RB’s dire turn

While I generally touted the bull case for running back going into this draft, it’s hard to argue the event was anything but a miserable stroke of luck for the RB’s. This isn’t to say, of course, that things were worse than my data suggested going into the draft, but at least from a quantitative angle, things didn’t get much better.

Oddly enough, though, I think the outcome of the draft was sneaky good from a qualitative perspective. To be sure, there are echoes of the 2024 RB class—which already looks quite poor—in the sense that most guys you’re drafting are more upside handcuffs than immediate starters. Yet I’d argue that the landing spots are largely good for each back taken this year, from both floor and upside perspectives.

Mid-round winners

Jonah Coleman and Emmett Johnson, for example, both landed in situations where they won’t be asked to be the top dog right away, but they also complement their backfield mates quite well. In Coleman’s case, while I still like RJ Harvey from a PPR standpoint, it seems the Broncos don’t want to use him as a pure bellcow, and I’m sure Sean Payton would relish the chance to grind out games on the ground (if only to take pressure off Bo Nix).

Johnson, meanwhile, might be my model’s most savvy tout in the middle rounds. His great PFF grades and decent receiving upside give him the rare distinction of the being dubbed a high-upside mid-rounder by the model, putting him in the company of some truly excellent receiving backs. And given Kenneth Walker’s injury history, betting on Johnson is hardly wishful thinking.

Another interesting riser (despite the data giving him roughly the same score as before) is new Tennessee back Nicholas Singleton. While I don’t think you should get over your skis with Singleton just yet, Tony Pollard is getting older (and almost a free agent), and Tyjae Spears hasn’t really proven much in his time in the league. Ergo, if you’re buying Tennessee’s offensive improvement—which, given their decent O-line, you probably should—Singleton could be another great opportunity-based value.

Hidden gems

Lastly, we have two late-round players who I pegged going into the draft as potentially worthwhile dart throws, with each being flagged as ‘medium’ upside despite their late-round status. Kentucky’s Seth McGowan was an excellent tester at the combine, and while the Colts backfield is definitely crowded now, Jonathan Taylor is closer to free agency than you might think. There’s a similar upside case for new Vikings back Demond Claiborne, who, while meaningfully undersized, has a case for being a viable Aaron Jones replacement if you squint hard enough, at least from a receiving angle.

Better WR clarity

While the RB class involved a lot of hedging, I’m prepared to speak more forcefully about the best WR’s in this class. By being taken in the top 10, for example, Carnell Tate and Jordyn Tyson were firmly enshrined as top-flight receiver prospects (my model gives them elite three-year upside). Just below those two, KC Concepcion—who our model was bullish on going into the draft—now joins Makai Lemon in the high-upside (albeit not quite elite) tier.

Perhaps more notably, Omar Cooper and Denzel Boston fell down a tier, joining surprise riser De’Zhaun Stribling to round out a cadre of, per our model, medium-upside, medium-floor guys. Keep in mind, of course, that these three players are still meaningfully superior to the medium-upside, high-risk guys below them.

Before we get to that throng of similarly rated players, however, an odd little tier 2.5 of medium-risk (and upside) guys exists. (Note: to see the full list of prospects, be sure to paginate right in the table above.) This tier is comprised of Germie Bernard (Bama) and Antonio Williams (Clemson).

Beyond shooting up the draft board, Bernard was also a big beneficiary of some post-draft model tweaks I performed. Since so many prospects were being harmed by early-career throwaway seasons, I decided to use best-year, per-game (collegiate) PPR scoring. This meant that Bernard, who couldn’t see the field at Michigan State, got a boost for his Alabama playing time, and like many of the other guys in this tier, found an excellent landing spot with Pittsburgh.

Williams, by comparison, was already flagged by my model as a stud, and even post-draft, he continues to outpace other players taken in his range. As I noted pre-draft, he’s a bit of a jack-of-all-trades, master-of-none type, and also found a great landing spot in Washington. Despite the Brandon Aiyuk rumors, it’s hard to think of a better talent and opportunity fit outside the first round.

The rat’s nest

For as clear as the top of this class was, however, the middle tier of this receiving class is an absolute mess. While I think managers far too often fall into the trap of playing the landing-spot game, that’s only folly when you’re drastically ignoring draft capital. That isn’t the case here: this echelon of receivers is so closely bunched together that I actually think some subjective judgment is warranted.

Before doing that, though, we can at least take some layups. Two of these guys—Zavion Thomas (Bears) and Caleb Douglas (Dolphins)—are regular Vincent Adultmen, whose rise up the board far exceeded their actual stature. While my model likes Douglas fine, I’ll point to the excellent work Arif Hasan and others have done on draft-day reaches, which suggests severe deviation from big boards rarely pays off.

Another guy in this tier echoes the iconic BoJack Horseman character in a different way, in that the way he moves at his height is just too good to be true. While writing a prior piece, I started to realize how skinny speedesters are increasingly fooling RAS with their ridiculous height-speed combos. This of course brings us to Tennessee product Chris Brazzell, whose gimmick-offense background already gave us enough grounds for skepticism; that he’s also sub-200 lbs at 6’4” is pretty scary.

To be sure, RAS is still a useful part of my model, and including some measurement of athleticism is likely better than not. But it’s worrisome how speed threats are increasingly able to game the RAS system, while equally athletic big-bodied types—like new Giants WR Malachi Fields—are unduly punished for their slowness.

To this point, I should also touch upon Georgia State’s Ted Hurst and UConn’s Skyler Bell. In my pre-draft writeup, I touted each’s high target share, but forgot that stat has diminishing returns, to the point where super-high volume is actually a negative for our model. It’s a good safeguard my model has against Jalen Royals syndrome, but the fact that these slimmer speedsters don’t see much RAS penalty just shows how flawed a metric it can be at the extremes.

Ravens’ pecking order

Another big faller is Indiana’s Elijah Sarratt, but his case is a bit more muddied. Sarratt and his new teammate Ja’Kobi Lane make for a fascinating juxtaposition, given their reversal of fortune from where they stood pre-draft. At first blush, it looks like Lane has a clear edge up on Sarratt, going about a full round earlier with nominally better athleticism.

Yet this may be yet another case of RAS trickery, since while that metric favors Lane, build might be the biggest factor in Sarratt’s favor. Simply put, Lane is a bit light-in-the-pants, and his addition surely frustrated analysts tired of the Ravens’ penchant for lighter guys with questionable play strength. Sarratt, conversely, likely pleased those same detractors; even if he ends up as a power-slot type, he’s still a bigger body than the team has seen in a long while (the withered husk of Nuk Hopkins excepted).

Sarratt was widely rated as a meaningfully better prospect (by consensus big boards) than Lane, too. He also matched—if not outproduced—Lane on every metric our model uses beside RAS, which almost begs the question of why, exactly, any fantasy manager would pick Lane over Sarratt. The blunt truth is, draft capital often tells you how a team ranks guys relative to each other, but I’m also not one to argue with The Athletic’s Dane Brugler, who ranked Lane (25th) far below Sarratt (12th).

Late-round bonks

I’m sure many of my readers listen to the Ringer Fantasy Football Show. While this reference is certainly dated by now, it’s time to talk about a couple of guys my model would flag as “go home to your wife” traps. The first of these is Bryce Lance, who I had high hopes for going into the process. Ultimately, he’s sunk by his late breakout age; while this is a bit unfair to him (I’ve heard NDSU likes to sit guys), it’s ultimately a good counterbalance for his FCS-prospect status.

The last player I think bears addressing is Louisville product Chris Bell, perhaps the hardest prospect to rank in this class. Wherever he goes in your league, though, I can pretty firmly say I disagree with managers taking him as early as the late first. Given that was his pre-draft range, that means people think his nominally excellent landing spot (Miami) totally offsets his precipitous fall, which I can’t quite get on board with.

To be sure, I don’t doubt Bell’s talent, nor will I pound the table for any other guys in his real-life draft range over him. Yet the math isn’t in his favor: just as Hasan’s analysis earlier found reaches were fool’s gold, nominal “steals” like Bell are only marginally better than at-value picks. The blunt truth of the matter is that NFL teams have extensive medical and character info the public can only dream of, and if Bell’s injury history caused him to fall to the middle of the third round, then there’s real cause for concern here.

And while I won’t cape super hard for, say, Germie Bernard or De’Zhaun Stribling, the math of second-round picks like them and Williams making it is a lot better than Bell’s case. If the data-based case for Bell were a bit stronger, then I’d tolerate the dart-throw, but he’s meaningfully dinged for his later breakout age and combine no-show because, well, he was hurt. Ultimately, injury history is a signal, and it’s one of the many things draft capital (among other metrics) implicitly captures, so ignoring it in favor of other upside indicators is, in my mind, a tad foolish.

Parting thoughts

Ultimately, while the specifics of how the draft fell were a bit different than I expected, a lot of my pre-draft prognostications proved pretty prescient. While I was directionally wrong on how RBs would move on the board post-draft, outside of Mike Washington, I don’t think any of the landing spots were catastrophic. As an RB-needy owner with only a handful of second- and third-rounders to work with in one league, it’s about as good as the board could’ve fallen for me.

And maybe it’s the knock-on effect from the RBs falling down the board, but the way most Dynasty mocks have fallen post-draft, it seems the community agrees on this being a deep receiver class. Just like with the RBs, the guys outside of the first round may not get an immediate crack at heavy volume, but you don’t have to squint very hard to see opportunity in a lot of their cases. And while this might be blind optimism, I don’t think this year will be a repeat of the Kyle Williams or Jalen Royals situations some have feared; even if 2026 was a weaker draft class, many of these WR’s saw a bigger draft capital investment than a fringe fourth rounder like Royals.

But that’s all for today. I’ll be back next week with a write up on this year’s tight ends, a group which is, hopefully, just as deep as the receiving class was. Join me then, and hopefully I’ll be able to get through them more concisely, because as much as I like these receivers, they gave me a lot to unpack.

Predicting running backs is, in many ways, a paradox. The secret sauce is easy to figure out: league success is heavily contingent on a back’s college production, with receiving prowess being a particularly strong signal. Maybe you can add a bit of athletic testing in for good measure, but the overall picture is pretty clear by now.

Yet over the course of building my new model, I learned that predicting running backs is anything but a closed case. It’s easy enough to get a good model, and at least in fantasy terms, beating draft capital isn’t too hard either. But making a truly great model is a different matter entirely, with so much room for marginal improvement it’s easy to fall down the rabbit hole.

Another hurdle is juggling the burden of explainability while chasing better performance. Again, it’s not too hard to achieve good accuracy if you’re willing to throw a bunch of esoteric metrics into a complicated machine learning algorithm. Yet explaining why your model behaves the way it does is another challenge entirely, one arguably more difficult than just boosting metrics.

Designing the model

So how, exactly, did I manage to distill all these complex insights—ones learned from years’ worth of training data—into a relatively simple, five-feature model? Well, I cheated a little, combining players’ overall and route-running grades to get a composite PFF grade. (Strangely enough, simply adding the two together provided the best signal to the model; however inelegant it seems, it’s still effective.)

As you can see in the chart above, our top feature is still draft capital (specifically log-transformed pick number, which outperforms raw pick). Our composite PFF grade is also pretty important, and is the first feature I’ve seen to even come close to draft capital (it even surpassed draft capital in 2021 importance).

Much credit, really, is owed to PFF data for this model. Their elusive rating provides information that doesn’t cleanly emerge from simple rushing rate or volume stats. Ditto for targeted QBR, which finds itself tied with elusive rating in terms of importance.

Lastly, we have height-adjusted speed score (HaSS). It’s lucky that this fell out organically while searching for the best feature set, because I always like to have some athleticism numbers fed into the model. Even if it’s the least important of our main features, it’s proven extremely useful in some of the more difficult classes (e.g., 2021).

Model performance

As was the case for our receiver model, we’re going to be using the Spearman correlation coefficient to evaluate our model’s out-of-sample performance. Essentially, for each holdout year, we first train a model on all the data available to that point. For example, if we’re holding out the 2023 class, we feed it all the three-year outcomes from 2016 through 2022.

While our final model ultimately ends up using all the data available to us—i.e., the 2016 through 2023 draft classes—this gives us a decent idea of how our model will perform on data it hasn’t seen before. If, by using the same model specifications and features our final model does, we can achieve good performance on held-out historical draft classes, there’s a pretty good chance our model will perform well in the future, too.

The chart above demonstrates how our model specifications perform on recent draft classes. Already, the value-add of our setup seems pretty apparent, with our model significantly outperforming a draft-capital-only approach in the 2021 draft.

It’s a great example, frankly, of why I chose Spearman coefficient as an evaluation metric over r². While my RB model also does pretty solid on r² (.57, weighted across all hold-out classes), its performance on rounds two through six speaks volumes. By ignoring rounds one and seven—which are layups to rank—we see how our model does on the more difficult mid-round prospects.

A 2025 refresher

One piece of feedback I received on my receiver model piece was that some people wanted to see how it predicted past classes. The issue with this, of course, is that since my final model was trained on classes through 2023, it already knows how those players did.

What’s the solution, then? Luckily, since I only trained on data through 2023 (my model predicts for a three-year horizon), my model was totally blind to the 2024 and 2025 classes. As a result, they’re about as close to “pure” unseen data as you can hope for.

(A quick note: a player’s “Score” is their percentile rank across all historically predicted players since 2016.)

Overall, the new model isn’t that different from last year’s version when it comes to the 2025 class. The model rightly fêted OSU product Quinshon Judkins, while also correctly identifying how RJ Harvey’s receiving ability would boost his dynasty floor. Notable, too, is its favoring of Bhayshul Tuten over Kaleb Johnson, despite the latter going well before the former in the NFL Draft.

The mid-round guys are more of a mixed bag, with few definitive successes or failures. Woody Marks, for example, was flagged as “low-upside,” but my model also correctly did the same for Cowboys flameout Jaydon Blue. Perhaps the savviest call by my model was correctly recognizing “Bill” Croskey-Merritt as a noteworthy talent. Though his upside was marked as ‘Low’, he only missed the ‘Medium’ upside cutoff by nine points, and his prospect score roughly matches the likes of earlier picks Devin Neal and Ollie Gordon.

This demonstrates what I think the most important aspect of a predictive model is, at least in fantasy football terms. It isn’t just about ranking players correctly, or getting a higher R²; it’s about giving managers a competitive edge of some sort, a way to arbitrage. Being correctly bearish on Kaleb Johnson is great and all, but not everyone has top-10 picks to burn. But everybody could’ve bid on Bill, and that’s the kind of advantage we’re seeking here.

The 2026 Class

Before we look at my model’s actual predictions, it’s worth addressing the bad vibes engulfing this RB class. Much of this negativity likely comes from whiplash due to how good last year’s class was, but it’s important to remember just how exceptional the 2025 class was. Most recent classes resemble this year's crop instead: an elite guy or two, then some decent second-tier dudes who, per my model, score in the 70’s and 80’s.

(One thing to note when looking at these projections: we’re using mock consensus rank (MCR) as a temporary draft-capital stand-in. You can find the consensus big board here, and I’ll have updated predictions after the draft.)

The real difference between this year’s class and prior ones is its atrocious depth. If you tab over on the table above, you’ll see that the next prospect after Emmett Johnson, Nicholas Singleton, scores 30 points below him. This is likely to improve somewhat after the draft (assuming some of these guys go earlier than their consensus board ranks), but it’s still a stark indication of how shallow this RB class is.

Early notables

My model clearly likes Love and Price, but they’re known commodities. The real question is, how do we find enough other quality players to salvage this less-than-stellar RB class? Luckily, my model found three more guys who, while not all necessarily first-round dynasty locks, are at least worthy picks in the early second round.

The best of these three is Washington product Jonah Coleman, whose composite PFF grade and targeted QBR are both top three in this class. The only thing holding him back is his just-OK elusiveness rating, which comes with a bit of an asterisk. Assuming a sufficient sample size, our model uses a player’s best collegiate season (in fantasy PPG terms) to pull targeted QBR and elusiveness from.

By this approach, Coleman’s best college season was 2025, largely on the back of receiving yards and touchdowns. His standout work as a pass-catcher this year obviously boosts his targeted QBR, but his elusiveness rating took a nosedive, clocking in at barely half his previous number. Even with his terrible 2025 elusive rating, though, the model still likes him a lot, and if his previously strong ratings are to be believed, the sky is the limit for Coleman.

Scoring slightly lower than Coleman are Arkansas’s Mike Washington and Nebraska’s Emmett Johnson. Near-polar opposites, Washington is largely carried by his combine testing, while Johnson gets by on his solid PFF grades and receiving ability. It’s worth noting, however, that speed score is my model’s least important feature, meaning Washington’s profile is mostly carried by his earlier expected draft position. Thus, while Washington was the better tester, Johnson is likely the better overall player.

Late standouts

While I do think the time spent on the top guys in this class was worthwhile, there are some late-round guys who still warrant attention. Eagle-eyed readers may note, for example, that Pitt RB Desmond Reid’s score far outpaces his mock-consensus rank, but because this means scouts see him as just a priority UFA, he’s still flagged as a ‘Low’ upside player. Thus, I think it’s better to use this space to address the three late-rounders our model pegs as having ‘Medium’ upside.

The first of these guys is Penn State product Nicholas Singleton, who could go earlier than expected this weekend due to his past pedigree. He’s uncannily similar, frankly, to Coleman, at least when it comes to his excellent PFF grades and targeted QBR, the latter of which almost exactly matches Coleman’s. Only his elusiveness rating is meaningfully worse, but while that is a considerable red flag, the numbers support the notion that Singleton can be a valuable post-hype sleeper.

