2024 NFL Draft Prospects | Draft Sharks Rookie Model Reveals Top Targets

Find the Next Stars Before They Break Out

Rookies matter for every fantasy format. If you didn’t have Puka Nacua, Sam LaPorta, or C.J. Stroud this season … you probably envied the teams that did.

Bet you’d like to turn that around and become the envy of your league mates in 2024, right?

That’s exactly why we’ve developed the Draft Sharks Rookie Model.

How We Measure the NFL Draft Prospects

Are you a film person or an analytics supporter?

Your answer should be “both.” The more data you can collect, the more likely you are to make an informed decision.

The Draft Sharks Rookie Model accounts for analytics, film, and all-important draft capital to grade prospects – highlighting upside … as well as red flags.

Here’s how we break it down …

Analytics Combine Production and Athleticism

Your middle-aged uncle might hate “analytics,” but they’re not evil. And that word doesn’t always mean the same thing.

In this case, “analytics” just refers to our particular combo of production and athleticism metrics.

We could just roll with more familiar stat categories, but that would be lazy. And those numbers often lack important context.

Production Score

Our production score looks at a player’s total college output. That, of course, includes his yardage, TDs, and other traditional numbers.

But this score also customizes those measures to each position. Because the most important stats for assessing WRs don’t necessarily matter the same for projecting TEs. (And each differs even more vs. RBs and QBs, of course.)

We also account for each player’s competition level, as well as his situation. It’s important to note, for example, that Michigan’s low-volume pass offense limited stat upside for QB J.J. McCarthy and WR Roman Wilson.

Athleticism Score

You don’t necessarily need top-level college production or elite athleticism (vs. already elite athletic competition). But the more marks in your favor, the better you look as a prospect.

This score compares a player’s Combine measurements vs. historical numbers for his position.

If he doesn’t go to the Combine, we’ll instead apply the estimates from his pro day (or other event) – with the appropriate correction to account for traditionally more favorable testing in such settings.

Film Grades Highlight How a Player Wins (or Doesn’t)

We started with analytics here, but that doesn’t mean we’re against film. You’ll find samples of our own film review in every NFL Draft prospect we break down on DraftSharks.com.

The film score in our Rookie Model, however, combines some outside sources.

Whose Film Grades Count?

We aggregate grades from Pro Football Focus, NFL.com’s Lance Zierlein, and a few other sources …

The result: Basically, an average of the film pros’ read on those NFL Draft prospects.

Why not just pick one source? Because even the best film grinder is bound to have some biases and positions they don’t judge as well as others.

NFL Draft Capital

Our initial model scores come out well before the draft.  So, we use aggregated mock draft data to approximate draft capital.

As soon as a player gets drafted, his model score changes.

Draft capital – where a player gets selected – tells us at least how his NFL team feels about him. And it likely signals his general league valuation as well.

The extra layer for fantasy purposes: Draft capital is a great signal for NFL opportunity.

Sure, you can point to plenty of exceptions (Hi, Puka). But in general, the earlier a player gets drafted, the more likely he is to get a chance to deliver.

How to Read the Results

The inputs above combine to produce a single model score for each player. You can find that in our prospect profile for the player. And it’s a good way to compare NFL Draft prospects, especially players at the same position.

But we also continue to keep in mind the player’s scores in the separate categories – and how well those categories agree with each other.

Agreement Factor Can Raise Red Flags

The last aspect to discuss here is the agreement factor, which is basically how much agreement there is (or isn’t) across the categories that make up a player’s rookie model score.

The higher the number, the more the categories agree on the prospect. A low Agreement Factor, however, means at least one aspect of the player’s profile is out of whack.

That doesn’t necessarily mean we should discount his Model Score. But it does mean we should check the disagreeing variable and decide whether it’s reason to be more wary of the prospect.

Data is awesome. Here is how we apply it: