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As to how they came to that realization, that’s where Eager comes back in.

Throughout the year, he’s building the model so Morgan, Tilis, and Canales have all the information they need to make quick decisions — and the easy ones if they’re fortunate.

To make a really complicated thing too simple, Eager takes all of the data on every prospect and puts it into the model. Stats, testing times, physical profile, all the stuff scouts are collecting throughout the year. He’s then pairing it with their evaluations and external factors as deemed appropriate. Stuff like certain media mock drafts, prospect rankings, and such all go in the pot.

“It’s all of the above,” Eager said. “Everything goes into the stew, and I think a lot of the secret sauce is how much they get weighted. But, basically anything you can think of is going in there because When it’s all said and done in NC, it’s more information, as long as it’s from independent sources, that’s valuable, right?”

Eager pointed to an example from a decade ago that illustrated some of the fine points the mathematical models picked up. When Kansas State wide receiver Tyler Lockett was coming out in the 2015 draft, he was an undersized returner who had two seasons of big production in the Big 12, going for 1,262 receiving yards as a junior and 1,515 as a senior.

Eager recalled noticing him as a potential player that could create value relative to his draft status (third round, 69th overall, by the Seahawks, where he worked with Dave Canales). Lockett began his NFL career as an All-Pro returner in 2015 and became a big-time target, with four straight 1,000-yard seasons.

“That was the first year that we had some of these advanced statistics for a player,” Eager noted. “And Lockett obviously had the speed that you need to stretch a defense, and he was efficient every time he had the ball. He also had the yards after the catch ability that you inferred from his returns at Kansas State, and he was an All-Pro returner as a rookie, and then became a really good receiver with Dave in Seattle.

“That was an example of a guy who ended up being better than where he was selected. And I think that the statistics at the time, which were relatively new, showed that and good on Seattle for taking him.”