The other two late-rounders of note—Wake Forest’s Demond Claiborne and Kentucky’s Seth McGowan—couldn’t be more different. Like similarly small speedster Desmond Reid, my previous analysis flagged Claiborne as a no-go, and with his light-in-the-pants profile (he weighed in below 190 at 5’10”) it’s easy to see why. Yet he’s got real receiving bona fides, with a borderline elite targeted QBR, suggesting a creative coach could get something out of him in the league.

McGowan, meanwhile, weighed in over 220 pounds at 6 feet flat, making his 4.40 and elite broad jump numbers staggering. Yet his miserable elusive score hints at some underlying red flags, with his just-OK 10-yard splits suggesting there might be some holes in his profile after all. Still, if I had to pick between the two, I’d definitely lean McGowan, and if he goes early enough (or at least slots into an open enough backfield), managers should definitely take notice.

Summary

Ultimately, while I think this pre-draft projection exercise has been pretty useful in defining the overall shape of this running back class, the specifics are simply going to be murky until we get actual NFL draft data. While using log-transformed draft pick makes the difference between late-round guys a lot smaller, a guy going earlier or later than projected is still going to affect his projection fairly strongly.

Nowhere is this truer than with the mid-round guys. At the end of the day, fantasy football (and real football, really) is about opportunity more than anything, and the teams and the level of draft capital a team puts into these guys can drastically impact their NFL career outlook. While I wouldn’t overreact, where these guys land in the draft is still going to matter, and to pretend otherwise would be misleading my audience.

But I’ll also say that my running back model this year has a lot more going for it than just draft capital, which isn’t something I can say of last year’s. Some of our holdout test years even showed PFF grade surpassing draft capital in predictive power, which, in my opinion, speaks to the importance of player quality. At some point, you have to go beyond mere notions of value and ask yourself one simple question: do I want this guy on my team? And if your answer is yes, you’ve got yourself a player.

A couple of weeks ago, I took my first plunge into the 2026 receiver class. In it, I used a smattering of metrics to try and figure out what kinds of receiver prospects pan out. This led to some pretty cool takeaways, such as the fact that collegiate deep-threats better be tall if they wanna succeed in the NFL.

That last finding is what I want my work to provide: ideas that feel intuitive, that you can explain to a guy on the street. The issue, of course, is that when you get into the most predictive stuff—like machine learning-based models—the path from point A to B gets a lot murkier. Thus, a big goal of mine when developing this year’s receiver model was to make it something a layman can understand: if I tweak this aspect of a receiver’s profile, how will their outlook change?

Framing the Problem

One of the hardest things to simply convey about a predictive model is just how ‘good’ it is. Ultimately, I found an article by FantasyLife’s Dwayne McFarland to be illuminating: if you’re looking for a good measuring stick for your model, what better point of reference than a player’s draft pick?

Most prominent, public-facing receiver models use draft pick as their main input. Ergo, if you want to prove your receiver model is worth its salt, you need to beat a draft-capital-only model pretty handily.

This is, of course, far easier said than done, and this doesn’t mean we’re abandoning draft capital as an input; it’s still a huge part of the process. Rather, this just means that we are no longer doing everything in a vacuum, and have a real way to concretely judge the value add of our product.

The Model

What, then, is the model that will take us to the promised land? Which magical black box achieved all my goals? To answer this question, I dug through multiple data sources, with the bulk of my final file derived from Stathead, PFF, and Pahowdy’s ever-useful spreadsheet.

Above are the average ranks, in terms of importance, for my model’s inputs. They essentially break into three tiers (ergo the coloring). In the first group, you have Draft Pick and Breakout Age (20%), which are irreplaceable core features. (Note that draft pick is log-scaled, which is a rough proxy for the draft-chart approach used in McFarland’s Super Model.)

In the second tier, all on its own, you have collegiate PPR per game, our main stand-in for a player’s college-level production. Finally, we have Relative Athletic Score (RAS), Target Rate (TGT%), and Rushing Attempt percentage (RA%).

For these three metrics, higher is (usually) better. RAS, while imperfect, is the best catchall (publicly available, at least) athleticism metric I’ve found. RA% and TGT% are good measures for a player’s run- and pass-game involvement, respectively.

Model design

This year’s model is a blend of linear regression (specifically Ridge) and a random forest. This means that the relationships between our inputs and outputs are more straightforward than usual, with fewer wacky caveats about how a change in one metric might shift our outlook on a player.

A good example of this is Breakout Age, where we see that the later you break out, the worse your prospects are as an NFL player. Again, this isn’t a perfectly linear relationship—rather, it’s more of a steady decline—but the idea is far more intuitive than what, say, a deep neural network would give you, while still performing quite well.

Model performance

What about our model’s performance, though? Is it an improvement on last year’s version, or am I blowing smoke? Surprisingly, my previous model held up pretty well; with 2025 now in the fold, it did a good job at producing three-year outlooks for the 2023 class. Yet when we introduce the prorated 2024 class—our model uses a three-year horizon, so we have to project out a bit—it falls off, predicting career outcomes worse than draft capital would alone.

One question eagle-eyed readers might have is why I’m using the Spearman coefficient over R-squared. This is mainly because, in my opinion, the most useful objective is to get the right order of these players, partly so we can cleanly compare ourselves against NFL teams. While it’s nice to say a player will produce some amount of PPR to start his career, or that we only get PPR projections wrong by a certain amount, what matters more is that we can correctly rank these players.

In short, our main goal should be twofold: how does this player compare against past prospects, and how does he rank within his own class? By converting a player’s raw PPR projection into a percentile score (0 to 100), we make each of those tasks much easier.

The Results

We’re almost ready for my preliminary predictions for the 2026 wide receiver class. Now, you might notice that I’m using Mock Consensus Rank (MCR) as a draft-pick proxy, since the draft hasn’t happened yet. I went back and forth on this a bit, and ultimately, I decided to publish this before the draft. In my opinion, big board consensus is a good enough proxy for a player’s actual draft pick number (plus, I plan to publish an updated version of this post-draft anyway).

(Note: to see the entire table on mobile, be sure to tap the image below.)

Who, then, is our model diverging from the pack on? Just as I found a while back, there’s real grounds for skepticism regarding Carnell Tate. Simply put, his production at OSU was uninspiring. Alongside his slower 40 time—and slightly less-than-ideal size—there’s real reason for concern here, and he needs to maintain his expected top-10 pick status to be considered a high-upside guy.

As for the crown jewel of this receiver class? That would be ASU’s Jordyn Tyson, and I think many in the industry would agree, with a few major caveats. Quite frankly, Tyson has one of the scarier injury profiles we’ve seen in recent memory.

The “low” risk Tyson is flagged with is mostly a product of his ADP, since guys who go that high usually get enough volume to be useful fantasy contributors. Yet make no mistake: the fail case is still real, and if he’s only seeing the field intermittently, it doesn’t matter if his three-year outcome clears the bar for success.

The debate between him and Tate at the top ultimately boils down to which type of risk is more worrisome for you: that the guy will never become an elite player, or that he can’t stay on the field. For a rebuilding team, or a team desperately in need of stars, I think it’s fine to take a bet on Tyson. But if you’re looking to fill your lineup with an immediate contributor, there might be a case for Tate, too.

A potential star?

What about the rest of the class? Who are our model’s other darlings? A player who immediately pops is Texas A&M speedster KC Concepcion, who’s jumped ahead of Indiana product Omar Cooper Jr.

In fact, if we’re looking at players who our model is the high guy on, Concepcion definitely takes the cake. His recent (albeit slight) rise on the mock consensus board has elevated him to “high-upside” status (i.e., over 500 PPR projected in his first three years). This makes him a potential value pick for dynasty drafters, assuming the market doesn’t correct further in his favor

It’s useful, too, to explain why my model loves Concepcion so much. He might just be the ideal profile for the model: he broke out early, commanded a huge target share, and was a real part of his team’s run game. While none of this means I’d trade the farm for him, it’s enough for managers missing out on this year’s nominal big three to relax a bit, knowing they’re still getting a great player in Concepcion.

The fringe first-rounders

If our model likes Concepcion so much, what does it think about the other late first-round players? It’s a bit cooler on those guys, but each has their own strengths that should make us take notice.

Cooper’s nominal selling point is his athleticism, which may seem surprising for a guy who only ran a 4.47. It’s doubly confusing, too, when you realize he’s barely six feet tall and weighed in below 200 pounds. Yet there’s some burst to him—evidenced by his solid vert and 10-yard splits—even if the high-level numbers aren’t that inspiring.

By comparison, Denzel Boston was a better career producer, and broke out earlier (age 21 versus Cooper’s 22) to boot. While it doesn’t feed into our model, Boston’s big leg up (literally in this case) on Cooper is his stature. Like I noted in my previous piece, Boston clears the 6’3” bar for high-aDOT receivers, proof that he can do more than just one thing well.

Now, these guys still seem to be a half-tier below our best prospects, at least based on my metrics. A lot of their future rides on where they’re taken in the draft, with Boston in particular a decent bet to get meaningfully over-drafted. At the time of writing, they’re only considered medium-upside guys. But don’t be surprised if you check back in a month and one of these guys has shot up the rankings.

The sleeper hits

Our model is a big fan of two likely third-rounders in particular. The first is Clemson product Antonio Williams, a real jack-of-all-trades, master-of-none type as far as our key metrics are concerned. Everything about Williams is good, but not exceptional: he’s better than most players, but never quite reaches the level of our most elite prospects.

Perhaps this is best represented by his 8.8 RAS, which essentially means he’s better than 88% of all historical receiver prospects in terms of athleticism. He can run a little, broke out early, and commanded a respectable amount of his team’s targets in college. In short, he looks like the epitome of a safe bet, and playing at Clemson means these stats likely aren’t fool’s gold.

The other guy my model loves is pretty much the antithesis of Williams, i.e., a small-school baller with some truly elite traits. George State’s Ted Hurst commanded almost 25% of his team’s targets while there, and his 9.9 RAS score is hard to beat. It is fairly strange that the athletic Hurst saw little-to-no involvement in the run game, but then again, the same is true of the exceptionally athletic Bryce Lance.

Yet the one thing Hurst and Lance both have in common is they firmly clear the 6’3” bar. If you’re a traits hawk like me, and already keen on Lance, Hurst should definitely be on your radar, too. He might be a marginally better prospect, even, given his younger breakout age and actually competing at the FBS level.

Jeff Caldwell is another potential sleeper, and he unequivocally clears the 6’3” threshold by two whole inches. Yet Caldwell, like the other tall speedsters in this class, carries red flags of his own. As I covered in my earlier writeup, the commonalities between these guys—relatively low BMI, skipping the bench press—indicate they might be low-physicality fool’s gold.

Still, I reckon it’s fine take a chance swing on a guy like Caldwell near the end of your league’s draft (or even as a UDFA). Guys like Hurst are already great values at their price, and Caldwell is carried by his traits arguably even more than those previous standouts. The simple answer, is you’re not gonna find many guys out there running a 4.3 at 6’5”, let alone in the back end of drafts.

Summary

Overall, I’d say this is a pretty solid receiver class with a surprising amount of depth. For trait hawks like me, there’s a lot to like, even in the back end of the class. Maybe these guys are just track stars in disguise, sure, and maybe I’ve got too much Al Davis in me.

Still, the more time I spent with these players, the more they grew on me. A lot of them feel poised to become real players, rather than just late-round flyers, and the data agrees. In fact, the top 14 prospects all scored an 80 or above in my model (15 if you want to round up Malachi Fields’ 79).

Now, there is a pretty big cliff after those guys, so if you’re looking for fringe dart-throws to pad out your roster, this might not be the class for you. Yet I will push back on the perception that this is a weak dynasty class, even though it is, admittedly, a rough year to need a running back. And while I harp on value plenty on this blog, at the end of the day, the real name of the game is finding talent, and as far as receivers are concerned, this draft’s got loads of it.

Last year, I wrote a piece about how the value of NFL rookies changes year-over-year in dynasty. The key thing about this piece was that it was concerned not with the hit rate of these picks, but rather, whether they would maintain their value going forward. That is to say, if you pick a guy in this year’s draft, what will his KeepTradeCut value be a year from now?

And while this concern is certainly secondary to our main focus—i.e., whether the guy is actually a good producer or not—it’s still illuminating as to the psychology of dynasty players. I’m certain that many of you in dynasty leagues have produced more-than-fair offers for a slumping second-year player, only to get rebuffed by a (perhaps delusional) manager who still believes the kid has a shot.

This might not be great process anyway, given how most players who don’t make it in year one often don’t turn into anything. Yet I think it still offers some value come draft time. If you’re choosing between roughly equal players (which is often the case), it might be useful to know which of these guys is likelier to maintain their value a year out, and which is lighting your pick on fire.

The rookie value arc

Now that we’ve defined our focus as meta-gaming the market, how do we actually gain an advantage over our fellow managers? The obvious angle is breaking out our data by position; if we can get an idea of how specific groups gain or lose value every year, then we have decent tiebreakers when it comes to making picks.

But first, let’s establish the parameters of our experiment. To get a rookie’s initial value, we use his early-June value from his draft year, thus giving it some time to stabilize as more and more people draft. For subsequent years, we use early-April data, since we don’t want the upcoming NFL draft to mess with things too much. As for the format this data is taken from, it’s still all single-QB, no TEP leagues.

Above you can see how this plays out by position. Somewhat surprisingly, the median WR or RB sees basically no change in value from year one to year two in the league, with only a slight decline heading into year three. Tight ends, by comparison, skyrocket after their first year in the league before falling back to earth, while the median QB’s arc is a constant decline.

That was in percentage terms, however; what about in absolute terms? Surprisingly, TE’s still hold up here, perhaps because fringe TE’s have nowhere else to go but up (in terms of value, at least). To be sure, this is pretty noisy data, given some small-sample concerns (not to mention some pretty good TE classes propping up the position). Still, this was also what I found last year, with the ‘25 class only further bolstering their value-positive reputation. Also note that this is all without including TEP leagues, which could boost their surplus value even further.

Factoring in ADP

This is all well and good, but still a bit coarse. We aren’t quite making apples-to-apples comparisons: by using data from single-QB, no-TEP leagues, those positions are obviously going to go later in drafts, and thus have lower starting values. Thus, if we look at how players from each round did, we might have a better reference point.

While we’ll tackle round-based data later, there’s something else I want to examine first. Research has shown that the value drop-off in dynasty drafts occurs in the middle of the second round, meaning late-rounders are, nominally, a crapshoot. This means that if we split prospects into early- and late-ADP groups, we’ll be making cleaner, more apples-to-apples comparisons.

The same small sample size caveat applies again, but the findings here are still pretty stark. Across the board, regardless of position, the ROI is unequivocally worse for picks in the early rounds of the dynasty draft (blue dots). Most early picks, it seems, are losing bets to retain value over a rookie’s first year.

Conversely, players taken at pick 2.07 or later seem to gain value from year one to year two. The takeaway, then, is pretty clear-cut: early-round dynasty picks are high-risk, high-reward assets. You take them because you want elite production. Late-rounders, by comparison, thus seem much safer, and while you aren’t as likely to find studs, they offer a high floor.

Now, I will say that KTC’s numbers are a bit warped. Truly bottomed-out players like Damien Martinez maintain some value, when they should be, for all intents and purposes, at zero. Still, I think the data above makes a pretty strong case for taking swings on the guys in the back half of your rookie draft, if only because there’s a decent chance you can still flip them a year later when things have gone south.

Round data by position

What if we got a bit more granular, and cross-section rookies by position and (dynasty, not real-life) round taken? QB’s have too low a sample for this exercise, so we’ll ignore them. Tight Ends, too, have a low-sample issue, but the overall pattern is still worth noting:

As the rest of our data implied, tight ends are a stable asset, regardless of which round they were taken in. The same caveats still remain, of course: the average TE is taken way later than other positions, which means that it won’t experience the same value shocks early picks do (both in terms of magnitude and percentage). Even still, almost no TE’s lost value during their first year in the league, which is pretty staggering even if you consider the small sample size.

When we next examine running backs, we see further confirmation of early-pick volatility. The logic is again simple: the expectations were already diminished for later picks, so the value of these RB’s is often sustained—often growing, even—over their first year in the league. Conversely, first-rounders have outsize expectations placed upon them, making it hard for them to consistently meet the hype.

Finally, we reach the WR’s. My analysis last year found them to be the hardest nut to crack, with little pointing to why, exactly, receiver value fluctuated the way it did. While this article refutes a lot of points I made in my earlier piece—I now think most prospects are more likely to maintain value, and not lose it entering year two, for example—the chart above shows that WR’s remain as slippery as ever.

My best guess, then, is that there’s a pretty clear mapping between player quality and dynasty rookie ADP. That is to say, the reason the round-two fall-off is so big is because you’re going from receivers who were taken in the first round of the actual NFL draft to second-rounders or worse. The intuitive argument is that real-life first-rounders will see the field come hell or high water, while second-rounders will, almost assuredly, be eased in by their coaches.

Why, then, do these third-round dynasty picks hold value better than players taken earlier? Ultimately, it’s the same issue we just saw with early-pick RB’s, where their playing time or productivity often doesn’t match their draft capital. How many managers were likely expecting immediate contributions from the likes of Jack Bech or Tre Harris, for example, when pretty much the opposite occurred? While I still think opportunity is a huge part of why receivers succeed in fantasy (see: Ayomanor, Elic), it still can’t compare to the weight that a first-round pedigree provides young prospects.

The 2025 Class

Theory is great and all, but practice makes perfect. For historical data, you can look at my previous posts; I’m saving this space in specific to look at 2025 rookies. Note that some guys (Gadsden, Monangai) are missing from the upcoming tables; while I could include them, this analysis is based on guys who, at the time of writing last year, had an ADP of 48 or earlier.

