockey app


ShiftChart-logoEmerging tech entrepreneurs Sai Okabayashi and Erik Iverson have created a most unique hockey visualization app that has the potential to become the de facto tool for measuring and optimizing the amount of ice time among NHL hockey players.

Using D3 on the front-end, angular.js, couch.db and node.js — ShiftChart is a currently a single page app providing near real time (2-3 minute delay) aggregated shift data for all hockey games and players during a season.  Their archived data goes back as far back as 2007/08, with complete access made available to everything this season, every minute of who was on the ice and when.

“There’s something harder to keep track of than the puck when it comes to this game,” says Okabayashi, a self-described hockey aficionado with a penchant for stats.

“A player is usually only on the ice for a quick turn and it’s always in constant flux with players going and coming,” he says.  “This chess match makes it hard for fans to keep up.  And beyond that…what is the relationship been the Blackhawk’s best defensive player and the Wild’s best offensive player so far this series?” he asks from a different perspective.

“That can matter,” he assures me.

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ShiftChart is young but smart. With a little tweaking and polish, it fits well with the future of sports, a world with coaches and analysts looking for that edge at every turn.   “The concept of a shift chart isn’t new, but what we can do with ShiftChart is bring forth the interactivity of big data like never before.”

Okabayashi is an East Coaster who moved to Minnesota in 2005 for PhD in statistics at the University of Minnesota.  His co founder, Iverson, is a Wisconsin native who runs the Twin Cities R User Group. The duo met while working at SavvySherpa and started ShiftChart last summer; in a most un-Minnesotan fashion, Okabayashi left his comfy job two months ago to go all in and pursue ShiftChart full time.

“The focus is to turn this in the the ultimate hockey analytics site,” Okabayashi asserts.  “When you start to look at patterns over time, comparing and contrasting other data points, that’s when things get really interesting.”

Two obvious approaches they see towards revenue include targeted advertising and a premium version for the hardcore users, such as coaches or analysts who stand to win more and profit more with access to such intel.