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Goals, assists, points and plus/minus are major categories entrenched in hockey statistics. Found on the back of hockey trading cards, on various online databases, and league websites, these measures of performance are basic, widely used, and heavily circulated. Traditionally, these would be the major areas for an organization to determine the value of a player’s worth. There has always been an emphasis on the intangibles in hockey: this player is a “character guy,” that one is a “glue guy,” players who may be lacking in skill but are nonetheless great teammates that hold the group together. Players who regularly find their way onto the score sheet with many goals and assists have always been valued higher than players without a strong stat line. But what about those players whose efforts cannot be summed up with basic statistics?
Given the undeniable reality that hockey is a complex sport with dozens of events occurring on the ice simultaneously, it is impossible to sum up an individual’s entire contribution to his team based exclusively on these elementary performance measures. Contained herein is an argument for the use of advanced statistics, the vehicle with which an observer may be able to get closer to gauging true performance. This is not an exact science; the goal is to quantify occurrences that by nature cannot be quantified. By pursuing the events that lead up to the eventual goal instead of focusing only on the players who passed and shot the puck, one can get closer to the previously indiscernible. Focusing only on the goal scorers is akin to only recognizing the lead actor in a theatrical performance, completely ignoring all the work done before the event and those who contributed to it like the set builders, costume designers, choreographers. This is what advanced analytics does in hockey; it goes beyond the plain to quantify and recognize performance for those who previously went unnoticed backstage.
Ultimately, it is about creating value for the club, and those organizations that ignore the benefits of data analytics risk letting a good player fall by the wayside only because they failed to realize what kind of unseen production he was bringing to the table. It is important to recognize that there is no perfect measure, no perfect stat that can evaluate a player’s play without error. There will always be room for the “eye test.” It is impractical to think that every on-ice event can be quantified and used to sum up a player’s strengths and weaknesses. Advanced efficiency statistics seek to inch closer towards highlighting true player performance by using analytical measurements to evaluate player efficiency.
Culhane, Travis, "Hockey Data Analytics" (2017). Honors Theses. 2822.