You may remember last week, right before the draft, I laid out a more holistic vision for what I wanted to see from the Jets. Fast forward a week, and the draft has come and gone.

Bits and pieces have come out about how the Jets prepared and approached the draft, and a lot of it points in a direction I like. That said, it wasn’t perfect. There were decisions I agreed with, and others I probably wouldn’t have made myself.

So here, I want to break it all down: the pros, the cons, and what stood out most. What gave me confidence, and what gave me pause.

Before diving in, it’s worth saying this upfront: we won’t truly know how this class turns out for a few years.

Like everyone else, I’ve been wrong on prospects before….a lot. So at the end of the day, this is just my read on it right now.

Now, it’s hard to know exactly how heavily the Jets leaned into analytics, but from the outside looking in, a lot of their decisions feel rooted in data.

Take the decision to draft David Bailey over Arvell Reese. That’s one most data-driven analysts would back. Albert Breer even mentioned in his mailbag that “advanced analytics” factored into the choice. Rich Cimini had also reported a couple of weeks prior that Bailey was a strong possibility largely because the Jets lean on analytics in their evaluation process.

Honestly, I’m not sure Bailey is the pick if this were a purely traditional scouting approach.

It was the same thing with D’Angelo Ponds. From a conventional standpoint, he doesn’t check the typical boxes. He’s undersized, listed at just 5’9”. But when you look at the numbers, it’s a completely different story. Over the past 2–3 seasons, Ponds has been one of the most productive and consistent corners in college football.

He’s another player who ranked significantly higher on analytically driven boards compared to traditional ones.

Zooming out a bit, it’s clear the Jets made a conscious effort to target players who were analytically productive. Guys with strong production profiles paired with measurable athletic traits.

That doesn’t guarantee anything, of course. But it does suggest a process that, at least in theory, gives you a better shot at finding value.

The Jets targeted high-production college prospects early in this year’s draft, selecting a league-high four players with NGS production scores of 80+ with each of their first four picks.Analyze every team's draft class with https://t.co/KG7TomIZYW powered by @awscloud pic.twitter.com/XU22xMssrr

Add in that their analytic department was visually featured in the draft war room, it seems like the Jets are leaning more into the data than ever before from a decision stand-point. To me, that’s a positive.

This was something I specifically called out last week as an area I wanted the Jets to avoid messing with.

Now, I’m not saying blindly following consensus boards is some magic formula for success. It isn’t. But one of my concerns with the Jets over the years has been this underlying tendency to operate like they’re consistently outsmarting the rest of the league.

For the most part, the Jets didn’t reach on players. The only selection that went meaningfully earlier than expected was Cade Klubnik. And even that one is a bit more defensible given the positional value of quarterback.

Outside of that, guys like Sadiq, Anez Cooper, and Jackson more or less came off the board around their expected range, or even slightly later than where they were projected. Payne, Omar Cooper, and Ponds in particular went later than anticipated.

To put some data behind this, here’s a look at where the Jets ranked in Draft Capital Over Expected, based on work from Benjamin Robinson:

Again, this doesn’t guarantee anything. Drafting in line with consensus doesn’t automatically translate to a good class. And this year was also a bit unusual in general as there wasn’t a strong unified consensus, especially once you got into the later rounds.