I prefer to stick by the ADP values I used last year, since any 2025 values you can find right now are a bit iffy. Some of them were updated way into the dynasty offseason (with a few even leaking into the season proper), so I’m erring on the side of caution to keep as clean a record as possible

First, the fall-offs:

Many of these are quite obvious, with Kaleb Johnson and Travis Hunter boasting the highest-profile falls from grace. Their value drops are self-explanatory, as is that of Damien Martinez. Martinez in particular is a clear example of the holes in KTC’s system, and why I’ve grown to prefer FantasyCalc over time: that service does a much better job of properly rating guys who are, for all intents and purposes, fantasy non-entities.

Perhaps the most interesting remaining duo is that of Jack Bech and Jalen Royals, two receiver prospects with meaningfully different profiles. When I did my first receiver analysis last year, it looked like Jack Bech was a good bet to bust, given how much of his receiving production in college look like empty calorie work. By the same analysis, Jalen Royals looked to be a great prospect, catching nominally difficult deep targets at a high rate.

Yet when I ran my updated model, the narrative flipped, with Bech looking to be one of the very best prospects in the 25 receiver class. Royals, conversely saw his fortunes flip. My model disliked his profile quite a bit, even before considering how it penalized him for being a fourth-rounder.

I already wrote last week about why gimmicky deep threats like Royals are fool’s gold, so his value drop-off should come as no surprise to my readers. As for Bech, the picture’s a bit sunnier, with likely #1 pick Fernando Mendoza offering some tantalizing upside. Ultimately, I think it’s a fine strategy to buy low on Bech—the draft capital invested in him isn’t nothing—but I wouldn’t expect any miracles, either.

Arrow-up rookies

What, then, of the rookies who gained value? These guys feel very telling, being mostly fringe fourth-rounders who doubled their value. Using percent change obviously favors them in this regard, but even still, they largely outpaced (or at least matched) Emeka Egbuka in value gained, which is no small feat.

One thing to note is that while later-pick QB’s often don’t hit, the ROI can be massive, even in single-QB leagues, as Jaxson Dart demonstrates. The results here also bolster the notion the late-round RB’s and tight ends are great investments if you want to retain value. It’s again worth noting, too, that this analysis was not conducted using TEP league data, so there’s even more reason to invest in tight ends in such leagues.

The receiver picture, meanwhile, is a bit muddier. There’s an argument to be made, frankly, that the ‘25 class is the main source of the idea that third-rounders are a better investment than second-rounders at WR. Yet I think we also have enough data at this point to push back on that notion, nudging us in a different direction.

Ultimately, I think the name of the game for receivers is opportunity, which feels incredibly fickle. A lot of rookie RB’s will see the field in at least a minor capacity, while WR’s get frozen out to unprecedented degrees. To predict why this happens, though, is another question entirely, and is definitely something I want to dig into in the future.

Summary

More than anything else, fantasy football is about horse trading. The best fantasy managers aren’t necessarily the ball knowers, nor are they the analytic geeks with an impeccable model. Rather, the dudes who win championships are the guys who can flip a ham sandwich into a Corvette, always eking out more value than they started with.

Yet even this is an incredibly hard feat to replicate, with even the best managers burned by moves gone awry from time to time. This is why, no matter what kind of manager you are, it’s best to have some sort of insurance, an exit plan to offload your distressed assets when the time comes.

And that, I think, is the value add of this piece. It should serve as an investor’s guide for dynasty rookie drafts. What I’m not saying is to blindly take running backs and tight ends at every turn just because they’ll age better in terms of value. You should, all else equal, be taking the best player available unless you have a pressing need.

Yet whether you’re a rebuilding team or perennial contender, it’s hard to argue against the case made here for picking late round RB’s and tight ends. At the very worst, these are guys you can use as throw-ins for trades, when you need to add a little bit of spice to get a deal over the finish line. And at their best, these guys might be crucial handcuffs, or bye week contributor who helps you on your way to a championship. For late round afterthoughts, I’d say that’s a pretty good return on investment.

Last week, I looked at the 2026 running back class. The class was, in short, a tad homogenous, with only one true standout in Jeremiyah Love. While some value-pick candidates certainly popped, it was more or less a confirmation of how lackluster this year’s crop of RB’s is.

Yet while the RB’s seemed to be both same-y and ‘meh’, the receivers are, quite typically, anything but dull. It’s a real Baskin-Robbins receiver class, with each guy being their own unique flavor of prospect.

Defining ‘hits’ and ‘busts’

To start things off, let’s look back to a piece I wrote last year. In it, I defined the Shakir Line—named after Bills wideout Khalil Shakir—which is a tool for evaluating receiver prospects. To clear it, a guy must put up over 300 total PPR points across his first three years in the league.

While I tried hard to find different alternatives, the Shakir line was still the best metric I found. There’s still a bit of squishiness to it, as 300 PPR isn’t a perfect cutoff, with a lot of guys in that range blending together. Yet I still found it to be a better evaluation tool than anything else I tried, including multiple best-season and per-game-rate statistics.

Like with our RB’s, I’m also going to utilize Next Gen Stats’ NGS Score, a pretty solid metric that’s good for multiple reasons. First, it’s good at filtering dudes out, and is better than any other stat I tried at differentiating between players. Second, it’s publicly available, so you don’t have to pay to see what I’m talking about; you can check it out here.

The rules of the game

Now that the ground rules are set, how do we figure out which receivers are likely to succeed? NGS Score provides us with a massive, immediate win: if your NGS Score is below 77, you’re probably cooked.

While this means we won’t be focusing on some lower-tier prospects in this piece, it’s for good reason, given nearly 80% of players below that threshold bust out. If you still want to get granular with the also-rans, check out my broader analysis of last year’s WR class, as well as the process behind my receiver model.

Clearing the NGS Score bar isn’t an automatic pass, either. As an additional hurdle, you still have to register an aDOT below 11.4, too. This helps weed out the false signals many deep-threat prospects can send us. Failing that, you need to be at least 6’3” tall, which tracks: if you’re gonna be a deep threat, you’d better be tall, and not just some scrawny speed guy.

The rules, made simple

If this still seems a bit too math-y, let’s reframe in even simpler terms. Here are our final heuristics, in plain English, with ‘baseline’ used as shorthand for NGS Score:

• Bad baseline —> lock to bust

• Non-deep threat, good baseline —> lock to hit

• Tall deep threat, good baseline —> likely hit

• Shorter deep threat, good baseline —> likely bust

Again, I think these all align pretty well with conventional wisdom, and specific examples later on will help solidify this notion.

A 2025 Refresher

Before we delve into the ‘26 class, though, how about a little amuse-bouche? Though we’d need three years’ worth of NFL data to fully evaluate players, I think it’s still instructive to see how our rules apply to past years. While a player or two might have slipped through the cracks, these are the most relevant 2025 WR’s, the guys people actually used a pick on in rookie drafts.

The hits

First, the guys flagged as probable hits:

Excepting Noel, I think all of these guys are pretty solid bets for fantasy success, even if their actual, real-world impact on their team might be a bit smaller. For various reasons, they seem like the kind of guys who will get you enough PPR volume to be worth the investment.

Two other interesting cases are Jayden Higgins and Tetairoa McMillan. They demonstrate why height, in specific, is an extremely useful filter when trying to figure out the deep-threat problem. It seems like a tired old adage: if a guy’s gonna be a consistent downfield playmaker, then he’d better be big and tall.

But that’s what the math shows, albeit with some asterisks. Higgins sometimes played below his size in college, which was borne out by his subpar man/press numbers. T-Mac, by comparison, definitely didn’t play small, but his production was troubling (though you can chalk that up to poor QB play).

Yet these dudes just look like NFL receivers. To be sure, height is firmly not a prerequisite in general for success, and there’s plenty of room for guys like Jaylen Noel who are excellent athletes in every regard but size. But if it looks like a duck, and if it quacks like a duck, it might well be a duck, even if there’s still some room left to grow.

The busts

Just as the hits contain some clear-cut, big-bodied X receivers, the busts feature a lot of undersized deep threats. Some of these are guys who often look like the real deal—e.g., they’re still over 6 feet, with real traits—but fall short for numerous reasons. Others, by contrast, are clearly straight-line speed merchants, like Andy Isabella, or Jalen Reagor, who ran a blazing 4.22 at his pro day.

These precedents seem incredibly relevant to the 2025 class. Many of its potential busts already looked shaky based on charting alone, with Tre Harris and Jalen Royals in particular running limited route trees in gimmicky offenses. Ayomanor, meanwhile, had similarly iffy charting data, but without the production to back it up.

Perhaps you can chalk it up to very poor QB play, but it’s still hard to ignore just how much worse Ayomanor’s collegiate stats were compared to his peers. While he ran, incredibly enough, nearly as many routes in 2025 as Tetairoa McMillan, he was nearly 200th in catch rate. Even if you factor in QB, the fact of the matter is‚ Bryce Young and Cam Ward were both pretty bad last year; T-Mac vastly outproducing Ayomanor thus speaks volumes to their talent gap.

Maybe the most surprising potential flop here is Matthew Golden, given my receiver model liked him quite a bit. His charting data was solid—he’s less limited than most deep threats—and his 40 time was out of this world. Yet his speed and youth carried his profile hard, papering over his slender frame and weak college production. Given those two things are very related (his production is low because he was injured), his injury-proneness continuing into his NFL career is an ill omen, to say the least.

The 2026 Class

With all these rules out of the way, we can finally classify the notable 2026 prospects. Again, the guys with an NGS Score of below 77 are being saved for when I release my model, given their high implied bust rate. Thus, if you want in-depth analysis on them, be sure to subscribe (it’s free!) and watch this space.

Above, we see which WR’s with an NGS Score above 77 are in the clear (i.e., their aDOT is below 11.4, or to the left of the red area). Overall, this is a deceptively diverse group: while a lot of these guys are very clearly slot-only players—namely Concepción and Branch—we also have some shorter X’s, as well as some tweeners, like Skyler Bell. And while some of these guys have ADOT values closer to the threshold, the fact that they aren't only deep threats demonstrates their real viability on the outside.

Perhaps the most shocking potential bust is Jordan Tyson. This is despite a very high NGS score of 86, as well as his standing as a likely top-20 pick in this year’s draft. There are eerie parallels here, frankly, to Matthew Golden from last year’s class—a similarly often-injured prospect whose hype was difficult to reconcile with his meaningful flaws.

Now, they are different kinds of prospects, with Golden being more of a gimmicky speedster. My model also really liked Golden last year, and I think we’d be remiss to close the book on him. Yet Tyson’s injuries are even scarier than Golden’s were, and while he’s still a pretty exciting prospect, it’d be scary taking him at his current price.

Size matters

Now, we’ve already seen which short-area threats are primed to pop; what of the deep threats? Of the four nominal hits below, Denzel Boston and Bryce Lance feel the most conventionally obvious, with the latter being a particular traits freak. It’d be fitting, frankly, if NDSU product Lance—who, at an 86 NGS Score, boasts the same grade as Tyson—outpaces Tyson in the same way his fellow Buffalo, Christian Watson, overshadowed Matthew Golden last year

As for the other two hits—Brazzell and Caldwell—I might want to pump the brakes a bit. When talking about receivers specifically, it’s helpful to understand what kinds of profiles exist, and why players who fit them might not succeed. Over the last couple of years, as we’ve seen more and more dazzling combine performances, many of these nominal “traits gods” fail to translate their game to the NFL (or have some initial success, then fall off later).

This is all to say that both these guys look a lot like Brian Thomas Jr., which, while nominally a good thing, comes with major caveats. BTJ looked like he was on his way to becoming the next Calvin Johnson after a stellar rookie season, but he fell off hard in his second year.

Many pundits have attributed BTJ’s fall-off to his unwillingness to take contact across the middle of the field. While I think that, in his case, it may be more psychological than physical, he still boasts roughly a 25th percentile BMI, while Caldwell (10th percentile) and Brazzell (5th percentile) are even slighter. BTJ’s miserable bench press performance also speaks volumes to his play strength issues, so Caldwell and Brazzell shirking that test bodes ill for their prospects in the league.

The Tate dilemma

Let’s now pivot from some dubious ‘hits’ into a questionable ‘bust’. While our metrics are down on him, I’d probably give Carnell Tate some grace, especially given he’s a potential top-10 pick in this year’s NFL draft. Granted, his height’s a tad below where you’d want someone of his aDOT to be, but you could also chalk up his deep-threat role to the overabundance of receiver talent at OSU.

I’m eager to see how my updated model will rate him, because while I definitely think his slightly below-rate height and deep target share should give us pause, I’d be apprehensive about writing off this year’s top receiving prospect. At the same time, however, the skepticism may not be that far-fetched, given he lives right on the 77 NGS score threshold we set earlier.

It’s pretty likely, then, that he’s just as much of a fringe case as our BTJ-flavored guys, requiring you to overlook some pretty big warning signs. As a fantasy prospect, it ultimately might not matter a whole lot; as long as he isn’t putting up, say, Kevin White numbers, a guy who goes top 10 in the NFL Draft is going to get a lot of targets in the league. But as an actual player, there’s certainly grounds for caution, and based on our metrics, he doesn’t quite feel like a guy you should bet the farm on.

Parting thoughts

Overall, I found writing this article to be a productive thought experiment, and hopefully, you found the results useful, too. Maybe the final metrics I presented seem fairly simple to you, even reaching the point of near obviousness. Yet the process to get there was anything but, with a lot of false starts and dead-end alleys I had to explore before I got to a decent result.

To be sure, there are still some major caveats to my findings, and I don’t think these rules are ironclad. If anything, they’re meant to be used in concert with models, tape, and your own judgment; they’re tools meant to guide rather than constrain. The ideas in this article are meant to inspire deep dives, and are not meant to be the final word on a guy.

Yet all in all, I’m still pretty happy with how this piece turned out. While you should never let your biases guide your analysis, it’s pretty exciting when, incidentally, you end up with something that confirms your existing beliefs. Maybe it’s something as simple as speedy deep threats being fool’s gold, or tall dudes being superior downfield options.

But every little bit of knowledge helps in fantasy football, each new tidbit giving you an iota of leverage to wield over your leaguemates. Hopefully, too, that brings you another step closer to the ultimate goal of a championship.

Last year, I wrote about the Mitchell line, a threshold I came up with based on former 49ers RB Elijah Mitchell. Its purpose was to differentiate real-deal, fantasy-startable RB prospects from the rest, using a cutoff of 250 PPR points across the first three seasons of a player’s career.

I don’t want to go into too much more detail; you can look at the piece here, but in short, it divides prospects pretty cleanly. Our namesake back, Elijah Mitchell, resides just over that line; more importantly, though, he’s in his own zip code, with barely any other players living in the fifty-point range below him. Those are guys like Benny Snell, useful players who never became surefire fantasy starters.

And that’s why I think the Mitchell line works so well: if you’re spending a pick on a running back, you want guys who have the capability to be a starter. Even if Mitchell’s career was fraught with injury, I think his level of early-career production is a good benchmark for what we should shoot for when drafting dudes.

The 2025 Class

How, then, can we predict which backs will surpass the Mitchell line, and which will fall below it? I’ve been a fan of Next Gen Stats’ NGS Score in the past, which blends a running back’s on-field performance with his traits to get a holistic view of him as a prospect. It’s no surprise, then, that among the countless metrics I tried, it was best at sifting through RB’s.

Let’s first apply NGS Score, then, to the 2025 class and see how we do. I should note that this is in-sample for our data, meaning that it was part of what we used to determine our cutoffs. Yet it’s still instructive of what makes a great player (or, conversely, something less than that).

In the graph below, you’ll see two areas, one blue, and one orange. (Note: hover over the dots to see more backs). The bottom zone is what I’d call the ‘dead zone’, where backs who received an NGS Score below 67 live. In the past decade, only one out of the 22 backs below that threshold in the past decade, only one of them cleared the Mitchell line. That’s a pretty astounding result, and seems like a death sentence for any back.

The first thing that pops about the 2025 class is the number of clear-cut, high-impact starters in it. These are the guys in the blue zone (above 81 NGS Score); players in this region typically hit. In 2025, most of these guys went early in dynasty drafts, and have already justified their cost.

What’s equally exceptional, however, is how none of the top 20 backs by dynasty ADP fell into the dead zone. The closest is Woody Marks, who, after seeing newfound competition from David Montgomery, feels like the perfect example of a borderline case.

Marks succeeded where others failed for two reasons. First, situation: despite going far later than, say, Kaleb Johnson, Marks had extremely weak (and injured) competition ahead of him, whereas Johnson was forced to sit between two healthy, productive players. Second, draft capital: the fourth round is a relatively high price to pay for an RB, and you’d like that investment to give you some decent snaps. It’s why a lot of nominally superior prospects, like Damien Martinez, en up getting cut just because they were taken later.

The 2026 Class

If the 2025 class was stellar, then what can we make of the 2026 class? In a way, this year’s group is the mirror image of last year’s: similar amount of mid-tier guys, except with way more duds than studs on the periphery. The only sure thing this year is Jeremiyah Love, who has a real chance to go top 10 in the upcoming NFL Draft. He’s so good, in fact, that I had to adjust the chart below around him; if I included him, his 94 NGS Score would’ve greatly distorted the rest of our data.

As for the rest, the name of the game is, as always, value. If we go by the mock draft consensus, guys like Adam Randall and Desmond Reid (more on him later) seem like pretty solid bets relative to dudes like Singleton or Washington, who you’re going to be paying a far higher premium in fantasy for.

Now, there is, of course, a caveat, which is that based on big boards, Randall and Reid are likely to be mere seventh-rounders, if not UFA’s. While NGS Score is nice, the biggest indicator of prospect success is the real-life draft capital spent on them. If they go in the fifth or sixth round, take notice; otherwise, it might be wise to pass on these potential August cuts.

Diving Deeper into the Dead Zone

OK, so it looks pretty obvious that unless we go over this with a fine-tooth comb, there aren’t many stars in this year’s draft class. But if we’re looking at a bunch of JAG’s, then what moves a dude from ‘meh’ to ‘do not draft’? Should we add more criteria for success?

The answer, it seems—after a stupid amount of trial-and-error—is a pretty resounding ‘yes’. But what are these magical features that will lead us to the promised land? My goal was to find indicators that do two things. First, they have to be aspects of running back play that complement each other, i.e., they aren’t redundant. Second, they have to say something that NGS Score hasn’t already said.

This rules out most rote production metrics—rushing share, receiving efficiency, etc.—which are already rolled up more or less in the NGS score itself. Where this ultimately leads us, then, is to weight and PFF’s elusiveness rating. From a mathematical standpoint, it basically doubles our effectiveness in finding busts.

NGS score was truly a killer on its own, to be sure. Of the 22 players with an NGS Score below 67 in the last decade, all but one fell below the Mitchell line. Yet when you add in the cross-section of weight and elusiveness rating, that number grows to 38 (i.e., 56% of total busts)

Even if that number doesn’t sound like much, the important thing is, we have an incredibly low false positive rate. Some of the guys the system flags as busts might do OK in the NFL, but very few of them will become outright fantasy starters. Of the 42 potential busts we flagged using our method, only four of them turned out to be real-deal, productive backs, and some of those, like Isiah Pacheco, were massive outliers.

But more importantly, I chose these metrics because they were both clean and intuitive. It’s easy to understand how, if a back is both elusive and big, they’ve got a good shot at success. Again, few players in this class fit that bill, and are probably more one than the other (i.e., we have a lot of bruisers and slippery little guys). Only Jeremiyah Love clears both bars resoundingly, and we all know how good he’s gonna be.

2026 Class

With all that out of the way, what does this practically mean? Well, the bad news is, we don’t pick up much new information from this. It mostly confirms what we were seeing with the NGS score, and if I had to use one word to describe the ‘26 running back class, it would be borderline.

Just as we saw all of our guys jumping out of the NGS dead zone, most of our guys are also just elusive and big enough to meet reasonable NFL thresholds. Everyone else—the dudes who are too scrawny and slow—we already flagged as potential busts via NGS Score.

The two notable exceptions whom NGS Score failed to flag share similar names. Demond Claiborne and Desmond Reid both sit well under where you’d like your backs to be in terms of size, with Reid specifically standing at a very tiny 5-foot-6. Despite not measuring at the combine, Reid is quite possibly a sub-4.4 back, yet his low elusiveness rating suggests his speed didn’t translate to the field.

Claiborne instills more optimism: despite being similarly undersized, he actually ran at the combine, giving us concrete proof of his elite speed. It’s telling, too, that Claiborne is projected to go about 75 picks above Reid—around pick 150—where Reid is almost in priority free agent range.

But I do think these two players are instructive of where we’re at with this class. Even including the more traits-y guys, it’s still a class full of box-checkers, and not outright studs. Perhaps an even better word to describe it would be flat, though some of these guys are definitely better than others. Yet there’s still not a whole lot separating them, and I wouldn’t be surprised if teams leaned more on preference than pedigree come draft time.

Summary

Overall, I think we’ve done a good job at confirming the consensus about the 2026 RB class. There’s still plenty of time between today and the draft, of course, and what looks like an extremely strong correlation between NGS Score and success rate is bound to get noisier once we know where these prospects are headed. (To my understanding, NGS Score incorporates scout grades, which means it should be as in-line with big-board rank as it currently is).

Yet I don’t think the overall picture will change too much from now ‘til then. Maybe we’ll have a better idea of which fringe guys got a boost in the draft, and who had an unfortunate draft-day fall. When that happens, I’ll be back with an improved RB model of my own. Until then, we can still make mental notes of early red flags we saw, warning signs to heed lest we fall prey to some runaway hype train.

I wrote a lot of fantasy football content last year, though somewhat ironically, I took most of the season proper off. Granted, there’s not a lot of dynasty content (my main focus) to produce in-season, but I still did some redraft analysis before the season started. Thus, there’s still ample room to break down whether these takes of mine held water.

The goal of all this is, hopefully, to provide fantasy managers with useful heuristics heading into the 2026 season. I do, of course, caution readers to take all this with a massive grain of salt; the ground beneath fantasy analysis is ever-shifting, and what’s true today may look incredibly wrong tomorrow. Still, some of my preseason deductions—which were based on years’ worth of data—look incredibly sharp, and are worth drilling into.

Getting on base

If you’re reading this, you’re probably aware of the 2011 film Moneyball, or at least know a couple memes from it. You’re likely familiar, too, with the scene where Brad Pitt and Jonah Hill override the scouts’ picks in favor of guys who “get on base.” In short, they’re trying to find high-floor guys who can contribute in some fashion to their team.

This doesn’t map perfectly to fantasy football, of course. You obviously still want star players, guys who can nuke your opponents in the playoffs. Yet every pick who ends up contributing something to your team—whether it be a late-season spot starter or trade chip—should be considered a win.

If you want to “get on base”, then, you should probably follow conventional wisdom. The graph above shows pretty clearly that in recent history, if you want a rookie to provide meaningful value—defined here as providing at least 100 total PPR points over the final 10 or so games of the season—the best place to look is at either the front or back end of drafts.

For early-round guys, the story is simple: teams spent a good deal of draft capital, and want them to see the field. There’s a meaningful distinction, in fact, between rookies taken in the first two rounds of the NFL Draft and mid-round picks. It’s pretty telling for example, that second-rounders RJ Harvey and TreVeyon Henderson came on strong late in the season, while Kaleb Johnson barely got a shot after making an early rookie mistake.

(Note: click on the ‘expand’ button on mobile to fully see the chart above’s legend)

When it comes to the later rounds, however, it’s more about preferences than anything. Say you need a spot starter to fill in until your rookies and IR stashes can come on. If so, the left side of the graph shows that vets are meaningfully better at filling this role. If we define a useful early-season starter to be a guy who clears 50 PPR points in the first five weeks of the season, then vets win easily.

In the stretch run, however, rookies take the lead. If we look at end-of-draft picks who came on late—i.e., had 100 or more stretch-run points—rookies outpace vets. The issue, of course, is whether you have the stomach to hold onto them for that long. I’m sure some fantasy managers who drafted Woody Marks and Kyle Monangai were happy eventually, but those late-season points matter a lot less if you’re in an early-season hole.

Scouring for stars

Some of you may quibble with my methodology so far, and I wouldn’t blame you. After all, the point of the draft is to find stars, not just depth pieces. In fact, I’d reckon that the very best fantasy managers find players of that tier—i.e., guys who are useful, but not exceptional—in FA and on the waiver wire, and not in the draft.

Yet it’s these same locked-in managers who often fall prey to some of fantasy football’s biggest fallacies. Many experts will tout breakout rookies’ league-winning potential, and many fantasy players buy into these hype bubbles. But the truth of the matter is, rookies often have shorter leashes—and longer ramp-up times—than entrenched vets. Even if they can turn into solid contributors, very few of them outside the first six rounds or so will win you games come playoff time.

Now, while the chart above paints a pretty grim picture for rookie RB’s, they’re still relatively close to vets in the top half and back end of draft. (Note that a “big game” is considered 18 or more PPR points). If you have a strong hunch about a particular player or backfield situation, then calling your shot early or taking a late flyer on a guy is perfectly fine.

In the middle rounds, however, the chart above suggests that vets were being wildly undervalued, and this year’s results strongly confirmed that notion. I’d imagine anyone who picked up Travis Etienne or Javonte Williams was a happy camper, for example, with both posting close to top-10 seasons. Even Rhamondre Stevenson and Zach Charbonnet make me look smart: while their seasons overall were bumpy, they came through late in the fantasy playoffs, and I’d wager few owners regret picking them.

Now, this is still an imperfect science, since there are obvious exceptions to this rule. This was a particularly noisy year for mid-round RBs, too, with wild movement up and down the board just before drafts fired in late August. Players like Joe Mixon and Quinshon Judkins were in free fall, while upstarts like Bill Croskey-Merritt skyrocketed up draft boards.

Still, the two biggest success stories, Javonte Williams and Travis Etienne, feel more instructive than usual. So much of their success ties back to factors outside the players themselves, like having the inside track to the starting job, which is worth its weight in gold.

The Cowboys, too, have consistently invested in their offensive line, and their new OC had prior experience as a run-game coordinator. New Jags HC Liam Coen was also known for his clever running back designs in Tampa Bay. It’s no surprise, then, that Williams and Etienne broke out. The lesson: given the chance to draft a talented back in a great ecosystem, take it.

The WR edge is shrinking

Now, I’ve spent a lot of time on RB’s, but what of receivers, the second-most important position in fantasy? I’d argue this was one of the most difficult seasons for evaluating WR’s in recent memory, and a real rollercoaster for managers. With the exception of guys like Michael Wilson—who was, in the words of Yahoo analyst Matt Harmon, the product of a “fantasy scam” offense—the usual edge players got from finding useful receivers off the wire felt much slimmer this year.

This is slightly ironic, of course, because going into the season I thought I’d found an excellent heuristic for drafting receivers. Judging by the data above, it seems that rookies performed much better relative to their ADP (thus the higher Scoring over Expected). You can read more about my methodology for Scoring over Expected here.

It’d stand to reason that receivers taken in the first round of the NFL draft would go fairly early in redraft. Yet going into the 2025 season, these players were criminally under-drafted: you could get a top-10 real-life selection like Garrett Wilson as late as the thirteenth round.

Now, past-year ADP data can get pretty fuzzy, especially when you factor in how players on auto-pick can screw up the data. Still, I don’t think it’s a stretch to say that Brian Thomas Jr., Justin Jefferson, and Ja’marr Chase would’ve been values at any pick during their rookie years, with the latter two going outside the top 100.

Yet this year, it seemed that the crowd finally caught up, and while I wanted to try this strategy in my leagues, almost every rookie WR went right before I aimed to pick them. Granted, this was likely the product of specific guys getting massive hype, such as Matthew Golden and Emeka Egbuka, the latter of which went in the sixth round of one of my league’s drafts.

Still, the trend seems pretty clear over the last few years, with rookie WR’s continuing to skyrocket up fantasy draft boards. It isn’t even really a product of the two most recent WR classes boasting multiple first-rounders and elite talent, too. For example, multiple players from the excellent 2022 WR class were, based on ADP, nearly going undrafted, or close to it.

However unsure I am about the direction receiver drafting will go in the future, I’m pretty sure we pass the days where multiple first-round talents will fall out of the top 100 in redraft. Us numbers-savvy managers will thus have to work hard at finding new ways to outsmart the herd, because we’re certainly getting priced out of the rookie receiver market.

Summary

I started out writing this article to be a comprehensive piece, or at least part one of an article outlining everything I learned from the 2025 fantasy season. There’s still a lot more to cover, especially from a dynasty and advanced stats standpoint (watch this space for 2026 NFL Draft prospect analysis).

But ultimately, I think what I covered in this piece represents the most essential takeaways from this season. While the number of noteworthy rookie quarterbacks and tight ends entering each draft class is pretty variable (and definitely volatile), it seems that in today’s game, there will never be a shortage of noteworthy rookie running backs and wide receivers ever again.

Thus, I think it’s crucial that we know where exactly to find value when drafting these players. Take it from someone who’s built multiple rookie prediction models: it’s incredibly hard to know how any one prospect is going to perform in the NFL, let alone in their first season.

To be sure, fantasy football is definitely about picking good players, going with hunches, and using the numbers to validate guys on an individual basis. But no matter how smart you are, no matter how good your model is, it’s about opportunity cost first and foremost. And as any fantasy player worth their salt can tell you, it’s about where you draft a player more than the player themself.

A few months back, I unveiled my wide receiver model, in a post cheekily titled “Another Receiver Model.” It had numerous issues, the foremost being its poor performance on the 2023 class. While that’s still a slight issue for my new version—let’s call it ARM 2.0—the updated model still performs meaningfully better on that class.

Key Features

While a lot has changed from my original model, ARM 2.0 still relies heavily on where a player was taken in the NFL draft. The logic, of course, is simple: beyond telling you what the league thinks of a guy, a player who was drafted earlier is simply more likely to see the field.

This isn’t to say, of course, that draft capital totally determines a prospect’s fate; our model finds real value in other metrics, too. Note that the model utilizes other features not pictured above, which are excluded for redundancy reasons.1

Many of these features are self-explanatory, such as rushing attempt percent (RA%)—i.e., how many of a receivers’s touches were runs—and body mass index (BMI), which is essentially a height/weight ratio. The story for BMI is simple, with higher values generally being better. After all, you don’t want a slight breeze to knock your guy over.

RA% is slightly trickier to unpack. The ideal seems to be dabbling very slightly in running: we’re talking an RA% of one or two percent here, with less than a yard per game rushing. Barring that, you either want a guy to be real rushing threat—think Deebo Samuel—or somebody who’s never even run the ball once.

BOA (20%), meanwhile, seems a bit less straightforward, but it’s just the age where a player produced at least 20% of his team’s receiving yards and TD’s. Naturally, the younger a player does this, the better: in most cases, you want your receivers to be wunderkind, and not late bloomers.

“Just right” usage

Below, we see the slightly more complicated case of target share. Though this example is smoothed out slightly, the overall picture is clear. It seems you want a prospect to reside in the “Goldilocks zone” between roughly a 15% and 20% target share.

Like with many of our other features, it’s possible this is picking up on other hidden effects, too. While a super-low target share is obviously bad, it’s not immediately clear why residing in the middle is superior to hogging most of your team’s targets.

Beyond small sample size concerns, my hunch is that if one guy is getting north of 25% of his team’s targets, the talent around him might be lacking. It’s also pretty likely TGT% is indicating whether a guy was a small-school player or not. After all, teams with only one NFL-level receiver tend to pepper him with targets, meaning his stats could be artificially inflated against weaker competition.

Model Performance

Now that we know how the ARM 2.0 works, how does it actually perform? The whole thrust for updating the model was the previous version’s middling performance; is the new one meaningfully better?

The answer, it seems, is a resounding yes. There are myriad reasons for our model’s improvement, the foremost being a refined feature-vetting process and trying out new model architectures.2

What’s equally important, however, is that it’s still explainable. That is to say, while ARM 2.0 is still a bit of a black box, our small handful of input features means it’s relatively easy to demonstrate why the it behaves the way it does. These features are still distinct (and useful) enough, however, that they keep us from relying too much on where a player went in the NFL draft.

ARM’s large training scope—dating back to 2009—also gives me confidence we aren’t just overfitting to recent developments. We’ve also got three out-of-sample classes from 2021 to 2023 to make sure our model performs well on unseen data. Together, these design considerations helps ARM avoid being blindsided by any single draft class.

Finally, the biggest improvement of all is on the 2023 draft class. There’s still room to improve further, of course, which might come from having a full three years’ worth of data (like the rest of the classes do). Still, jumping from a worrisome R² of .3 to a just-fine .47 is a big leap, and enough to give me real confidence in this version of the model.

The Results

Below are my new model’s projections for the 2025 receiver class. For each player, we have their three-year outlooks, as well as their relative risk and upside. (Note: for this table and the next two, click on the right arrow above the table to see more prospects.)

Already we see some big changes from my previous rankings, with Emeka Egbuka not only surpassing Travis Hunter as our top prospect, but practically dunking on the rest of the field. The model, of course, knows nothing about Egbuka’s stellar debut as a pro (I swear I didn’t rig it to like him, either). While I’d still take its bullishness with a grain of salt—some of my previous analysis was a tad cooler on Egbuka—it’s hard not to be excited by his early play.

Egbuka surpassing golden-boy Travis Hunter—the number two overall pick— is doubly impressive, really, given the heavy weight our model puts on draft capital. That Texas product Matthew Golden nearly matches Hunter’s projection—while also leapfrogging earlier pick Tetairoa McMillan—is noteworthy as well.

What’s most surprising, though, is Kyle Williams leapfrogging multiple second-rounders. In fact, he’s one of two players outside the first round who ARM sees as having “high” upside. The other, of course, is Luther Burden, which tracks: he has all the talent in the world, but comes with character concerns as well.

What about player comps? While ARM 2.0 produced some tantalizing upside comps for the 2025 class, some players have scary downside. Even though Egbuka has a high floor, his profile is still similar enough to Laquon Treadwell that he gets comp’d to him.

This does, of course, warrant a massive heap of salt: nobody’s confusing Egbuka for the lumbering Treadwell, after all. The same goes for Tetairoa McMillan and Devonta Smith, about as opposite of physical profiles as you could have.

Still, there are some gems here. The comps for Tre Harris are uncanny: his upside comp is fellow Ole Miss legend A.J. Brown, while his downside comp is straight-line speedster Andy Isabella. It’s a bit uncanny, frankly, given Harris seems destined to either end up as an everyday “X” receiver or a vertical-only threat.

Evening the field

OK, you might be wondering, but how would our model look if we didn’t account for draft capital? It’s likely a pointless hypothetical, given how heavily draft position affects ARM’s predictions. Still, it’s also easy to see draft capital makes ARM, by and large, higher on top-100 picks, and bearish on everyone else. By removing it from the equation, we can theoretically even the playing field

Unfortunately for me, the idiot who drafted Jalen Royals in multiple leagues after I saw his stellar advanced stats, ARM really just hates the guy. Also noteworthy: ARM 2.0 is no longer letting guys coast by on draft slot alone. While Jayden Higgins and Pat Bryant still get big boosts for being top-100 picks, if we remove draft capital from the picture, they’re among our worst prospects.

Again, this is a pretty messy experiment, built on assuming everybody was taken with the 50th pick in the draft. Still, it’s illuminating to see what potential sleepers might be flying under the radar. Tez Johnson, for example, shines here; my previous analysis also pegged him as a statistical superstar, his worryingly small frame notwithstanding.

Parting thoughts

What, then, should be the takeaways from this article? The answer’s a bit tricky, since there are few truisms we can derive here from any single stat.

I’ll still try, though. If you get anything out of this article, it should be these ideas:

• The younger a guy breaks out, the better

• You want a guy to be sturdy (high BMI)

• Too few—or too many—targets is a red flag

Most importantly, though, is that for dynasty evaluation, the conversation starts and ends with draft capital. No matter how good a guy is, no matter how much the stats like him, the later he’s taken, the smaller the margin for error is.

Does this mean that you should’ve drafted, say, Isaac TeSlaa over Jalen Royals based on draft capital alone? Not necessarily; consensus big boards had Royals rated firmly as a top-75 prospect, while TeSlaa didn’t even crack the top 150.

Still, when a model as robust as ARM 2.0 thinks TeSlaa is that much better a bet than Royals, you should take notice. This isn’t to say that you should take a guy like TeSlaa a lot earlier than consensus just because a model says he’s a winner, or completely blacklist somebody it’s down on like Royals.

Rather, the name of the game is still value, and if a model like ARM says a guy is underpriced, you should take notice. The obvious use case is thus post-draft waivers, where everybody’s looking to pick up their favorite sleepers. It’s there where ARM 2.0 shines, expressing its affinity for guys like Jimmy Horn Jr. (who it likes more than many fourth-rounders).

In short, if the model strongly deviates from consensus on a player, I’d take notice. Does it mean it’s time to panic sell Jalen Royals, or trade the farm for Kyle Williams? Not necessarily. Hopefully, though, it can serve as a tool for you to find undervalued guys the next time you try and flip a vet for some prospects.

In my previous post, I explored whether rookie running backs were a better value than vets in redraft. By and large, the rookie RB’s fell short of comparable vets; today, we’ll see if that’s the case for receivers, too.

Early-season performance

Everybody knows that rookies take a while to get going; the question is just how poorly they perform at the start of the season. Like with RB’s, we’ll judge these receivers on their play through week 5; if they score over 20 PPR points in a single game, we’ll consider that a “breakout” performance. The intuition is that if a player has a great game early on, you’ll be tempted to keep rostering him until he really turns it on in the back stretch.

As the chart above shows, there’s a pretty significant difference between early-season rookie and veteran performance. There’s not a massive difference between late-round rookies and vets here, with 30% of vets having a breakout game, while only 22% of rookies do so. The difference between mid-round vets and rookies is slightly more pronounced, with vets leading 40% to 26% there.

The gap between top 100 vets and rookies, of course, is far more substantial, but also carries a pretty big asterisk. Top 100 vets go way earlier in drafts than rookies, and this is before we even account for how few rookie receivers go that early. Because of this, we’ll have to frame our analysis differently this time to compensate.

The back stretch

We still need to see how rookies do in the back half of the season first, though, given players draft rookies banking on late-season breakouts. Like with running backs, we first see what percent of receivers clear 100 total points scored after week five of the season. This essentially checks if a player is a useful stretch-run contributor or not, and is a fairly low bar to clear.

Above we see something promising, at least for rookies just outside the top 100. While deep-sleeper rookies get clobbered by vets here, rooks with an ADP between 101 and 150 hold a considerable advantage over veteran receivers.

If we look at what percent of players had two “big games” in the back stretch—i.e., 20-point games, the same criterion we used for an early-half breakout—our mid-round rookies again take a commanding lead. Late-round rookies also get closer to vets here, perhaps suggesting they can offer some explosive potential in the back half, just not consistency.

That previous point may be an understatement, really: mid-to-late-round rookies blow vets out of the water when it comes to having multiple huge games down the stretch. There are caveats, of course, since it’s only a handful of players who accomplish this feat.

Still, this is one area where our small sample size works in the rookies’ favor. Our data contains nearly 90 mid-round veteran WR seasons, yet only in three of those did players chain together four big back-half games. Mid-round rookies, meanwhile, eclipse the vets with four such seasons, doing so with just 27 overall players.

Of course, we still have to address the elephant in the room, which is that I’ve brought top-100 picks back into the picture for this specific metric. I’ve previously noted how comparing top 100 vets and rookies gets to be pretty iffy, since the top vets go way earlier in the draft than rookies.

Yet in the graphic above, the fact that zero rookies with a top-100 ADP had four big stretch games says it all. Even with their ADP handicap—and a small sample size—that’s pretty striking stuff. The lesson, it seems, is to take rookies when they’re a value—i.e., just outside the top 100—and to not pay a premium.

Defying expectations

So far, it seems that mid-round rookie receivers are exceptional values. Even still, our methods are a bit coarse; we need a better way of judging receiver ROI.

This comes in the form of Scoring over Expected (SoE), which essentially looks at how our receivers perform against players with similar ADP. To do this, we divide ADP into buckets—e.g., rounds 4-7, rounds 8-13, and so on—and get the median values for those buckets.

Early in the season, the biggest leaders in SoE are vets. This isn’t surprising, of course, since as the rest of our data has shown, vets come on much stronger than rookies. Thus, if you’re taking a flyer on a rookie, it might be good to get some veteran starters to tide you over.

When we look at data from week six and beyond, however, rookies really step up. In the table above, there’s strong rookie representation, despite there being far fewer rookies overall than vets. (Note: click the right arrow above the table to tab over and see the full data.)

Granted, there’s a reason this metric is called Scoring over Expected, since few of our breakout rookie seasons in the table above were predictable. Anyone who tries to convince you they knew Amon-Ra St. Brown would be that good, that quickly probably has a bridge to sell you, too.

Even still, the table above affirms the trend we’re seeing that some rookie WR’s might be going too late in redraft. You don’t need to be a rocket scientist to figure out that guys who went in the first two rounds of the NFL draft are pretty likely to pop off.

Group performance

While looking at individuals is great and all, we need to understand the overall trends better. This way, we’ll be properly equipped to exploit the inefficiencies in receiver ADP.

Unsurprisingly, many rookies fall well short of expectations through week five. While rookies taken in rounds four through seven seem to do well here, this only includes 11 players.

We start from this round because, like before, rookies are poorly represented in the early rounds. No rookie in our dataset has a first-round fantasy ADP, and only one (Marvin Harrison Jr.) lands in the second.

When we look at the back stretch of the season, things come into focus. So far, late-round rookies have looked poor in comparison to their veteran peers.

However, the discrepancy has never looked so stark, with rookies firmly underwater vs. expectations. While vets with a 14th-round ADP (or later) roughly meet their already-low expectations, the median rookie scores five points below expectations.

Granted, this isn’t terribly meaningful, since these are guys who are fringe picks at best. Anyone drafting them is almost certainly reaching; maybe they’re making a Homer pick, or taking a rookie they really like. For example, somebody took Luke McCaffrey in my league last year, even though he had an ADP of 244.

Even still, our data says the upside for these guys is basically nonexistent. The arguments that support drafting late-round RB’s don’t apply here either. Taking a late flyer on a rookie RB at least demonstrates an understanding of positional scarcity, though I’ve poked holes in that strategy as well.

Summary

So where can we find surplus value, then? If you’re looking for a proactive strategy, and can stomach waiting a bit for a good return on investment, then the answer seems fairly clear. Everything else being equal, you should hone in on in on rookies with an ADP between rounds four and 13.

Again, this doesn’t mean all rookies are good; you shouldn’t be reaching on guys with an ADP South of 200, for example. Nor does it mean you can build the whole plane out of these rookies, since they do take a few weeks to come on.

Still, this is strategy with league-winning upside. A lot of this is certainly due to the increased level of rookie receiver play, which is itself the product of factors too numerous to get into here.

The simple answer is that if you can get guys who were top-50 picks in the NFL Draft on your team, it’s probably not a half-bad idea. While this strategy is far from infallible, it’s certainly a market efficiency waiting to be exploited.

It might be even better, really, to let your league-mates draft these rookies, then trade for them when their stock is in the toilet. You’ll likely be the one laughing to the bank when your guy turns it on down the back stretch.

Most of my posts so far have centered around dynasty leagues, but today, I’m tackling a question that’s more relevant to redraft formats. Simply put, I want to know whether rookie running backs offer better return on ADP than veterans.

Defining RB Success

The ultimate question of whether rookies or vets are better value is not one of what, but when. That is to say, you ideally want your running backs to perform well early, for a variety of reasons.

If your guy takes a while to get going, you either want to drop him or trade him. Doing the latter will be significantly harder for an under-performer, and you’ll often be stuck coping your pick can turn the corner.

This leads me to my next point: if your guys can’t produce early, you at least want them to turn it on late in the season. This isn’t to encourage sunk-cost managing, of course, but I also don’t expect managers to jettison top draft picks without a good reason to do so.

The week 5 cutoff

For most fantasy managers, the time comes when they have to cut bait on a player, whether they like it or not. At some point, you have to stop coping that your guy is going to supplant the starter, the opportunity cost of rostering him over a waiver pickup proving too great.

Where is this mythical cutoff point, though? For the purposes of this exercise, let’s choose week 5; if a player hasn’t produced by then, he probably isn’t somebody worth rostering.

Should breakouts move the needle?

How, then should we define what playing well looks like? The answer, in my mind, is pretty simple: a player should have a breakout performance. If a player crosses the threshold—defined here as having at least one 18-PPR game—then managers will feel more justified in continuing to roster a guy.1

The problem, of course, is that this holds late-rounders to a much higher standard than early picks. Thus, we put players into three buckets: early picks (top-72 ADP), mid-round picks (ADP 73 through 144), and late-round flyers (ADP 145 and onward). Note that our cutoff is ADP 225; this was chosen because it gives us leeway to include backs likely to be drafted as handcuffs.

The end result is above; at first blush, it seems like rookies lag far behind vets in early-season performance. This isn’t really surprising, however, since most rookie picks are really just managers betting on a mid-season breakout.

Even still, the gap is large enough that this breakout has to be pretty significant when it does come. While our early-round rookies look pretty good here—more on them later—the mid-round rooks lag way behind comparable vets. For late-rounders, the picture is slightly fuzzier, with neither group exhibiting the early promise you’d like to see from them.

Late-season success

This is all well and good, but we’re still ignoring the other half of the problem. If rookies are lagging behind in the early season, then they really need to come on strong in the back half.2

Unfortunately, that doesn’t seem to be the case. The chart above shows that, from week six on, more vets score 100 total points than rookies do. Even if this benchmark is fairly arbitrary—ideally, you’d want a player to score even more—the fact that many rookies aren’t clearing the bar is pretty worrisome.

What’s noteworthy, too, is that this isn’t the result of vets being picked earlier (which would imply they’re higher-quality players). Within each bucket, rookies and veterans have roughly the same median ADP.

Notably, the biggest ADP gap—about eight spots—is between early-round vets and rookies. That our top rookies nearly match comparable vets in spite of this handicap is impressive, and might suggest such rookies are undervalued.

Late-season star performances

With our 100-PPR benchmark, we have a decent idea of which RB’s will be consistent contributors. However, we really want to find out which players are capable of producing like starters.

Let’s then define a “star performance” as game where a back has 18 or more PPR points, which is enough to give them real consideration as your long-term starter.

We’ll start with players who had at least two such great games after week 5. It once again looks like rookies taken within the first six rounds are the juice, since they outperform vets here. Mid-round rookies, too, are on about equal footing with the vets.

Yet the late-round rookies really haven’t delivered on expectations. Under ten percent of them will turn in at least two 18-point performances in the back stretch, while 24% of vets clear the bar.

At this point, we have to question the upside of these fringe rookie picks. From what we’ve seen so far, if a late-round rookie back is anything other than a handcuff, he might be a wasted pick.

What about real studs, though? How many picks turn into players who pop off every week? The answer, it seems, is remarkably few, with only early-rounders consistently becoming back-half studs.

The notable thing here, however, is how few mid-round rookies pass the test. Just three percent of mid-round rookies have four back-stretch games north of 18 points. Meanwhile, 19% of vets surpass that mark, meaning there’s a significantly higher chance of mid-round vets performing like stars.

It’s also interesting how late-round rookies and vets do equally poorly here. You can’t bank on either group to perform like superstars; instead, you’re hoping your late-round picks can contribute as flex plays or spot starters. If that’s what we’re looking for, then vets hold a clear edge.

Trends over time

Note, however, that we’ve been looking all the way back to 2015 for most of our analysis; are there any trends we should be noting in recent years? Let’s see if, by breaking our analysis into two groups—2015-2019 and 2020-2024—anything interesting comes of it.

Looking at how many players cleared 100 total points from week six on, the picture is quite interesting. Top-72 rookies are clearly the best bets here, though with the caveat that the sample size is only nine backs.

This is not the case, however, for our late-rounders, who boast a far more substantial sample size. Indeed, it seems the bearish case for late-round rookies was largely driven by our 2015 through 2019 classes. Not even 10% of those backs became solid second-half contributors.

Since 2020, however, late-round rookies have outpaced vets. Roughly 30% of those rookies hit the 100-PPR threshold, compared to 25% of vets. It’s a small win for such rookies, who have otherwise lagged far behind end-of-draft vets.

If we look at the percent of players who had at least two 18-point games after week 5, though, the picture’s a bit more grim. Less than five percent of late-round rookies from 2015 to 2019 had two such games, while only 13% did from 2020 to now.

Note, of course, that this isn’t really all late-rounders, since our ADP cutoff is 225. Even still, I think this encompasses all reasonable picks, and then some. You likely aren’t drafting guys below that line; instead, they’re more likely to be mid-season waiver adds.

In any case, the takeaway is clear: late-round rookies are not going to be your end-of-season saviors. If you want a guy with a chance, however slim, of being a plug-and-play starter down the line, you should probably bet on a player with some prior NFL experience.

Summary

So what, then, did we really learn here? Let’s break down the key takeaways:

• Vets have a higher chance of producing like stars

• Late-round picks, meanwhile, have little star upside

• Early-round rookies might be slightly undervalued

• Late-round rookies are often bad picks

To put it even more concisely, you should probably be taking unsexy vets across the board. The only exception might be in the early rounds, where rookie backs have been over-performing (and under-drafted) in recent years. Even then, vets in that range have more big games down the stretch, thus reducing the rookies’ edge there.

What’s most striking, really, is that most mid-to-late-round rookies boast little late-season upside. This is a problem, of course, since that’s almost the entire reason people draft them. If they both start slow and don’t finish strong, then there’s little reason to pick such rookies.

A lot of this, too, is probably driven by vets often being higher in a team’s depth chart than rookies. The logic is simple: a second-string vet is more likely to see the field than a third-string rookie. Even if they do see the field, rookies might have a shorter leash for fumbles or blown blocks in pass protection.

This is getting into the realm of speculation, though, so I think it’s fine to end here. Ultimately, the simple argument for vets is one you’d probably be following anyway, i.e., you should take vet handcuffs for your starters over enticing rookie backs.

It’s a terribly boring answer, really, one that sucks the fun out of the sport. But when you’re etching your name into your league’s trophy at the end of the season—or at least avoiding a last-place punishment—it’ll all be worth it.

To wrap up my projection series (I’m still waffling on whether I’ll actually build something for QB’s), today we’re going to cover the 2025 tight end class. As I did with running backs and receivers, I’ve trained a machine learning model to predict tight end performance.

The model configuration is broadly similar to that of my RB and WR models, and I’m still using the same data sources.1 Tight ends, however, are a slightly more difficult problem to solve; I’ll explain my process below.

Model Performance

Overall, our Tight End model performs fairly well, though there is some year-over-year inconsistency. It’s still about as good—if not better—than my receiver model, which was also stumped by a single draft class. Here, the laggard class is 2022, with an R² score of .31.

The difference, though, is that the problem class for receivers forced me to rethink my whole strategy: do I adjust my model to raise the floor performance of my worst year, or do I go for the best performance overall? Ultimately, I chose the model that boosted 2023 performance the most, which had real trade-offs. My receiver model now undervalues athleticism, something I might rectify for next year’s class.

This is certainly not the case for Tight End prospects. As our feature importances in the next section show, athleticism is, excepting draft slot, the most important facet of a TE prospect’s profile.

Feature Importance

Above are the most important features for our model; though it utilizes other features, their impact is significantly smaller on actual out-of-sample data.2 Our totals above are based on how much removing them hurts our R² score; thus, they don’t sum to one.

As I hinted at earlier, it seems you not only want a TE who’s a combine warrior, but one who’s drafted early. Notably, however, a player’s draft slot is less impactful for TE’s than for other positions, which tracks with conventional wisdom, too.

As far as the combine goes, three areas stand out. A top-flight TE prospect should have good straight-line speed (40 time), agility (3-cone), and explosiveness (burst score). Burst score is simply a weighted combination of a player’s vertical and broad jump, pulled from the ever-useful Pahowdy spreadsheet.3

Great, you might be thinking, but what about actual production metrics? Well, for that we turn to yards per route run (Y/RR). While our model finds some utility in aDOT and air yards, players those stats rate highly could just be running sprints out there, without actually producing. Players Y/RR views as successful, by comparison, are making every snap count.

2025 Results

Above are my three-year PPR projections for the 2025 tight end class. (Click the arrows above to switch between tabs; if you’re on mobile, scroll down to see the full list). I should first note that the upside cutoffs here differ from my RB projections, though the risk breakpoints remain the same.4 Essentially, the bar to clear for Tight Ends is lower, taking a smaller projection to be considered high- or medium-upside.

