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Do The Fancy Statistics in the NHL Really Mean Anything?

When placing the letters “NHL” into Google, those interested in the global sport and best league of said sport are given links to major news outlets like NHL.com, ESPN, TSN, a whoever-edited Wikipedia page, and links to the more popular sports like the NBA, MLB, and NFL.

Those interested are also faced with other often-searched questions about the NHL such as, “what NHL teams no longer exist?” Or, “where is the NHL Network located,” and “how is the NHL divided?”

One question that does not pop up to a potentially new fan of the sport or an NHL historian that likes looking up the variations of peculiar divisional gerrymandering the NHL has gone through over periods of team expansion and volatile hockey markets is how to ascertain all the fancy statistics that writers and (uh) mathematicians have constructed to help better understand the game.

Yeah, but what is his Corsi, CF/60, CF%, SR/GP, QoT, or GS/60?

There are numerous advanced statistics (and sites, for that matter) that fans and arm-chair pundits can reference in order to argue their points as to why Mark Giordano or Ryan Suter could be considered a better defenseman than players like P.K. Subban and/or Drew Doughty.

Both are Norris Trophy winning defenseman and Doughty has won two Stanley Cups, two gold metals to boot too, yet the argument of the former two being better than the latter two was stated in a “Top 5 Defenseman in the Western Conference” article somewhere in the hockey ethos where everyone has a say.

All the statistics listed in the heading above are metrics used to both distinguish the most minute and inscrutable details of the game from other minute and inscrutable details of the game and to predict the propensity of a specific series of events performed by a player or team happening and that those predicted outcomes can potentially dictate the conclusion of the game.

Corsi is a metric that measures all shots attempted within the game including missed shots, blocked shots, and shots on goal. Fenwick is another shot-differential measurement that excludes blocked shots.

But, a team can be outshot and still win the game. And just like every other single professional sport, the final score determines the outcomes of games. Even non-sports like golf, pool, or darts have a scoring system that dictates the outcome of the game.

Then there are derivates of these metrics like measuring them through 60-minute periods, specific situational performances like 5v5, 5v4, 4v5, etc., measuring players relative to specific their teammates/line mates, and/or where each individual player’s shots were taken from and whether they were taken from high-danger, medium-danger, low-danger areas in the offensive zone.

Defensively, players are evaluated by their ability to compress shots against and drive the play towards the opposition’s net. These metrics can be elaborated on by comparing how a specific defenseman plays while being paired with another particular player and whether that defensive pairing works well together or not.

In 2015, I had once argued that Los Angeles Kings’ goalie Jonathan Quick should be considered elite when compared to other goalies like Carey Price, Pekka Rinne, and Henrik Lundqvist using measurements like save percentage, goals against average, and wins in both regular season play and the playoffs.

Comment trolls argued that these stats were antiquated and that measurements like adjusted save percentage is a more telling statistic. Kind of like how the gross domestic product of a country can be adjusted with inflation to make real gross domestic product (rGDP) as a more feasible economic measurement.

This small adjustment is just one metric that can draw parallels to micro and macro economic applications that economists have both theorized and put into practice since the .

However, like Nobel Memorial Prize in Economic Sciences and Distinguished Professor of Economics Paul Krugman said when discussing the 2008 financial crisis, “economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.”

Mistaking a Stripper for a Significant Other

In his 2009 piece, “How Did Economists Get It So Wrong?” Krugman argues that regardless of the unbelievably advanced applications to theoretical economics in contemporary markets, there was some that fell to the allure of fancy algorithms when improperly predicting and handling the 2008 financial crisis.

This argument can be attributed to the emphasis placed on advanced NHL statistics.

NHL writers, statisticians, and even professional NHL managements staffs have taken a liking to these advanced stats to the point of even signing players to salaries based on the potential maximum output a player has. But, these are anticipatory measure that could potentially determine the outcome of a players contributions and team metrics. Not, the outcome itself.

The outcome of a game, like the 2008 financial crisis, can come out of nowhere. It may be unlikely that a team being outscored 3-0 in the first period wins the game, but there has sure-as-shit been comebacks that defy these statistical odds. Plus, some comebacks are far more memorable than some team successfully dumping and chasing the puck while driving puck possession.

Fancy stats are that: fancy. They are a way for pundits who never played the game professionally to separate themselves from other fans and writers in order to be cooler or more respected even though there are other (and maybe even better) ways to analyze the game.

You can still enjoy the game without knowing what corsi or fenwick means and without having to do the same marked scatter chart I would do in general Biology during my ten-year tenure at community college.

Have a drink, eat some shitty food, and yell at the screen while watching the game because advanced statistics are like clicking on a pornographic thumbnail only to find no money shots, or studying surf reports to only sit out and tread water, or believing that bigfoot is real.

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