NHL Analytics: Why Moneyball will never work in professional hockey

Today in professional sports there is no shortage of advanced analytics to judge players and follow. While there’s been plenty of success and controversy with using these statistics in baseball, would Moneyball ever work as a part of NHL analytics?

While the NHL hasn’t dove head first in sabermetrics like Major League Baseball has, it doesn’t mean they haven’t tested out the waters. The New Jersey Devils were the first to hire former poker player and stats guru Sunny Mehta to a full-time analytic position in 2014, although he’s since been recently replaced by Tyler Darrow. Critics have deemed this the “Moneypuck” movement in NHL analytics.

Nobody can blame sports teams for at least being interested in the advanced analytic philosophy. For those of you who don’t know, or only watched Moneyball to see Jonah Hill attempt a serious acting performance, the basic idea behind sabermetrics if teams to try to find someone to do as good a job at a much smaller price by exploiting market inefficiencies.

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In the Moneyball example, it meant putting together a group of underlooked or undervalued baseball players to put together a competitive roster. It worked. Other teams took notice and started doing it as well.

The NHL is an entirely different story, however, and it doesn’t appear to be working. Perhaps one reason is the over saturation of leagues for NHL players. All players want to be in the NHL. However if they don’t get offered a contract, most deflect to the multiple international leagues, such as the KHL, that offer a lower, but still competitive salary. For baseball players, there is a huge difference in both pay grade and pride between the minor leagues and the majors.

The then youngest GM in the NHL Jason Chayka with the Arizona Coyotes bought heavily into the analytics-based approach in 2017. Although the Coyotes are finally on the right path with almost grabbing that last Western conference playoff stat this past season, it would be hard to credit their success with Chayka’s approach.

In a 2017 interview with NHL.com, Chayka explained he was all in on the process and how the coaching staff used analytics to define and refine Christian Dvorak’s game.

“We’re not good enough right now. They’re good players, good young players, but they’ve got to get better. How do we bridge that gap? Any time we see a player trending the wrong way or conversely if we see a player trending the right way, doing good things, that’s when the coaching staff puts into action the [statistical] information. I think part of their job is to sell players on what they need to do to have success. This helps them clarify that and quantify it.”That’s what happened with Dvorak. He was showing signs of becoming an impactful player because his rates of puck touches per shift, per minute and per 60 minutes were rising. Puck touches is the key stat Chayka uses for Dvorak because the center is expected to be involved in the play all over the ice.

Chayka also used the analytics argument to defend the demotion to the AHL for then Coyotes player Anthony DuClair.

“He was extremely reliant on other players to generate, which has its benefits when you’re playing with elite players, especially elite centermen, but when you’re not what are you bringing to the table on a consistent basis?” Chayka said. “If he’s not acquiring pucks and hanging out around the slot looking for passes, that’s not ideal.”

After an improved showing on Arizona’s AHL team, judged by the same metrics that sent him down, Chayka once again gave Duclair a place on the roster.

The main reason that the saber analytics model for professional baseball is not a one size fits all model for all professional sports is the little problem of scoring in one game is different from scoring in another.

In the movie Moneyball, general manager Billy Beane shuts down all his scouts’ objections to his player picks by asking “but can they get on base?” Getting on base in an integral part of scoring in baseball, but there’s no hockey equivalent of “getting on base”. There’s only scoring a goal or stopping it.

Perhaps it’s better to stay away from that. The over-reliance of using the cheapest players has led to talks of labor problems within the MLB, which may or not may not serve as a precursor to the next NHL lockout.

Low salary teams in baseball have begun to have such low talent that teams are having staggering attendance problems. The NHL largely avoids this issue with the implementation of a hard salary floor, mostly to protect against tanking, but also prevents these type of situations.

The truth is there’s too many variables in hockey to rely on team building analytics. Now former head of analytics of the Devils Sunny Mehta said it best, ironically describing why he thought he’d be successful at his position.

“Never underestimate the randomness of hockey,” he said. “It’s not impossible that we exceed expectations.”

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Those who love hockey analytics love to point out numbers in advanced stats such as puck possession, Corsi, and a personal favorite of Chayka in his interview, of puck touches. Every game this season, there have been heat maps of high danger vs low danger shots.

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