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Why enhanced statistics are a game changer for NHL contract negotiations

Tuesday, 29 September 2015 By Ryan Lake

The 20th century saw multiple attempts to quantify and measure every aspect of an athlete’s performance. The desire to measure and compare the performance of one player to another to develop criteria in which to establish differences and find similarities between and among players has driven individuals ranging from team executives, players, agents, and fans to push the limits of traditional statistics.

This article examines how players and player agents have been and could further utilize enhanced statistics to bring a new and more sophisticated approach to the contract negotiating table, which has the potential of revolutionizing the sport of hockey. The article will first discuss the genesis of advanced statistics, second highlight the enhanced statistics recognized by the NHL, and third explore how players and their agents can utilize the information provided by the analytics to change the way player contracts are negotiated.


The genesis of statistics

Baseball pioneered the field, which has become known by many names including: Advanced Statistics, Sabermetrics, Analytics, and now Enhanced Statistics. Earnshaw Cook was one of the earliest innovators in the field.1 Cook’s book, Percentage Baseball, which was first published in 1964, was widely criticized at the time.2 However, Cook’s research laid the groundwork for Bill James and others that developed the modern day advanced analytics, a field that has become widely accepted in baseball and has now started to cross over into other sports.

One of the most notable examples of the use of advanced analytics was demonstrated by the Oakland Athletics in the 2002 season. Billy Beane, the manager of the Oakland A’s was presented with a unique situation where his team did not have the budget to compete with the other teams in the league to acquire and retain the top talent in Major League Baseball.3 In an effort to find talented players on a budget, Beane turned to the use of sabermetrics.4 The team assembled by Beane and Paul DePodesta also known as Peter Brand, the assistant general manager of the A’s, set a league record in consecutive wins and reached the postseason.5 The success of this season was detailed in the book, Moneyball: The Art of Winning an Unfair Game, by Michael Lewis.6 Later the true story of the A’s unique and unprecedented approach to building a team was immortalized on the silver screen in the movie, Moneyball, starring Brad Pitt as Bean.7

Many, have dismissed the applicability of advanced statistics in sports other than baseball. The cynics have long said that advanced analytics work in baseball due to baseball’s unique characteristic of being a team sport defined by a series of one v. one battles.8 However, this has not stopped dedicated believers from developing algorithms that are capable of accounting for the team nature of the other sports.

In the last ten years, there has been a revolution, developing under the surface in the hockey blogosphere. The movement, largely led by Matt Fenwick and Tim Barnes (aka Vic Ferrari), the creators of the Fenwick stat and Corsi stat respectively, burst onto the hockey scene in 2015.9 Throughout the summer between the 2013-14 season, several teams in the National Hockey League (“NHL”) hired experts in advanced statistics to help scout and acquire talent.10 In February of 2015, the NHL formally recognized advanced statistics, or as the league refers to them enhanced statistics.11

Traditionally advanced statistics or enhanced stats, have been a tool utilized by teams to build a successful team on a budget. However, analytics are starting to have a much broader application. 

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Written by

Ryan Lake

Ryan Lake

Ryan is an American attorney at Lake Law Group, LLC and a sports consultant at Beyond the Playbook. He works extensively on ice hockey, soccer, baseball, basketball and Olympic movement issues. Ryan is also an Adjunct Professor at St. John’s University School of Law.

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