There's a lot of 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 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. Thinking is for doing, as the social psychologists say. 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—incredibly practical.

I have a model for evaluating rookie receivers, and I publish the results every offseason. No, not this year's rookies—there will be gallons of ink devoted to that cause already, there's no way I can add any value on top. My model evaluates last year's rookies.

While there is a ton of effort devoted to valuing prospects before they reach the NFL, there is much less dedicated to revising those evaluations once they're here, so it's much easier to find a comparative advantage. Which is good because I'm incredibly lazy and want to get the maximum return on the very least amount of effort possible.

This model was mostly 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 there was an extraordinary edge to be had, and 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 be sure of who they are, rookie seasons are shockingly predictive of the overall course of their careers. Most importantly, they're predictive regardless of draft position. A model that tells you that a Top 10 draft pick with a monster rookie year like Ja'Marr Chase might go on to have a pretty good career isn't especially helpful. Not because it's wrong, but because you didn't need a fancy model to tell you that.

But my model has a great track record at identifying off-the-radar players before their value reaches its peak. From 2006 to 2023, the Top 12 scores for players who were drafted outside the first two rounds belonged to Puka Nacua, Tank Dell, 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. It would be foolish to expect perfection; if a model does give a 100% hit rate, you can be confident that it's 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, Nacua was the 7th WR off the board, Dell was 24th, McLaurin was 27th, 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.

The Basics of the Model

The core of the model is yards per route run (or YPRR), which I've studied for years and have found to be very predictive of career outcomes. Yards per route run is exactly what it sounds like-- the number of yards a receiver gained divided by the number of routes he ran. In my opinion, this is the only true "efficiency" stat for receivers. (Many people like to use yards per target—or YPT—but YPT is a bad statistic for reasons both conceptual and practical that I'll detail in a bit.)

I'm further adjusting YPRR by adding a bonus for every touchdown. I've tested the model in the past and found that scoring at a disproportionate rate as a rookie does tend to carry predictive signal for the rest of a player's career.

There are several different ways to calculate "routes run". Some sites only count routes run on plays where a pass is attempted. Other sites count routes on any play where it's clear that the offense's intention at the snap was to pass the ball. (This means it counts routes on sacks and scrambles even though the ball was never thrown, but it doesn't count routes on draw plays or designed quarterback runs.)

There are pros and cons to each approach, but I'm using the latter definition of a "route run". Under this definition, any value of YPRR over 2.0 is extremely good. Different methods produce different baselines; if you only count routes on attempted passes, a YPRR of 2.0 is less impressive.

Of course, if a receiver runs one route all year and catches a 13-yard pass on it, he'll have a YPRR of 13. We need some way to ensure small-sample guys like this don't dominate the model. I have two means of dealing with this.

The first is a qualifying threshold; receivers must run at least 250 routes to qualify for the model. On average, we see around 10 rookies a year reach that total. This year, we saw 12 qualifiers, which is down a bit from the last two years (15 and 18 qualifiers), but still above average. The league does seem to be playing rookies more recently. Is this because they're more pro-ready coming in? We'll get to that in a bit.

The second way I protect against small samples is by including a "usage rate" term. Currently, I'm using (routes per game) per (team pass attempt per game). This means if a receiver averages 30 routes per game and his team throws 40 passes per game, his "usage rate" is 75%. When I've tested, I've found that penalizing players who only play in specific packages improves performance.

I normalize both terms so that the sample average results in a score of 100 and every standard deviation above or below adds or subtracts 15 points, and then average the two scores together, putting twice as much weight on the efficiency term as the usage term. This produces the final score.

(Note that because these values are normalized to the sample average and distribution, scores will change slightly over time as new data is added. Past scores will fall slightly after an exceptionally strong class and rise slightly after an exceptionally poor one. These shifts are always small and rarely change the ordering of players.)

Why Yards Per Route Run?

There are two primary reasons. The first is conceptual: any "efficiency" stat should be "units of production divided by units of opportunity".

