Hockey’s Stanley Cup playoffs began last Thursday, and most hockey fans –particularly those whose teams are in the hunt – are in heaven. But hockey fans who also are students of analytics and data visualization are in a sort of purgatory, because hockey lags behind other sports in adopting advanced statistical analysis of players and teams. Put another way: There’s no Moneyball for hockey.
Don’t take my word for it. A panel at last month’s MIT Sloan Sports Analytics Conference (called Hockey Analytics: Out of the Ice Age) noted that the rink “remains a less-than-forgiving climate for perfected analytical judgment and implementation.” That’s changing, though: Conference organizers also said “in-depth statistical analysis has permeated the front office of NHL teams” and noted that fans are getting in on the data action.
Maybe hockey can learn something from the Internet of Things (IoT). Think about it: The data collected by sensors in factories, vehicles, and infrastructure is more varied, voluminous, and faster-paced than business data of just a few years ago. Developers have created systems – often using BIRT – to analyze and derive value from this data.
Similarly, hockey teams are moving beyond basic stats (like wins, goals, and time on ice), collecting more data, and trying to build models based on that data to deliver insight. Already, new statistics are growing more important and valuable.
The most popular modern hockey statistic, the Corsi (named in 2007 after goaltending coach Jim Corsi, who was just released by the Buffalo Sabres), counts shots on goal, including misses and shots blocked by players other than the goalie. (That definition is oversimplified; sportingcharts.com has more detail.) Some hockey fans say the Corsi shows the “tilt of the ice,” and studies of past seasons show that the Corsi tracks pretty well with puck possession, zone time, and most importantly winning percentage.
For developers who want to analyze NHL data – and maybe discover the next Corsi – I recommend two sites: Behind the Net has NHL stats plus a 10-article series about statistical analysis in hockey; its downside is an unfortunate relationship with online gambling sites. You might prefer Hockey Prospectus, which has detailed records for every shot taken in the 2012-2013 and 2013-2014 seasons, an extensive hockey blog, and some lively interactive data visualizations of the NHL draft.
In the future, real-time data collection and advanced data analysis – coupled with the knowledge and experience that only humans can bring to the game – might reveal other correlations that can help teams rack up more wins. Who knows – maybe the infamous FoxTrax (also known as the glow puck) will be resurrected, with embedded sensors, to aid with puck tracking. One thing is certain: Greater use of data and personalized analytics is coming to hockey. After all, if Miami and Phoenix can host professional hockey teams, anything is possible.