There's a lot of really strong dynasty analysis out there, especially when compared to five or ten years ago. But most of it is so dang practical-- Player X is undervalued, Player Y's workload is troubling, the market at this position is irrational, take this specific action to win your league. Dynasty, in Theory is meant as a corrective, offering insights and takeaways into the strategic and structural nature of the game that might not lead to an immediate benefit but which should help us become better players over time. (Additionally, it serves as a vehicle for me to make jokes like "theoretically, this column will help you out".)
Breaking the Code
Here at Dynasty, in Theory, I have a code: nothing practical, nothing actionable. We have a lot of really strong dynasty articles on Footballguys dedicated to giving practical advice for managing your teams. My focus is more on the weird, interesting, or conceptual. Some might accuse me of naval-gazing. (I certainly wouldn't argue the point.)
But to quote a fictional pirate, the code is more what you'd call guidelines than actual rules. So let's break it with some really practical, useful information!
I've been working for a while on a model to evaluate rookie receivers. In part, this is motivated by a lack of competition; there is a ton of effort devoted to valuing prospects before they reach the NFL but much less effort devoted to revising those evaluations once they're here, so it takes a lot less effort to find something of value and stand out from the crowd. Which is good, because I'm incredibly lazy and want to get the maximum return on the very least amount of effort possible.
But it turns out (for wide receivers, at least) that, despite conventional wisdom that players need a couple of years before we can really be sure of who they are, rookie seasons are actually shockingly predictive of the overall course of their careers. And best of all, they're predictive regardless of draft position. A model that tells you that a Top 10 draft pick with a monster rookie year like JaMarr Chase might actually go on to have a pretty good career isn't especially helpful. Not because it's wrong, but because you didn't really need a fancy model to tell you that.
But my model has a great track record at identifying completely off-the-radar players before they really break out. From 2006 to 2021, the Top 10 scores for players who were drafted outside the first two rounds belonged to Marques Colston, Terry McLaurin, Keenan Allen, Mike Williams (Tampa Bay version), Stefon Diggs, Cooper Kupp, Doug Baldwin, Tyreek Hill, Hunter Renfrow, Denarius Moore, T.Y. Hilton, and Amon-Ra St. Brown.
That's not a 100% hit rate (though it'd be foolish to ever expect perfection, and if a model ever does give a 100% hit rate, you can be confident that it's probably overfit). But I have startup dynasty ADP since 2014, and almost all of those guys were extraordinarily cheap to acquire after their rookie seasons. In that span, McLaurin was the 27th WR off the board, Allen was 9th, Diggs was 34th, Kupp was 30th, Hill was 34th, Renfrow was 65th, and St. Brown was 22nd. If you acquired all of those players at prevailing market rates, you probably built yourself a dynasty.
Crucially, once you have a player's score, knowing their draft position adds very little predictive power, meaning rookie performance is almost entirely new information that's not already accounted for in draft capital.
I've run my model early on this year's rookie class. With two weeks left, these scores are not yet final and will change a bit, but they're likely fairly close to where they're going to finish. I want to show the scores, but I also want these scores to serve as a jumping-off point for a larger discussion on the use and abuse of models when it comes to making decisions.
And since this column cares so little about the practically useful stuff, I'll even share the full scores for this year's class and every qualifying rookie from 2006-2021 before the paywall. If you're not a Footballguys subscriber and you've come this far, I appreciate the click and want to make your visit worth your time still. All the good analysis is going to be behind the jump, though.
I'll provide the full results below, but in the meantime, this thread shows how the 2021 rookie class scored and then provides screenshots of all 163 qualifying rookies from 2006 to 2021. The model is tuned so that scores of 100 are roughly average for the sample, with higher scores being better and lower scores worse:
Hey all, just wrapped up all 2021 data in my rookie model. As a reminder, this strictly looks at performance in a player's rookie year (both usage rate and production per route). But it winds up being super-predictive, especially good at identifying off-the-radar outliers. pic.twitter.com/Z9Pfp3zwSI
— Adam Harstad (@AdamHarstad) January 10, 2022
And this thread shows the early look at the 2022 class along with the four closest comparable players from the past, so you can see where this year's rookies rank in contrast:
Couldn’t resist a sneak peak at how the rookie WRs are faring in my production model. Remember, this score is strongly predictive of career outcomes.
— Adam Harstad (@AdamHarstad) December 27, 2022
Robinson/Burks aren’t anywhere near the minimum qualifying threshold. Watson is a hair short but should get there this week. pic.twitter.com/oKjpYzQEpy
I'll probably write much more about the model over the offseason, but for now, let's talk a bit about models in general and this model in particular.
Everything You Didn't Know You Ever Wanted to Know About Models
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