Many think that the target is the unit of opportunity for the wide receiver; you can't gain yards if you aren't targeted. But earning targets is a skill; if a bad receiver and a good receiver are both running a route on a play, the quarterback is more likely to throw to the good receiver than the bad receiver. Role players might post huge numbers on a per-target basis, but they're only earning a target when they're comparatively more wide open.

The second and more important reason to use YPRR is simple: because it works. If I rebuilt my model using YPT instead of YPRR (but kept everything else the same), the rookie receivers who would benefit the most are Kenny Stills, Mecole Hardman, J.J. Arcega-Whiteside, Gabe Davis, Tre'Quan Smith, Dante Pettis, Hank Baskett, Jahan Dotson, Henry Ruggs III, Anthony Miller, Jalen Hyatt, DeVante Parker, Terrance Williams, Malcolm Mitchell, Tyler Lockett, Mike Wallace, Chester Rogers, Michael Wilson, Robert Foster, and George Pickens.

Despite my philosophical objections to Yards per Target, I would be glad to use it if it improved results, but Lockett, Wallace, and Pickens notwithstanding, that is not a list of receivers you wish you had been more invested in for dynasty. It largely fits with the conceptual case: they're mostly situational deep threats who posted a high yard per target average because YPT is biased towards deeper passes and because these players saw a disproportionate share of their targets on broken coverages.

On the other end, these are the receivers who would be downgraded the most by a move from YPRR to YPT: Tyreek Hill, Davone Bess, Puka Nacua, Chris Olave, Drake London, Odell Beckham Jr., Rondale Moore, Demario Douglas, Donnie Avery, Kelvin Benjamin, Rashee Rice, Percy Harvin, DeSean Jackson, Garrett Wilson, Doug Baldwin, Jarvis Landry, Jaylen Waddle, Cordarrelle Patterson, Kendall Wright, and Allen Robinson II. Again, it's not a perfect correlation-- I doubt managers would be upset about avoiding Kelvin Benjamin and Cordarrelle Patterson after their rookie years. But taken as a whole, that's definitely not a list of receivers you wanted less exposure to.

Results To Date

When presenting the data, I often divide it into rough categories. This is merely for convenience—scores are continuous, so a higher score is always better than a lower one. Notice that the players at the top of each group tend to have more in common with the players at the bottom of the group above than they do with the players at the bottom of their group.

With that out of the way, here are the previous qualifiers:

Superstars (Scores of 118+)

There's no such thing as a sure thing in football, but this is about as close as a receiver can get. I don't have dynasty valuation data from Colston's prime, but every other receiver on this list peaked as a Top 6 dynasty WR except for Dell (who has dealt with multiple catastrophic injuries), Olave, and McLaurin (who both peaked at 7th but have largely been held back by terrible quarterback play).

Thomas, McConkey, and Nabers arguably didn't live up to these comparisons in their sophomore campaigns, but I shared my thoughts on that last week, so no sense repeating myself.

Strong Starters (Scores Between 108 and 117)

Here we see several misses starting to creep in, but around two thirds of this cohort became strong multi-year starters in fantasy and nearly a third became superstars, cracking the Top 6 dynasty receivers at some point.

Good Bets (103-108)

Players in this range still have elite upside, but the success rate begins to noticeably decline, especially towards the bottom. About half of the players in this group became multi-year fantasy starters.

Average Rookies (97-103)

By this point, there's not much meat left on the bone. Only about 33% of players in this group became multi-year starters, while Hopkins and Smith-Njigba were the only stars to emerge. Michael Wilson went on a rampage to end the season; perhaps he joins them in 2026?

Bad Bets (93-97)

This group produced no stars and few starters. It's typically not worth considering any receiver in this range unless they're available quite cheap—Chris Godwin (WR42), Michael Pittman Jr (WR45), and Tyler Boyd (WR57) all outperformed their ranking after their rookie year, but every receiver in this group who was valued within the Top 40 at their position strongly underperformed expectations.

Terrible Bets (<93)