Web Sant, the author of the analytics blog, “Hockey Simplified,” is not one to mince words. It’s what drew this author to examine him and his positions in the first place.

Isn't Corsi dead yet? pic.twitter.com/Epj6XK4iLl — Web Sant (@web_sant) July 3, 2015

Yet while his bristly demeanor on Twitter has earned him few followers, it belies a nature both introspective and measured when it comes to his analysis.

“Barring spurious correlations, any metric that has a statistically significant relationship to a desired outcome is not ‘altogether useless’…” he tells me after I question his coarse wording of the above tweet.

However, he opines, with CF%’s (Corsi For Percent) low R-squared (the numerical correlation of any statistical relationship, which he estimates – in this case – at around 0.25) “…It should never be a surprise when a high Corsi team doesn’t win a lot… Nor should it ever be a surprise when a low Corsi team does.”

Mr. Sant continues: “And, all this business about post hoc explanations drives me up the wall: The Kings had great Corsi but they missed the playoffs because of x… OK… That’s fine… Build a new model that incorporates both Corsi and x and test the new model. Back-test it against prior seasons and see how it works… But, one-off explanations of the lack of predictive ability of a low R-squared metric are silly.

“So, useless? No.”

“Highly useful?”

“I would say no.”

“The numbers indicate, to me, that Corsi can’t carry the load that some hope it will.”

The use of analytics in hockey has slowly emerged from the Dark Ages a simple decade ago, when, in the wake of Baseball’s Moneyball-inspired renaissance, new hockey statistics emerged to help redefine how to divine player usefulness. There was a strong backlash, but eventually the hegemony of the old gave way to the new advanced-metric-driven world we live in today. But is this just another false dominion… coming up with the answer to only a quarter of the question?

“If there is to be a ‘Moneypuck’,” Sant states, “then higher R-squared metrics are needed.”

One of Sant’s most notable conclusions is team-level assessment of hockey schemes.

“I think that the biggest opportunities in NHL hockey are at the macro level,” he posits, “… that is to say, strategy and tactics.”

“Always be trying to score 4 while expecting to allow 2.” It seems like direct logic, but Sant’s exhortations are as profound as they are straightforward: “If you go down a goal, don’t tighten up to try to prevent another goal. You should have known they were likely to get 2 (No big deal).”

“[If] you go up 1 – 0. Don’t sit on the lead. The likelihood is, the other team is going to score a goal.”

“Some coaches have said that the ideal score on the road going into the third period is 1 – 1. Is it just me or is this silly if you’re trying to win the game in regulation?”

The St. Louis born-and-raised Sant has extrapolated some of his findings to personnel questions. His VSM (Very Simplified Model) or Sant.score for players is a multiple linear regression of goals allowed and goals for. Through it, he has emerged with a metric with what he reports as a significantly higher correlative rate (R-squared) than either CorsiFor% or FenwickFor%… and the results are enlightening.

VSM (Sant.score) for Forwards in 2014-15.

VSM (Sant.score) for Defense in 2014-15.

Sant, an avid Blues fan recollects a Bluenotes’ coach saying that their goal was to “focus on the zero,” an objective he found “silly” in light of his analysis. Even when it brings him into indirect conflict with his favored franchise’s someday-Hall of Fame head honcho.

But Sant didn’t set out to actively disprove what’s become conventional wisdom. When he started, he was “a complete agnostic about hockey stats.” But the years of fandom and experience with scientific statistical analysis (Sant, who possesses both Bachelor’s and Master’s Degrees in Physiological Psychology from Amherst College and UCLA), birthed in him a desire to understand and account for the true means of success in the NHL.

“My initial interest was,” Sant explained, “how do you win games? Specifically, how many goals do you need to score and how many should you expect to allow.”

“… After all, you won’t win many games in the NBA if your objective is to score, say, 50 points per game. Hockey is not dissimilar, but the numbers are different.”

His investigation took him beyond the straightforward, and after coming to the conclusion that several long-held statistical edicts of hockey were false (for example: the importance of faceoff percentage, blocked shots, and the detrimental effect of back-to-back games); I started to wonder whether much of the conventional wisdom about hockey was wrong. This led to further inquiry:

“I knew nothing about so-called “advanced hockey analytics” so I started reading up on them. Many seemingly smart people seemed to hold Corsi and Fenwick in high esteem. When I ran the analysis and generated the accompanying chart I was very, very surprised… Yes, most of these relationships are statistically significant. But look at the R-squareds.”

A successful Human Resources manager and businessman, Sant hopes that his approach will encourage further exploration of the true factors behind success and failure in hockey, and perhaps even earn him a place consulting with a NHL franchise on personnel and strategy.

While acknowledging the usefulness of Corsi and other possession-oriented metrics, he decries the tunnel vision of the ‘so-called experts:’ “… To [the] extent that the analytics community is using Corsi and Fenwick to evaluate players not only have they not struck gold, they are not working in the right mine.”

He concludes, “Yes, I think there are big opportunities in individual player analysis.”

Undoubtedly there will be those who dispute his claims and brush his analysis aside, but Sant will continue to work towards a more harmonious union between the numbers on the ice and those behind the curtain.

As for this observer, I will continue to use the flawed metrics of Corsi and its ilk; but with even more of an awareness of their shortcomings, thanks to Mr. Sant and his endeavors to evince the true ‘power behind the hockey’s analytics throne.

It may take some time, but through the efforts of folks like Web Sant, we may reach some statistical Shangri-La after all.

Follow Web Sant on Twitter at @Web_Sant

Follow Bob Mand on Twitter at @HockeyMand