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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, and 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.
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. As I mentioned last week, the most elegant theory in the world is useless if it doesn't match reality. To that end, there is one thing I do every year that is-- and it pains me greatly to admit this-- actually incredibly practical.
I've built a model to evaluate rookie receivers. In part, this was 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.
Mostly, it was built on dumb luck. A decade ago, I spotted what I thought was a fairly glaring market inefficiency. I watched it for years, and it persisted. Eventually, I realized that there was an extraordinary edge to be had with very little effort, and suddenly, building the model was the path of least resistance compared to muddling along without it.
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 2022, 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, I have found that 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 built my model and first deployed it in the wild in 2021. This is a crucial test; anything that explains the past but does not predict the future isn't doing anything for us. But even over the last two years, it has proven consistently useful both in terms of which receivers it was higher than consensus on and which it was lower on.
With 2023 now in the books, I've added this year's class and want to share the results. But I also want to use those results as a jumping-off point for the use and abuse of models to aid in decisions.
Everything You Didn't Know You Ever Wanted to Know About Models
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