At first blush, it seems like Mason Taylor occupies his own rung on the ladder, a sort of tier 1.5, if you will. This is driven by his extremely slight advantage in draft position, relative to his peers. Given he was only taken four picks before Ferguson, we can likely chalk it up to a quirk in our model.5

Take away this leg-up, and Taylor’s relative strengths become questionable; however, this is before we consider just how closely bunched together our second-tier TE’s are. Taylor’s Y/RR was a fairly middling 1.1, though it’s worth noting that Ferguson’s was only 1.5, and Arroyo’s was 1.6. In any case, all three of these players are tremendous athletes, whether it be per their combine testing, or tracking data in Arroyo’s case.

We’ve now, it seems, reached an impasse with the 2025 class, our second-tier prospects making it difficult to differentiate between them. Thus, we now turn to the 2021 class, which may offer some clarity.

Much like the ‘25 class, many players from the ‘21 class were projected to score somewhere around 150 points, give or take a couple dozen. What’s remarkable, however, is that, barring a couple misses, the model’s projections were mostly right.

The takeaway is, my model isn’t just blowing smoke when it sees a tight end class as closely grouped together. It’s making its judgments based on years’ worth of data, and is fairly likely to be right.

Does that help us figure out which of Taylor, Ferguson, and Arroyo will be the best NFL player? Probably not, but it does underscore how similar TE prospects can be, and how hyper-fixating on any nominal edge between players can be dangerous. This is especially true for this trio, given each is heading into situations that will offer them ample playing time. My best advice, then, is to take whoever is available latest, rather than singling out a must-have “your guy” among these three.

The Fannin Dilemma

If that doesn’t satisfy you, then I’ll offer up Harold Fannin Jr. His projection is still likely driven by being a mid-third rounder, though he does surpass earlier picks Arroyo and Ferguson in our rankings. We could perhaps chalk this up to an excellent three-cone and Y/RR, with his performance in the latter stat (3 Y/RR) far outpacing his peers.

Again, however, there are caveats: it’s well known that Fannin faced softer competition at Bowling Green. This would generally be fine, but he’s also quite undersized as well, which ought to give us pause due to the demanding athletic restrictions being an NFL tight end requires.

In any case, I think the model has Fannin properly rated, as a highly productive college player with a great three-cone, but question marks about his broader athleticism. Ultimately, which player you choose among these four tight ends is partially about your preferences, but also opportunity cost. Given Fannin is going later than most of his peers on his list, I think he’s a solid bet in the late third of rookie drafts.

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Summary

Again, I’m not reinventing the wheel here; my model found the same things to be true about tight ends that many others have before. Yet they’re still useful lessons to internalize, so I’ll list them below.

In short, you want your tight end to be:

• Explosive (good broad/vert jumps)

• Fast (40 time)

• Agile (3-cone)

• Efficient (Y/RR)

• Picked early

This model isn’t perfect, but it takes a lot for it to be really wrong. One such failure, as you may have noticed above, was Chargers TE Tre’ McKitty, who got a huge boost for being a third-rounder.

McKitty was probably an aberration, though, and validates the importance a player’s draft slot more than it denigrates it. After all, McKitty was a middling athlete, ranked as low as TE13 by some evaluators, and was considered a late-round pick by others. So, of course Tom Telesco selected him in the third round, throwing everything out of whack.

The point is, playing tight end in the NFL is hard, and most late-round talents of McKitty’s caliber don’t make it. The analytical case against drafting TE’s or LB’s early isn’t entirely based on the low value of those positions; it’s also that it takes those guys a long time to get good. Even if the guy you drafted is solid by the end of his rookie contract, you’ll have gotten little value in the mean time.

Therein lies the paradox of tight-end production. The position is ripe with steady producers, and many mid-round tight ends will make it in the league. Yet it’s still a question of when, and even though the three-year outlook is often sunny, you probably don’t want to invest in an asset that’s very likely to decline after year one.

This means that you’re probably better off huffing copium that, say, an Ollie Gordon is going to bounce back in the league than you are drafting Gunnar Helm. Yet even if tight ends are worth less, they aren’t worthless, and a high-floor mid-rounder is often a safer bet than a lotto-ticket running back like Dylan Laube.

Even if your guy never becomes a real asset, if he’s at least perceived as one, that’s all that matters. It’s how I flipped Erick All from waivers into a part of a larger package last year, something I probably couldn’t have done with a UFA RB flameout. It’s how I picked up Jonnu Smith from the scrap heap, and ended up with a more-than adequate contributor.

However people see tight ends, arguably the black sheep of fantasy football, there’s still gold in those hills. You just need to know where to find it.

In my previous post, I outlined my process for creating a machine learning model for wide receivers. I’m going to spend less time in this post outlining my methodology, if only because I covered it so exhaustively in the prior article.

Today, I’m unveiling a new model that predicts running back performance. We’re still predicting PPR points scored over the first three years of a player’s career, so the outcomes will look fairly similar to those from my last writeup.1

Model Performance

Above are the R² scores for my model on my “test” data, i.e., out-of-sample data it wasn’t trained on. This, in short, means the model has not seen any of the data from 2021 onward, and is predicting blind off what it’s learned so far.2

With that in mind, the results above are quite good. Even if we filter out undrafted players, we get a correlation of .78 between our predictions and actual production, with an R² of .54, for 2021 and 2022 combined. Per one Redditor’s analysis, this is meaningfully better than most other models’ past performance.

This warrants multiple qualifications, of course. The models cited in the analysis were judged both on different criteria and time frames (the Redditor’s comment is from at least four years ago). It’s entirely likely, too, that 2021 through 2023 were relatively easy classes to predict; by comparison, 2024 looks much more difficult. Since many of its members have yet to see the field, however, I’m not including it in my analysis.

It’s also worth noting that running backs might just be easier to predict than other positions. Per Next Gen Stats3 , their model-generated “Production Score” is more indicative of running back success than any individual feature. Conversely, that same score is only fourth in importance for receivers, and 12th for tight ends. This means that, relatively speaking, analytical models are good at predicting RB success, while they lag behind individual metrics (like 40 time or overall athleticism) for WR’s and TE’s.

Feature importance

Above are the feature importances for our model. Obviously, a player’s draft slot is the most important feature, yet it isn’t the only useful one. To get a model as robust as ours, it really does require most of our other features: every bit of performance counts.

This might benefit from the minimal overlap between our top features.4 Most redundant are combine results, which tracks: a player who tests well in the shuttle drill will likely turn in a good three-cone result, too. This is why a player’s 40 time ranks so highly, if only because it’s the one drill RB’s rarely skip.

What this means is that many metrics which seem extremely similar—like Receiving Yards per Team Pass Attempt (RYTPA)5 and PFF’s Caught%—actually offer unique info. While there still is some overlap (around a .22 correlation), they’re each telling us different stories. RYTPA tells us if a back was a big part of his team’s passing game, while Caught% indicates whether he can actually catch the ball.

The 2025 Class

Above are the model’s three-year predictions (in total PPR points scored) for the 2025 RB class. The upside and risk categories are derived from various cutoffs in the ceiling and floor columns, respectively. (Note: click on the right arrow above the table to see the rest of the class.)

Much like Travis Hunter broke clear from the pack in our receiver group, Ashton Jeanty is leaps and bounds above our other running backs. I will note, however, that the margin between him and Hampton might be smaller than it seems. Some other versions of my model ranked Omarion Hampton above Jeanty, which speaks to the UNC product’s potential.

Yet in its current state, the model reckons Hampton’s ceiling is actually worse than that of TreVeyon Henderson, who is second only to Jeanty in terms of upside. Henderson, however, is flagged as a medium-risk prospect, while Hampton is decidedly low-risk. Perhaps this is due to the volatile nature of explosive backs like Henderson, since the model isn’t explicitly accounting for his extensive injury history.

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At first, I thought Henderson being a second-rounder was why the model saw him as comparatively risky. Yet multiple backs taken later are seen as higher-floor picks by the model, including Quinshon Judkins and Cam Skattebo.

Skattebo in particular is a good example of the model’s robustness, and how it relies on a lot more than just draft capital. His bullish projection is likely driven by his exceptionally high RYTPA, which is twice—if not four times—the number seen by most other RB’s.

There are a lot of nits to pick, of course, about whether his excessive usage is actually a good thing, due to the wear-and-tear that comes with it. It’s even more valid when discussing Devin Neal, for example, who saw his quickness take a downturn in 2024. The model likes Neal enough to justify his relatively high ADP, but its low floor projection is a stark reality check for fans of the sixth-rounder.

Finally, we can see that RJ Harvey and Bhayshul Tuten share extremely similar outcome profiles. The difference, of course, is that the model sees Tuten as a high-risk player, while Harvey is merely rated as medium-risk.

In any case, it sees Harvey as much closer to the second tier of backs than the first, his saving grace being taken far earlier than his peers in the NFL Draft. Perhaps this is my own personal bias seeping in—I’ve drafted Tuten in two leagues, after all—but I think acquiring Tuten in the mid-to-late second is a far better deal than Harvey in the late first.

Player Comparisons

Above are our upside and downside comps for the 2025 running back class. These are, once again, selected from the best- and worst-producing backs among a player’s most similar comps.6

Many of these comps are really great, with minimal thumbing of the scale on my part. It’s hard to think of a better upside comp than Nick Chubb for new Browns RB Quinshon Judkins, for example. Equally rich is Bhayshul Tuten getting a Devon Achane comp; both ran a 4.32 forty-yard dash, while also being undersized prospects.

Not all our comps are this perfect, of course, but they’re still interesting for the top two guys in this class. Jeanty has an upside comp of Christian McCaffrey, while his downside is fellow Raider great Darren McFadden, which basically means he’s too good to fail.

Hampton’s upside case is a bit curious, given how the blazing-fast Chris Johnson probably matches him more from a production standpoint than a speed angle. Rashaad Penny, however, is more interesting, especially given Hampton also ran a 4.46 forty-yard dash. With Penny being a talented late-first-rounder whose career was derailed by injuries, I think that’s a pretty fair floor case for Hampton.

Final Thoughts

So what are the takeaways, then, for people who can’t just run my model on their own? What should we expect not just from this class, but further running back classes down the line?

Well, the unsexy answer is to take backs who were picked early in the NFL draft. Even if that seems blindingly obvious, it still tells us something when the model ranks a late-round back like Devin Neal ahead of mid-rounders. Since draft pedigree matters so much, a sixth-rounder has to be a strong producer to outshine earlier picks.

Yet late-round picks like Neal also have a nonzero chance of being cut, which is one of our model’s shortcomings. Other issues abound, too, such as our model overvaluing a player based on his peak year. This is the case for Ollie Gordon, who, in 2024, couldn’t quite reach the stratospheric heights of his 2023 season.

With all these caveats, what’s left for us to fall back on? If I had to sum up our model’s findings, you want a back with these qualities:

• Was picked (reasonably) early in the NFL draft

• Ran a fast 40

• Is a good receiver

At the very least, you want a back who clears the bar in two of the three areas, like Bhayshul Tuten, who ran a good 40 and can also catch the ball. Barring that, you need a guy who’s superlative in one area, like Cam Skattebo, who, as an RB, commanded a truly ridiculous share of his team’s pass game targets.

That’s all for this post; once again, you can follow me at capn-collins on Bluesky, and at capn_cc on Twitter. Thanks again for reading; hit me up in the comments with any lingering questions (and, of course, subscribe for free updates)!

By now, everybody and their uncle has developed some sort of predictive model for fantasy football. That I made my own is hardly unique; I’m not even sure my model is meaningfully better than existing ones.

Therein lies the problem, however: it’s difficult to check your work against others’ when people are both modeling different outcomes and using different benchmarks for success. Thus, I decided to build my own Wide Receiver model to predict fantasy production over the first three years of a rookie receiver’s career. The goal was to produce a model that I myself found empirically sound, while also learning something about fantasy analysis along the way.

The Model

I built my model using an NGBoost framework, with decision trees as my base learners. Even for a relatively technical article, though, this is something I don’t want to wade too far into the weeds on, lest we distract from the main takeaways.1

The bottom line is, we want a nonlinear model2 that can capture complex relationships between our inputs (e.g., Dominator Rating) and our dependent variable (PPR points scored). We also want an algorithm can give us a probability distribution, instead of just a single predicted value. This lets us predict ceiling and floor outcomes for players, too.

NGBoost met both these requirements, while also delivering better results than comparable algorithms. It therefore seems to be more than fine for the task at hand.

Model performance

Above is my model’s performance for draft classes considered “out-of-sample” (i.e., data it hadn’t seen yet). It was trained on the 2008 through 2020 draft classes3. Its goal, once again, is to predict PPR scoring over the first three years of a rookie receiver’s career. Devonta Smith, for example, scored 668 total PPR points in that window.

Our model shows an R² score of .5 for the 2021 class and .51 for the 2022 class, which I’d consider relatively decent. By comparison, Playmaker Score—a metric formerly hosted on Football Outsiders—showed an R² of .25. (Granted, the linked article is from a decade ago, and they were also predicting a different dependent variable.)4

The model fares slightly worse in predicting the 2023 and 2024 classes, though that data is obviously incomplete. We are, after all, predicting the first three years of a player’s career, and those players haven’t played for that long yet. Thus, I’m comparing my predictions to a three-year rate based on the players’ production so far.

This has obvious issues, of course: those players could suffer massive injuries, or have late breakouts, each of which could bring our predictions more into line with reality. Yet this ignores the unique nature of the 2023 class, which features many notable outliers. Indeed, the class was full of players who bucked expectations, like Dontayvion Wicks, Pop Douglas, and, of course, Puka Nacua.

Still, while outcome matters, process is equally important. It’s heartening to see, for example, that my model was pretty in-line with Playmaker Score’s 2023 predictions. Despite its relatively low 2023 R² score, my model also had great called shots on Tank Dell and Josh Downs, too, ranking both as top-five prospects despite them being third-rounders. It was also notably bearish on Quentin Johnston, which provides me little solace as a Chargers fan.

Feature Importance

Above are the most important features for our model, in terms of impurity reduction. At the top is a player’s draft spot; it’s by far the most important feature in the model. This jibes with the work done by Zilla Fantasy, and is also borne out by our features’ correlations with PPR, where the relationship between when a player was picked and their three-year outlook is twice as strong as any other feature.

Our other variables come from the ever-useful Pahowdy spreadsheet, which itself sources additional metrics from PFF.5 Outside of knowing a player’s draft spot, his average Dominator rating is the second-most important feature. It tells us what percentage of a team’s production a player commanded, in terms of receiving yards and TD’s.

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Our third-most important metric is Best PPG/Age, a Pahowdy metric that takes a player’s best points-per-game in a season, then adjusts for how old they were. Players who had great success at a young age, like Malik Nabers, are rewarded. Conversely, players like Roman Wilson, whose best year came late (and was also underwhelming), are penalized.

Rounding out our list are some features that I found highly predictive in my previous post on the 2025 WR class. This includes Targeted QB Rating and aDOT-adjusted Caught%, both of which I discuss at length in that piece. The former essentially tells us how often a guy makes his QB right, while the latter judges his ability to catch, adjusted for target depth (i.e., deep shots matter more than layups).

2025 Rookie WR’s

Now that we have the technical stuff out of the way, we can finally see what the model thinks of the 2025 receiver class. Our player comps above (scroll to right to see on mobile) are based on the highest- and lowest-producing players, respectively, among the five most similar players. Our ceiling projection is a player’s 80th percentile outcome, per our model, while the floor is their 20th percentile outcome.

Travis Hunter clearly stands out: not only is he the clear best prospect in this class, he’s basically the best prospect since the 2021 class. That class contained Devonta Smith, who had a slightly higher ceiling projection than Hunter. It is worth noting, however, that the “3-Year PPR” column is what our model thinks the median—or 50th percentile—outcome will be for the first three years of a player’s career. For somebody like Hunter, this of course gets to be an inexact science, and the variance contained in his two-way endeavors isn’t baked into my model.

The rest of the prospects, however, jibe pretty well with common assumptions. Tetairoa McMillan, for example, is a relatively “safe” prospect with questions about his ceiling; he has the highest floor of anybody beside Hunter, with a downside comp of Rashod Bateman. Matthew Golden is at the opposite end of the spectrum, boasting star-level upside (and a comp of Garrett Wilson), yet also having a scary low-end comp in notorious bust Corey Coleman.

What’s most interesting, however, are the cases where the model deviates most strongly from consensus. As you can see above, the model really likes Pat Bryant. Compare him to Jalen Royals, however—who was taken about 60 spots later—and you may be confused. Royals boasts a slightly better Dominator rating, as well as a stronger best season in terms of age-adjusted Points per Game.

This would seems to suggest that we may be leaning too hard on draft position. That ignores, however, the fact that Bryant boasts a far better projection than Jaylin Noel, who was taken merely five spots later than him. This is likely because Bryant’s Dominator rating and best age-adjusted PPG season are both better than Noel’s. Bryant, too, has the fifth-highest ceiling of any prospect in this class, even better than many first-rounders, which suggests our model relies on far more than just draft pedigree alone.

The rest of the field offers few surprises, unless you’re really into ranking late-rounders. One interesting quirk is the model liking Kyle Williams much more than the beleaguered Royals and Noel, players who would be considered in the same tier by ADP and most rankings.

The biggest shocker, though, is its bullishness on Isaac TeSlaa, almost certainly driven by his status as an early draft pick. In some sense, I think this is sound, given he’s more likely to see the field than many of his peers. That said, I also think his poor Dominator rating and small-sample concerns are cause to pump the breaks.

Conclusion

Hopefully all this has given sufficient insight into my process, and why I built the model the way I did. It’s worth noting that, at the end of the day, there are still some of my own biases baked into this model, given I’m the one who chose the criteria for its success. Some of the models I tested performed slightly better, for example, on the 2023 and 2024 classes.

