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 every offseason, I publish the results. 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 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 15 qualifiers, which tied 2014 for the second-most in our sample. (Last year saw an eye-popping 18 qualifiers.)

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. For instance, this year's class was so good that the previous top scores dropped by about 0.4. 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 ended his rookie year with a significant injury and didn't look the same in his follow-up campaign), Olave, and McLaurin, (who both peaked at 7th but have largely been held back by terrible quarterback play. With the best support of his career this year, McLaurin finished as the #7 receiver in fantasy.)

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, and Hopkins was the lone star to emerge. Smith-Njigba looks like he might have the potential to join him.

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)

This isn't quite "abandon all hope, ye who enter here" territory. Two of these receivers-- Davante Adams and Jordy Nelson-- became fantasy stars. Nico Collins has been one of the top producers over the last two years and is prepared to join them (he's currently the 7th-ranked dynasty receiver). Jakobi Meyers has a few years as a WR3 / potential flex. Nelson Agholor had a pair of 8-touchdown campaigns.

That's pretty much all the positive production from this cohort.

How the Class of 2024 Fares

With our emptors properly caveated, it's time to get down to brass tacks. Here's how this year's rookie class stacks up.

2024 places a shocking three players into the "Superstar" tier. McConkey led the pack based solely on yards, but including touchdowns edged Thomas ahead. Nabers might grade as the 3rd-best rookie in the class, but that still places him as the 10th-best rookie since 2006.

Harrison might have underperformed the sky-high expectations he carried coming in, but his usage was phenomenal, his yardage was above average, and his 8 touchdowns tipped him even higher, finishing at the 82nd percentile and landing him solidly in the "strong starter" tier. The receivers immediately above and below him are Dwayne Bowe, Doug Baldwin, Zay Flowers, and Garrett Wilson. If any managers are frustrated and selling low, I'd be happy to buy.

Coleman missed three games and Buffalo ranked 26th in pass attempts, deflating his raw totals, but he did enough per opportunity to land himself in the "Good Bets" range. Coker, McMillan, Odunze, Pearsall, Worthy, and Vele all performed about average for a qualifying rookie-- though the degree of difficulty was likely higher for players like Coker (undrafted), Vele (7th round pick) and Pearsall (shot nine days before the season opener). 

(Pearsall and McMillan especially performed much better over the last month of the season. Do I think that kind of late-season improvement is meaningfully predictive of anything? No. But if you wanted a reason to be excited about them, I won't stop you.)

I was a fan of Xavier Legette coming into the year, but his rookie production cohort greatly tempers my enthusiasm. Franklin and Washington both find themselves in even worse company. Meanwhile, Ja'Lynn Polk barely cleared the qualifying threshold, finishing with 252 routes, but by doing so he has surpassed J.J. Arcega-Whiteside for the worst qualifying rookie season in the model's history. 

How I Use These Results

It's always tempting to seek one-size-fits-all solutions, but unfortunately, the most successful approach is to consider all information available; I tend to use the model not as a rank-ordering of players but as an additional data point. I move my original opinion of players up or down in response to their performance, but I do not ever overwrite that original opinion completely.

When the model ranked Christian Watson above Garrett Wilson in 2022, I wrote that I did not prefer Watson to Wilson as a result. I recognized that many elements of Watson's production were suspect; because of injuries, his route total was among the lowest of any receiver in the sample, and while rookie touchdowns are typically meaningful, Watson's 2.59% touchdown per route run rate was the highest in the sample and was disproportionately impacting his score.

(Beckham, Evans, Hill, and Chase round out the Top 5 in touchdown rate. Again, the ability to score touchdowns as a rookie does carry positive signal. But touchdowns are still stochastic and prone to vary for reasons outside of a receiver's control.)

Meanwhile, Watson was also playing with Aaron Rodgers, who had a history of goosing his receivers' efficiency stats, while Wilson was in a much less functional environment. And Wilson was drafted higher and typically looked more impressive.

(Note that most of these concerns also applied to Tank Dell, who only played 11 games and ranked 6th in touchdown rate, though I was still very high on him after his rookie season and am hopeful he can turn things around in Year 3.)

But Watson's high score did cause me to revise my opinion of his prospects upwards. And while I liked Wilson a lot before the season and his rookie year gave me no cause to downgrade him, I generally preferred London and Olave, who had similar draft capital but both scored higher in the model.

On the other end, while Nico Collins scored in the "terrible bets" range, I held him for years in one of my dynasty leagues simply because he was so cheap (WR74 in ADP after his rookie year). At the end of your roster, all players are terrible bets, but I liked Collins' size and draft capital and was willing to give him a bit of a pass on a very dysfunctional franchise.

In my opinion, the value in the model largely isn't in the highly-regarded players with big scores. You certainly didn't need my model to tell you to buy Ja'Marr Chase, Odell Beckham Jr, or Justin Jefferson after record-breaking rookie years. The true value, in my opinion, is in the players that were drafted later and who the community is still lukewarm on after their rookie campaign: A.J. Brown, Terry McLaurin, Stefon Diggs, Cooper Kupp, Jayden Reed, Doug Baldwin, Tyler Lockett, and the like.

When the model prompts me to buy or sell, I always try to index to prevailing market rates. In hindsight, Amon-Ra St. Brown would have been a bargain even if you paid WR10 prices to acquire him, but by buying closer to his WR22 price tag, you maximize your potential for profit and minimize your downside risk.

If you paid Superstar prices for Tank Dell because he finished in the Superstar tier and he winds up disappointing, you set your team back significantly. If you only paid WR24 prices, you probably didn't hurt yourself very much at all. (Most WRs in the WR24 range wind up busting, anyway.)

I also find that the model is valuable not just in the offseason immediately following, but for years afterward. I found myself more likely to roster Stefon Diggs, Cooper Kupp, and Hunter Renfrow for years because of their strong rookie campaigns; all three eventually rewarded my belief. When Drake London opened his sophomore campaign with a 0-catch game, I became a committed buyer in large part because of his bulletproof rookie score.

I am absolutely begging you not to trade Drake London for Zay Flowers.

Please. https://t.co/Ykh7H6o4qq

— Adam Harstad (@AdamHarstad) September 12, 2023

At the moment, I've called Chris Olave (currently the 28th-ranked WR in trade value per FantasyCalc) the biggest buy-low in dynasty. His quarterback play has left a lot to be desired, but he still has the 9th-best rookie season since 2006, and that still matters. If I wasn't buying Dell when he carried a WR24 price tag, I'm definitely buying today now that his cost has fallen to WR43.

That's how I use the model, but ultimately, how you use it is up to you. If you want to treat it as a straight rank-ordering, I won't stop you; certainly you could do much worse. If you want to ignore whatever results don't suit your priors, that's fine, too (I did as much with Collins and was rewarded for it).

Even when this column produces something practical, it's not especially concerned with what you do with it. I want to provide more data for you to consider, but most importantly, I want you to consider how best to use that data.