Hockey’s analytics movement lags behind other sports, but its early success stories are already well told.

There is the video of Canadiens scouting director Eric Crawford successfully pushing to select Lane Hutson in the second round, telling the room that every contending team has a defenseman like him.

There is then-director of analytics and recently hired general manager of the Devils Sunny Mehta’s draft model that ranked Jesper Bratt third overall in the 2016 draft, leading Mehta to push to select him in the sixth round.

There are figures like Hurricanes GM Eric Tulsky, Avalanche director of analytics Arik Parnass and Carolina assistant GM Tyler Dellow — former bloggers and writers who rose to prominence and helped build contending NHL teams.

There’s still a gap in hockey between teams that embrace data and those that don’t, but the beginnings of the next wave of innovation have already started rippling. This one will be about artificial intelligence, and which teams can integrate it into their operations best.

The challenges exist on two levels. First, in the sport slowest to embrace analytics, how do you sell the leap to AI? Just like analytics, that needs to be answered with results that prove those who don’t adapt will be left behind.

The second is broader.

“That is a question that every company in America is asking themselves, and it’s a daunting one: It’s where do we start?” one Eastern Conference executive told The Post. “Ultimately what it’s gonna be is every operational process will be rethought through the lens of AI.”

---

Analytics is a broad topic that gets pigeonholed into a simple one, but as it relates to any sport, you can boil down its purpose into a single thought: using numbers, trends and data to understand what the eye misses.

AI is not like that. If you had to boil down its potential use cases in hockey into one thought, it would be that it can save time. But that is so broad it’s barely a useful descriptor.

“The examples of how to use AI, I think the simple ones are … it’s useful for cap management and modeling, to analyze other clubs and other GM tendencies,” Steve Werier, a former assistant general manager for the Panthers who’s since worked as an AI lawyer for Amazon, told The Post. “I think it could help you identify signals in other media markets that might be relevant to strategic planning.

“Another example is, if you’re a hockey ops executive and you get 100 agent emails a week about players in various leagues around the world. An AI agent with the right prompts can sort through those and highlight ones that most closely fit the criteria your minor league clubs are looking for and that saves you time. AI can allow analytics teams to speed up coding and up the execution timeline.”

What else? Teams generally employ a handful of player development coaches to meet with prospects in college or juniors around the continent during the season. What if, to supplement the time in between those meetings, you could automate AI to give prospects real-time feedback?

Teams also use companies like SportLogiq, which has access to real-time tracking data, to help build internal models and give insights to staff. But could you use AI to aid a coach midgame by accelerating how quickly data and video influencing matchup decisions reach the bench?

“What if I were to give a coach an iPad that had real-time insights that said, ‘Oh s–t, your third line’s getting caved in?’ You may be seeing it anyway, but real-time adjustments on the fly to help him win games in the moment,” the Eastern Conference executive said. “All of this stuff is possible, and it seems far out, but it’s not in the context of what is possible with the technology we have today.”

Mike Kelly, the director of analytics and insights at SportLogiq, homed in on player acquisition as an area where AI can help.

“You’re rarely if ever saying let’s look at all the unrestricted free agents or even the restricted free agents, grade them out from best to worst and let’s just start plugging away at the best. You’re looking for fits,” Kelly told The Post. “You’re saying that maybe we need a depth centerman who can win faceoffs and kill penalties. That’s what you’re looking for. Or we’re looking for a bottom-pair defenseman who’s physical and kills penalties.

“You can input the data and have your AI tools, having certain contract information, maybe you want a guy with one or two years left tops or an unrestricted free agent at a certain price range, have it populate a list for you. Drill down once you’ve got your list into an even shorter list. That’s kinda the manual work that would have to be done. The AI tools can do that work quicker. So it’s a time-saver.”

---

There’s an obvious counterpoint to all of this that everyone who’s ever casually used an AI assistant can think of: Isn’t its reliability suspect?

The answer to that depends on the context — how good are you at prompting it, how good is the data you’re giving it to work with and are you asking the right questions about what it outputs? What everyone stresses, though, is how fast the technology is improving, and how much time it can already save.

“A year ago, I was hesitant to use it because it may stray you on a path,” a Western Conference executive told The Post. “I was learning my own way of it. And now it’s more commonly used this season for sure. I wouldn’t say it’s used for everyday, all-day kind of thing. It’s more of a support tool to help speed up or work through problems, different things like that.

“It hasn’t taken over and that’s where we’re at that precipice of, where is it gonna go and how are data providers gonna use it and how do we use it?”

Jules Lanari-Collard, a student at Imperial College London, recently presented a model he created to measure how players contribute away from the puck at the HALO conference in Denver, a first-of-its-kind hockey analytics conference hosted by Parnass. He used Google’s Gemini to write about 10 percent of the code for it, which in the world of statistics is somewhat on the conservative side.

“For writing code, for making things more efficient, it can really speed up a lot of things. That’s one aspect,” Lanari-Collard said. “I think fundamentally you still need really good people. Those people may be able to implement AI models to develop metrics and things like that, but you definitely can’t just sort of hand off analytics to an AI and expect to get particularly good results.”

AI will eventually be integrated into every organization, but, rightly used, it won’t replace data analysts, scouts or anyone else. It will supplement their work and save them time.

If there’s one downside, it’s that most of that innovation, at least in hockey, will happen away from the public eye. Unlike, for example, baseball — where granular levels of tracking data are available to anyone who wants to use it via Baseball Savant — hockey’s tracking data is largely inaccessible unless you work for a team, the NHL or another company with access to SportLogiq. To do his project, Lanari-Collard and others who participated in HALO’s Hackathon were given some of that data, but the average fan can’t access it.

“You want to break into baseball, American football, basketball, it’s really, really saturated,” Lanari-Collard said. “Not to say there’s not more that can still be done, but there’s a lot of space for innovation in hockey. We’re limited by the data available. That’s the bottleneck.”

What that may also mean, though, is a more noticeable gap between teams that embrace new ways of thinking and those that keep to tradition.

On the team side, though, that limitation doesn’t exist, at least not anywhere near the same way as it does for the public. It’s just a question of who can innovate fastest.

“I think, in terms of hockey operations, there’s not going to be many places [AI’s] not going to touch,” the Western Conference executive said. “… I can’t think of a department who wouldn’t benefit from the ability of it being used at some point in the next handful of years.”