Yet I didn’t want to over-index on those draft classes alone, for a couple of reasons. First is the obvious one: those classes simply haven’t played a full three years yet, meaning I’d be basing my model selection on a noisy process (with a lot of projection, too). Second, the subjective quality of these nominally “better” models I created was suspect. Their median projections were often quite low, and often were far too bullish on late-round picks in a way I couldn’t empirically justify.

This ultimately leaves us with a model that, based on my experience as a data scientist, lands in a reasonable middle ground. Most of its predictions are in line with both common sense and other models: I reckon it’s better to be very bullish on Travis Hunter and other first-rounders, for example, than it is to arbitrarily short them.

It also has enough interesting called shots, too, to be considered usefully unique. I don’t think I’ve seen too many other models this high on Pat Bryant, for example, nor have I seen many this bearish on both Jalen Royals and Jaylin Noel. What matters most, however, is that I can explain why my model is ranking those players the way it does, which is arguably more important than any performance boost I could engineer.

Thanks again for reading; you can follow me on Bluesky (‪@capn-collins.bsky.social‬) and Twitter (@capn_cc), and feel free to reach out to me on either site, or in the comments below, with any lingering questions.

As professional sports teams move further into the analytics era, the average fan has become increasingly invested in draft-pick horse trading. Much of this is spillover from NBA discourse, where the amount of picks being swapped—and the protective conditions attached to them—is the subject of rabid fascination. Yet as more and more fans get into dynasty football, the art of draft-pick valuation moves becomes a decidedly practical concern.

The goal of today’s article, then, is to try and figure out where potential inefficiencies lie in the draft-pick market. This is partially inspired by the real NFL, where forward-thinking teams have developed their own in-house trade charts, in order to outmaneuver other teams still using the Jimmy Johnson chart. The Johnson chart’s a good analog for the website KeepTradeCut (KTC), too, since both are the gold standards for asset comparison in their respective areas.

We aren’t using KTC data today, however; instead, we’re pulling from the FantasyCalc database, which uses slightly different formulas. Compared to KTC, FantasyCalc greatly devalues future picks, partially because it’s agnostic as to where they’ll end up. KTC, by comparison, requires you guesstimate whether a pick will land early or late in its respective round, which is an obviously imprecise science.

The biggest bargains

Let’s dive right in, then, and examine which draft picks are being consistently undervalued by dynasty managers (at least per FantasyCalc’s logic). To keep the scope relatively simple, we’re only using data from one-for-one or two-for-one trades. We also isolate each pick so that it’s the only asset on its side of the trade; that is to say, it’s always the “one” in a “two-for-one” scenario.

This leaves us with mostly third-rounders—and some seconds—being undervalued by FantasyCalc’s logic. (Note: I couldn’t pull fourth-rounders.) In fact, when managers sell off current-year thirds, they only “win” the trade a quarter of the time. The story for early second-rounders is slightly better, with roughly a third of them recouping positive value. The data still suggests, however, that sellers are being underpaid.

We now turn to the median value—per FantasyCalc—that managers are losing by trading away these assets. These numbers don’t map perfectly to KTC value differences, due to various discrepancies between their formula and FantasyCalc’s. Still, I think they capture the idea that people are consistently losing value by trading away early seconds and late thirds.

Granted, many nominal underpays for third-rounders are actually fair per KTC, and depth often is more valuable for contending teams than picks. That said, I’d likely prefer a late third—or whatever assets somebody desperate might offer to acquire it—over hoping last year’s rookies (e.g., Roman Wilson, Ben Sinnott) might still deliver.

The biggest overpays

The real juice, however, lies in the overpays. Many of the picks people overpay for are, unsurprisingly, current-year first-rounders. This is noteworthy because, as we’ve established, the FantasyCalc algorithm already values current-year picks quite highly.

What this means, then, is that even considering how FantasyCalc gasses up current-year firsts, people are still drastically overpaying to acquire them. Given how gung-ho dynasty managers can get on draft day, this isn’t totally surprising. There’s a fitting parallel, really, to the worst impulses of real NFL teams here, whose convictions lead them to trade up into the first round, often with disastrous consequences.

Interestingly, however, the amount by which managers overpay for primo picks is relatively small. The bottom five picks here—in terms of overpay—are within a 100-point margin, which means the median trade involving them is essentially lateral. Tellingly, two of these are late-second-rounders, suggesting that most second-rounders are appropriately priced.

Here’s where the intriguing part comes in: because other players overvalue first-rounders so severely, they might actually be undervalued. That is, of course, if you can acquire them well ahead of the draft, with the intent to then flip them when you’re on the clock. Given the insane offers people make for these picks, staying and picking is rarely worth it; the player would have to be a generational talent, or fill a dire need.

Who fuels underpays?

Above are the assets most commonly involved in underpays for current-year picks. At the top, we have future picks, which isn’t surprising: managers will gladly pay you Tuesday for a hamburger today. Granted, this is a product of FantasyCalc’s formula, which values current-year picks more highly than future ones.

That said, I do think there’s validity in how FantasyCalc devalues future-year picks. To me, the argument isn’t that future picks are inherently inferior to current-year ones. Rather, it’s that if you swapped a late third for a future third straight-up, you’re failing to capitalize on potential overpays for your pick.

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This is a big reason why FantasyCalc’s formula frowns upon trading current-year picks for veterans. Instead, you should consider flipping your 2025 picks for a surplus of future picks, then use that excess capital to acquire a vet later on. That way, you’re more likely to nab both a short-term contributor and an extra pick next year.

While this is certainly pie-in-the-sky logic, the players above add credence to the notion that you shouldn’t burn current-year picks on acquiring vets. To be sure, some of them offer real upside, like Jalen Coker and George Pickens. It’s harder to make cases for many of the others, though: Kupp’s likely washed, Darnold’s success could be a fluke, and Tank Dell might never be the same again.

What drives overpays?

Interestingly enough, many overpay packages include current-year picks themselves. This is surprising, since FantasyCalc’s formula is quite forgiving of managers paying hefty prices to move up in the draft. The issue is mainly that people are paying exorbitant prices to move up just a couple spots; for example, somebody in the database offered 3.09 and 3.10 to get to 3.04.

You don’t need to be a statistician to know the odds the player at 3.04 will be that much better than those other picks combined are pretty slim. It seems doubly silly, really when you consider how much of a crapshoot third-rounders are in general (see this article from Reddit user u/NassauBeat for more). The player you’re moving up to acquire basically has to be a blue-chipper who fell way down the board; for example, I jumped up to 3.02 to nab Drake Maye in my 1QB league last year.

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Equally compelling is how future picks are often key components of of overpay packages, given how frequently they’re parts of underpays as well. People seem to be recklessly tacking on future picks to deals, running into the same issue found in our previous trade-up. It’s hard to reckon that, outside of exceptional circumstances, this year’s 3.06 is worth both the 3.12 and a future third, for example.

Again, the FantasyCalc algorithm is pretty forgiving of trade-ups in general, which means that the trades it considers overpays must be truly something. It only rates trading a future second and third for 2.09 as a minor overpay, which is pretty surprising. That future picks are still the most common assets in overpay packages thus speaks volumes about how truly egregious those overpays must be.

Summary

Ultimately, the final word on draft-pick trades is the same tired refrain you’ve probably heard before. Even with analytics to guide you, getting an accurate read on one particular player is remarkably hard. This isn’t to say, of course, that you should never trade up, or that you shouldn’t let analytics guide your evaluation process.

Rather, it’s that you shouldn’t be overconfident in your process, even if the math checks out. The best NFL teams, like the Eagles, operate on consensus boards, only moving up to get studs who fell, or when they think there’s a drop-off coming up within a positional group. This is even truer in fantasy, where the tier breaks are more severe than in the actual NFL (refer again to this breakdown for more info).

Being less adamant in your convictions doesn’t just apply to prospects, however; veterans matter, too. If there’s one lesson to learn from our FantasyCalc data, it’s that you shouldn’t indiscriminately flip current-year thirds for middling vets. Rather, the goal should be to flip the third for surplus value in the moment, then use that extra value to snag vets at a discount when needs arise.

Another interesting idea FantasyCalc’s data seems to confirm is just how valuable first-rounders are. To our earlier point, it’s not that there’s anything inherently smart about actually making the pick, given how difficult prospect evaluation is. Rather, you want to hoard first-rounders well ahead of time, in order to get insane offers for those picks come draft time.

Thanks for reading; you can find me on twitter (@capn_cc) or on Bluesky (@capn-collins) if you want real-time insights, and be sure to subscribe so you don’t miss any future posts.

A few weeks ago, I introduced the concept of a Liquidity Index (LI) for dynasty fantasy football. It aimed to solve an issue with player value as defined by KeepTradeCut (KTC), whose trade values are used as a reference point by most dynasty managers.

The problem in question was the frequent disconnect between a player’s nominal KTC value and his actual worth. Even though Jermaine Burton is theoretically worth a fourth-round pick, for example, I doubt you’d get a leaguemate to bite.

This leads us to the formulation of Liquidity Index. If we divide how frequently a player was traded by his KTC value, then we can hopefully get a better picture of how desirable that player really is.

My analysis of Liquidity Index specifically focuses on second-year players, with our scope being the first three weeks in April, before the NFL Draft. This is because these players are some of the most volatile—and therefore most interesting—assets to hone in on.

The NFL Draft, of course, is a major reckoning for the previous year’s draft class, who risk being supplanted by rookies. Liquidity Index is, in large part, capturing these concerns, ones that often lead to a pre-draft trade frenzy. Our goal, then, is to see if it can tell us when this panic is justified, and when managers should cool their heels.

Historical Liquidity

Let’s start by looking at second-year players from the 2022 through 2024 classes. Note that our Liquidity index cleanly fits onto a 0-100 scale. This is because, for each draft class, we divide our initial LI values by the by the highest Liquidity seen that year.

The end result tells us whether a player was traded more (or less) often than we’d expect a player of their value to be, ahead of the NFL Draft. Tank Bigsby stands out, for example, because 73 trades is heavy activity for a guy whose KTC value is just 2500. Tank Dell also ranks highly; while we’d expect a player whose value is over 6000 to be a hot commodity, being traded 173 times is still a ton.

One notable exclusion is Rashee Rice, who, if we left him in, would’ve broken the scale. Rice was traded a staggering 346 times in April 2023, exactly double Tank Dell’s number. He’s exactly who you’d expect to have a hot trade market, in that any sellers would be more than happy with their massive ROI. Buyers, meanwhile, understandably salivated at the chance to own a potential WR1 for the Chiefs.

The 2024 Class

We now revisit the 2024 draft class, which I covered previously. Even though my data now comes from a larger sample, our most liquid players are still all running backs and receivers. This is likely because those are the positions most likely to face new rookie competition, something we’ll touch on in the next section.

The least liquid players (found by tabbing to the right) also make sense. You’d have better luck, for example, selling beach front property in Kansas than offloading Jermaine Burton in 2025. Brock Bowers, by comparison, exists on the other end of the spectrum. While he’s obviously quite valuable, few people are willing to move him, and it’s hard to put together a package of sufficient worth to acquire him.

Impact of the 2025 Draft

We now know which players managers were concerned about heading into the 2025 NFL Draft; the question is, does it matter? If this pre-draft panic was justified, then we’d expect the players with the highest Liquidity Index values to see their KTC values fluctuate wildly post-draft.

The results are somewhat mixed, though the biggest post-draft value drop-offs are hardly surprising. Of the 2024 class, for example, Tyrone Tracy had by far the highest liquidity index, and thus the most overheated market. Managers’ concerns turned out to be justified, even if Cam Skattebo is far stiffer competition than many would’ve expected.

Notably, there were very few significant value spikes post-draft. You can sort the above chart by Value Delta (rightmost column) to get the players who gained the most value; if you do, you’ll see Isaac Guerendo is our only real success story.

On pre-draft panic

It isn’t shocking, of course, that last year’s rookies often lose value after the NFL Draft. If a team drafts competition for breakout rookies—as was the case for Tyrone Tracy or Jalen McMillan—then those same prospects should decline in value.

The chart above confirms this, showing that many second-year players saw a post-draft value dip. The median player is expected to lose roughly 100 points of KeepTradeCut value, excepting QB’s, who remain unchanged. This may be because the same factors hurting position players—like increased competition—mean the opposite for QB’s, who instead get new weapons.

Let’s now take a more granular look at the draft’s impact on last year’s rookies. In short, we want to see if high pre-draft trade activity implies a drop-off—or perhaps even an increase—in a player’s post-draft valuation.

We first see that if a QB is being traded heavily pre-draft, it bodes well for their value after the draft. This mostly implies, however, that high-trade-volume QB’s are more likely to maintain value, and not necessarily gain it. The opposite is true of tight ends, where a high Liquidity Index pretty strongly implies a value drop-off.

Where Liquidity Index falls short is with receivers and running backs. While LI is still our best metric for predicting receiver value changes, its correlation of -.1 is far below the stronger relationships we see for QB’s and TE’s. The correlation for RB’s is even worse at -.04, suggesting we may need to attack the problem from a different angle.

LI and Future Value

Unfortunately, our analysis so far hasn’t produced anything actionable for fantasy managers. While it’s good to know that the NFL Draft is a harbinger of doom for last year’s rookies, our goal is to find assets that will appreciate, and not fall off a cliff.

What if we instead looked another year out? We’ll still use the same data as before, collected roughly a full calendar year after players were initially drafted (that is, after their rookie seasons have concluded, but before the next NFL Draft). From there, we’ll attempt to predict how their values will change over the course of their second season.

The results are mildly positive: Liquidity Index exhibits decently strong correlations with these sophomore-year value shifts. At the very least, Liquidity Index is largely an improvement over its component variables, KTC Value and Trade Count.

It’s also nice to see how much better Liquidity Index is at predicting running back value, which it previously struggled with. We can now say that if an RB was heavily traded after his rookie season, he’s fairly likely to increase in value the following year.

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Year-two breakouts

Let’s look at some concrete examples to bolster our analysis. At a glance, it looks like many RB’s saw a huge value increase over the course of their second year in the league. Notably, many of these players boast a high Liquidity Index, too, meaning there were significant concerns about their long-term viability heading into year two.

The lesson here is that some patience is warranted with rookie running backs as they enter their second year. The reward is very real, if you can stomach the risk. Thus, buying RB’s before the draft from panic-selling owners might be a savvy move.

Tight ends also seem to be great long-term investments, but the results are actually catastrophic. Many apparent jumps in TE value are, in fact, coming from fantasy nonentities, like Darnell Washington. While Trey McBride is a tantalizing success story, he was likely just underrated before his second-year breakout, given his excellent prospect profile.

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While second-year tight ends are clearly subpar investments, quarterbacks might be even worse. In fact, we’d expect a QB to lose nearly a thousand points of value over the course of his second year in the league, which is a truly drastic drop-off.

Is this information terribly useful? Probably not; the 2022 class was known to be awful going in, and the the classes bookending it aren’t faring much better. If anything, it suggests prospect pedigree might matter more for QB’s than any other position, and that chasing the next Dak Prescott—or even Jalen Hurts, for that matter—is a fool’s errand in 1QB formats.

Closing Thoughts

The main takeaway from all this is clear: most players lose value over time. While that seems strikingly obvious, it still bears repeating.

This is especially true after the NFL Draft, when many of last year’s rookies find themselves facing new competition. As a result, the draft is often a catastrophe for these players’ values, with many seeing huge declines. Only quarterbacks see their value remain steady, though they don’t benefit from much of an increase, either.

Don’t let that fool, you, though, as quarterbacks are some of the worst long-term assets out there. QB and tight end values often plummet over the course of their sophomore seasons, with few success stories to be found in our data. Even accounting for small-sample issues, the risk-reward profile of these positions is fairly grim.

Running backs, by comparison, offer a far sunnier outlook. If there’s one actionable insight I can give dynasty managers, it’s to buy the dip on RB’s entering their second year. There’s nuance to this, of course—I don’t think acquiring Tyrone Tracy right now would be a particularly inspired move—but the research I’ve done in this article backs up the fantasy community’s RB-first mentality.

As for receivers, the story’s a bit murkier, the relationship between trade activity and receiver value being noisy at best. Your best bet is probably to lean on predictive metrics as researched by Ryan Heath instead, given how strongly many of those variables correlate with next-season scoring.

Hopefully all this has been helpful; if you liked my analysis, hit the subscribe button (it’s free!) and comment away with any lingering questions or concerns. I can be found on both Bluesky (capn-collins.bsky.social) and Twitter (capn_cc), too, for anyone who wants to connect.

My last post introduced the Mitchell Line, a new measuring stick for running back prospects. In short, I defined RB success as a player eclipsing 250 PPR points scored over the first three years of his career. It’s named after Elijah Mitchell, the lowest-scoring player who crossed the line, who’s a solid reference point for RB success.

All in all, I’m satisfied with how it turned out; almost every player above the line was a veritable starter, while the guys below it were fringe players at best. RB data, however, often tells a far cleaner story than receiver data does, since so much of it boils down to college production, receiving ability and opportunity. Receiver statistics, by contrast, often lie, with scheme, usage and QB talent obfuscating the real story.

WR Value and the Shakir Line

While RB’s have the Mitchell Line, receivers have the Shakir Line. For WR’s to clear it, they must produce at least 300 total PPR points over their first three years in the league. The name comes, of course, from Bills WR Khalil Shakir, who just clears the bar. It’s an effective cutoff, in my opinion: with much due respect to Tajae Sharpe and DaeSean Hamilton, we want better producers on our team.

How can we figure out which prospects can clear this bar, then? One useful example is above, where we look at players with a Next Gen Stats Production Score below 75. I’ve already written about Next Gen Stats (NGS) indicators at length; in short, the chart above shows players with iffy college production, who are long shots to succeed.

The success stories in the chart show that athletic outliers can overcome poor college production, with many above-line players boasting very high Relative Athletic Score (RAS) values. Many who clear Shakir Line with more modest RAS numbers—like Amon-Ra St. Brown and Gabe Davis—are still notable athletes, too. Gabe Davis is quite fast for his size, and Amon-Ra St. Brown’s the son of a world-class bodybuilder, which speaks to his physical prowess in a way that RAS might miss.

The best indicators of WR Success

Many of the stats above are credited to Peter Howdy, AKA “Pahowdy”, whose spreadsheet gave me much of this data. The credit for many of these stats is jointly owed to Mr. Howdy and PFF, with Howdy adjusting PFF stats for teammate and target-difficulty considerations. This is the case for two of the metrics we’re using, Caught % (aDOT-Adjusted) and Targeted QB Rating (Teammate-Adjusted).

Our Caught% metric is fairly self explanatory, adjusting a receiver’s catch rate by their average depth-of-target (aDOT). This penalizes guys being fed lay-ups, while rewarding dudes laying out for 50/50 balls. We use Targeted QBR, by comparison, to see how guys “make their QB right” when the ball’s thrown their way. Howdy employs similar adjustments for teammate quality, in an attempt to suss out how much of a player’s Targeted QBR is his own skill, and how much is just his QB balling out.

The goal here was to find indicators where, on average, players above the Shakir line scored higher than those below it. I wanted this to hold for all four rounds, too, even given small-sample-size considerations. If a metric is meaningfully worse at predicting third-rounders, for example, then it loses a good deal of utility.

Useful metrics for late-rounders

One notable issue with our previous metrics is their usefulness wavers a bit in the later rounds. Granted, the number of fourth-round success stories is very small, so the volatility in the chart above should be taken with a grain of salt.

At the same time, though, the two metrics above might be our most informative ones. RoE is an undefined acronym from the Pahowdy spreadsheet, and compares a player’s Receiving Yards per Team Attempt to players with similar slot rates and aDOT numbers. In short, it tells us how much better a player performed to players with comparable usage; thus, I think it makes sense to call it Receiving over Expected.

As for Rushing Yards, it is what it says on the tin, standing in for a variety of extremely similar rushing metrics. We aren’t going to be using it in our analysis—the early/late-round split makes things messy—but it’s still informative. The takeaway is, if an early-round prospect has rushing ability, it bodes well, speaking to their athletic prowess. If a late-round player runs a lot in college, however, it’s an ill omen, implying that it’s their whole shtick, and not just an auxiliary part of their game.

Beyond success: Looking for stars

One thing to note when looking at our WR data is that our metrics are quite adept at separating elite prospects from those who are merely good. They’re meaningfully better at this, in fact, than they are at determining which players will surpass the Shakir Line, in essence putting the cart before the horse.

Thus, while our metrics perform well across all talent levels, they mostly excel at sniffing out star potential among top prospects. To sort out the starters from the scrubs, we use nfl.com’s Scout Grades, which are doled out by analyst Lance Zierlein. Their utility in finding starters may be because elite prospects really stand out: Travis Hunter, for example, earns a grade of 6.89, when most receivers sit in the 6.1-6.3 range.

The difficulty then becomes sorting out which stats truly offer something unique, and which are double-counting. Targeted QBR is a good example of this; its correlation with Zierlein’s grades is .52, far higher than our other metrics. The intuition is simple: top talents go to top schools with good quarterbacks, and thus by using QB Rating, we’re introducing a whole host of additional factors into the mix.

This doesn’t make it a bad metric, though; it’s still different enough from our scouting grades that it’s still worth including. Instead, we’re going to look at two slices of our data, one that utilizes Targeted QBR, and another using our aDOT-Adjusted Caught %. This will hopefully give us a more holistic view of the 2025 receiver class.

The 2025 class, by QBR

Here we see the 2025 receivers currently considered “draftable” per current expected dynasty ADP. The issues with Targeted QB Rating return here, in that players can only do so much to mitigate QB quality. Travis Hunter would likely still be excellent if Shedeur Sanders weren’t tossing him the pigskin, for example, while Tetairoa McMillan may have been disproportionately hurt by poor QB play.

So how do we solve these issues? Let’s return to RoE, which adjust a player’s receiving production relative to players with similar depth-of-target and slot-rate profiles. For example, Xavier Restrepo looks elite by Targeted QBR, but his slot rate of 65% and aDOT of 6.5 yards tell a different story. In short, he was just catching layups from Cam Ward, and his poor RoE suggests he actually performed worse than you’d expect.

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There’s also a trio of mid-round favorites in Jaylin Noel, Jayden Higgins and Kyle Williams that’s worth discussing. The three have some of the worst Targeted QBR numbers on the graph above, but the Iowa St. duo of Noel & Higgins feel disproportionately harmed by QB Rocco Becht’s sub-60% completion percentage.

Williams can put less blame on signal-caller John Mateer, however, who has a real chance to go pro as well. To be sure, all of our other stats back up the Williams hype train, but if there’s any reason to temper expectations, it’s his Targeted QBR.

The 2025 class, by Caught %

We now turn to aDOT-Adjusted Caught % for a different perspective. Tre Harris immediately stands out for his insane catching ability, though there’s a chance his numbers are goosed up by Lane Kiffin’s scheme. The Ole Miss offense helps its receivers get wide open, while also requiring they run a limited route tree.

Harris did have the most contested targets in our sample, however, which lends credibility to his catch rate. Instead, be wary of Jalen Royals, who saw just a third of Harris’ contested targets. I’m still high on Royals at his current price, but he may have benefitted more from scheme than Harris, while also facing worse competition.

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At the opposite end, we have Elic Ayomanor, who saw incredibly shaky QB play at Stanford. RoE implies his relative production was good, given the type of routes he was asked to run, but his Caught% is still quite poor, even if we account for target depth.

Matthew Golden and Emeka Egbuka are two other interesting cases: both look great through the lens of Targeted QBR, yet seem worse by depth-adjusted catch rate. The differentiating factor here is RoE, which marks Egbuka as an above-rate slot player, while putting Golden’s outside receiver bona fides into question. Essentially, if Golden’s sub-4.3 speed is a mirage, then it’s hard to make the analytical case for him.

Athleticism & Production Thresholds

Finally, we examine which players clear the Next Gen Stats (NGS) Score bar. The overall score is broken out into two components: Athleticism and Production. While a low score in either is less prohibitive for receivers than it is for RB’s, a rough threshold of 66 still functions as an inflection point where scores become problematic.

This leaves us with three flagged players: Isaac TeSlaa, Tez Johnson and Xavier Restrepo. To say Johnson and Restrepo don’t clear the athletic bar is greatly underselling it, given both would be extreme outliers if they made it in the league. While Johnson’s a solid performer in all our important metrics, there just isn’t much precedent for such an undersized player in the NFL.

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Ironically, the case for Restrepo feels much easier: while Johnson is buried under a stacked Tampa Bay depth chart, there’s real reason to believe Restrepo could break camp as a safety valve for former teammate Cam Ward. The margin for error, in short, feels smaller for Johnson, if only because it’s hard to imagine him offering any additional utility beyond just catching the ball.

The opposite is true of TeSlaa, whose athleticism and draft-pick pedigree almost guarantees he’ll see the field in some capacity. Notably, our other metrics don’t rate him as the absolute disaster his NGS Production Score implies, but the sample size is so small it requires an immense degree of projection.

Summary

In short, we want receivers who:

• Make their QB’s right (Targeted QBR)

• Catch the damn ball (Catch%)

• Outperform similar players (RoE)

• Pass the eye test (Scout Grade)

Ultimately, predicting receiver success is far harder than it is for RB’s. When we get it right, however, it’s easier to get it really right. If we can guess if a player’s starting-caliber, then we’re well-equipped to figure out if they’re going to be a star, too.

A second difficulty is that it’s hard to separate receiver play from their QB. Granted, offensive lines have a disproportionate impact on RB play, too, yet backs are still consistently put in situations where they get to prove it one-on-one versus defenders. Receivers, by comparison, can only do so much to overcome bad QB play, with even the best players unable to reel in every scattershot pass lobbed their way.

The best we can do, then, is start with catchall metrics, like our Scout Grade, then work from there. Though calling out scout evaluations in particular seems odd, these grades are slightly better than similar comprehensive metrics—such as NGS Score, or where a player was picked in the draft—at determining whether a player is starting caliber. We can gas up Tez Johnson all we want, for example, but given his subpar Scout Grade of a flat 6, it’s more likely than not he won’t make it in the league.

This concludes our deep dive into the 2025 receiver class. I’ll be back soon revisiting Liquidity Index, another idea pulled from a previous post. In the meantime, feel free to subscribe for more content, and follow me on Bluesky (capn-collins.bsky.social) and Twitter (capn_cc) if you’re so inclined.

I’ve spent much of the past few months digging through years of NFL Draft & fantasy football data, trying to uncover useful trends. In the process, I’ve found some success comparing the league’s Next Gen Stats (NGS) data with historical dynasty-league average draft position (ADP), examining both running backs and receivers in earlier posts.

What’s been a harder nut to crack, however, is predicting which late-round dynasty picks will succeed. In fact, it’s pretty much the entire challenge, given how high the hit rate is for first-rounders. Even if you whiff on your first-round pick, they’ll more often than not provide value by virtue of actually seeing the field.

Conversely, players picked after the first-round of dynasty drafts are significantly harder to peg. A metric that may be great at predicting success for second-rounders, for example, may flop at predicting third-rounders, underscoring just how noisy prospect evaluation can be. Yet this is something for later in the article; first we much define what prospect success actually looks like.

The Mitchell Line

Enter the Mitchell Line, a semi-arbitrary cutoff I’ve devised to judge whether a running back’s first three years in the league were—at least in fantasy terms—a success. In short, we’re looking for starters, or players who cleared 250 PPR points across their first three years in the league.

It’s named after former 49ers running back Elijah Mitchell, who overcame his late-round pedigree to unseat a player picked ahead of him in the same draft. Granted, he was meaningfully better than what I’d consider a typical starter-level player, with his career being derailed by injuries and the 49ers’ addition of Christian McCaffrey. Still, the line's more about the guys who don’t clear it than anything.

Dynasty managers were likely fine getting Jerome Ford and Khalil Herbert in the fourth round, for example. Those who picked Benny Snell or Mark Walton, however, were less happy, despite getting spurts of production and spot-start upside. They didn’t get a startable player, which is the point the Mitchell line tries to convey: you’re here to draft starters, not fringe backs who’ll be off your roster in a year.

The story told by the graph above—which compares backs above and below the Mitchell line by athleticism—is more about how un-athletic the below-line backs are than anything. The worst back above it is Kyren Williams in dark blue, with an NGS Athleticism Score (more info here) of 60, the only blue dot above the line. Despite boasting some strong athletes, players below the line look more like Williams than they do Mitchell and his blindingly yellow 99 athletic score (out of 100), a mix of ho-hum purples and blues that suggest avoiding subpar athletes in the back half of dynasty drafts.

Comparing RB Attributes by Round

While interesting enough, our athleticism analysis only tells a small part of the story, given we were only looking at dynasty picks 30-plus. This was deliberate, however, since that slice encompasses one of our biggest blind spots: fourth-round picks. There’s little difference, for example, in the NGS Score (rightmost group) of successful and unsuccessful fourth-round prospects.

It’s also harder to differentiate good from bad first-rounders, though their high hit rate makes that issue somewhat moot. Ultimately, there aren’t many one-size-fits-all metrics that help distinguish successful prospects from busts across all rounds of dynasty drafts. The best angle, in my opinion, is finding criteria like the ones above (many sourced from Peter Howdy’s excellent spreadsheet), where successful backs hold an advantage in across all four rounds of dynasty drafts.

Going beyond success & looking for stars

Beyond using round-based ratios, I also looked at correlations between our metrics and whether a player cleared the Mitchell line (the blue bars above). I also wanted to better suss out stars from just-OK players, so for each indicator, I compared its correlation with early-career production for players already above the Mitchell line (red bars). The idea here is that if we can find an attribute that’s specifically predictive for players we already think are going to succeed, we might be able to go a step beyond and find not just starters, but stars.

For example, Overall NGS Score (leftmost bars) was the feature that best predicted whether a player would surpass the Mitchell line, boasting a correlation coefficient of .48. When we looked solely at players above that line, the correlation with total points scored was still a comparatively robust .49. Bottom line: we want to find metrics like NGS Score, which not only predict player success, but help explain just how successful a good prospect will be.

2025 RB class breakdown

OK, great, you’re probably thinking; but what about the actual prospects? While the 2025 RB class looks to be one of the better ones in recent memory, there’s still only one elite-level prospect in Ashton Jeanty. It is worth noting that, in terms of NGS score, most drafts are lucky to have one prospect at Jeanty’s level, with many having only a single player grading out above 80. Here, five players clear that threshold, with others close behind.

What I find under-discussed about the 2025 class, however, is how few backs are the complete package. In a way, the league’s draft approach bears this out, with many teams selecting backs to compliment their existing backs’ skillsets. Take, for example, the Giants selecting Cam Skattebo as a steady-hand counterpart to the explosive Tyrone Tracy. Also see the Browns, who double-dipped and got both flavors in the sturdy Quinshon Judkins and the more dynamic Dylan Sampson (who are, quite fittingly, neighbors on the chart above).

Upper-flight talents

All this is to say, while I’d usually advocate for doing RB analysis independent of landing spot, I think that this year, team-fit concerns matter a lot more than usual. Take, for example, RJ Harvey, a player I’d have happily acquired for a third-round pick, which was his going rate before NFL Draft. By our metrics, he’s an upper-quadrant player, a productive receiving threat with solid athletic traits.

Now that Harvey’s likely to command a first-round selection, though, things have changed. Much of his analytical upside is already baked into that price, and his shortcomings now stand out more sharply. Harvey is, as Nate Tice described Oregon QB Dillon Gabriel, “old and short,” which raises questions about his ability to sustain a full workload

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Many other high-profile players in the chart above, like Harvey, offer little surplus value at their current ADP: you don’t need me to tell you TreyVeon Henderson is going to be good, for example. The edge instead comes from guys like Bhayshul Tuten, an exceptional receiver with explosive traits. He’s a great bet to succeed in Liam Coen’s offense, who made a three-headed monster work in Tampa last year.

Tuten is starting to run into the same issue as Harvey, however, offering less value the earlier his ADP gets. Consider, then, DJ Giddens, whose receiving upside should let him see the field early, and scores comparably to Tuten in most of our metrics. Perhaps an even better value bet is Ollie Gordon: while Gordon’s metrics are inflated by an outlier 2023 season, he boasts a degree of size that Miami sorely lacked.

Sifting through the rest

As for the rest of the 2025 class? Decidedly “meh” by comparison. More importantly, these backs all rank similarly in age-adjusted PPR per Game (our y-axis here), which is an especially strong predictor of success for third-round prospects. Though many of these guys are actually fourth-rounders, the stat’s still useful—as is Average Yards per Route Run (our new x-axis)—in determining prospect quality.

Since most players above are ranked similarly, we should first hone in on outliers. Jordan James stands out for his particularly poor age-adjusted production, though it’s worth noting he had to share a backfield with Bucky Irving at Oregon. Even if he wins Kyle Shanahan’s favor, however, he’s still decently unlikely to eclipse our 250-PPR benchmark, with his middling receiving profile likely capping his upside.

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Maybe, then, we should turn to Jaydon Blue, the best receiving back in the chart above, who also boasts blazing speed. Woody Marks stands out, too, with an enticing blend of pedigree (fourth-round NFL Draft pick) and receiving ability that should give him a chance to climb up a weak Texans depth chart. The difference, of course, is that Blue’s continued rise up draft boards makes him less of a value pick, while it’s hard to see Marks’ ADP changing further.

Simple thresholds for ‘25 RBs

Since differentiating between these late-round backs is so difficult, let’s once again turn to NGS Athleticism & Production Scores, using a score of 65 as a rough cutoff for success. Only twos backs fail to clear our production threshold: Trevor Etienne and Woody Marks. This is a considerable red flag; as I’ve covered in a previous post, Production Score is arguably our best metric at predicting RB success.

Less disconcerting—though still notable—are our three athletic outliers, Devin Neal, Kaleb Johnson and Ollie Gordon, whom we covered earlier. If anything, Neal’s low athleticism only serves to highlight how strong his Production Score (seventh-best in the class) is relative to other backs with a third-round ADP. That makes him an interesting sleeper, especially with Alvin Kamara getting up there in years.

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Johnson’s a slightly more interesting case, if only because his poor combine results overshadow his superb tracking numbers. If he is, in actuality, a stronger athlete than his testing shows, then the only thing holding him back would be his weak receiving profile. While there are still enough red flags to make me wary, the further he falls down draft boards, the more willing I am to bet on his three-down-back potential.

It all boils down to opportunity cost, which is a good lens to reexamine our low-production outliers through. Etienne in the third, for example, feels slightly rich, given how little he’s done in college. By contrast, Woody Marks is a worthy dart-throw in the fourth due to his receiving ability and Joe Mixon’s advanced age, even if it means engaging in Tobias Fünke logic regarding his suspect Production Score.

Summary

Let’s codify what good RB prospects should do:

• Clear the bar for both production & athleticism

• Be more than their best season (age-adjusted PPR/G)

• Be holistically good (Overall NGS)

• Be competent receivers (Avg. Y/RR)

There are a couple more concepts that fit less cleanly into bullet points. First, backs should be picked appropriately based on NFL Draft pedigree. Landing spot is a great for differentiating players of comparable pedigree, but that doesn’t mean you should select Isaiah Spiller (the 123rd overall pick!) in the early second just because of a good landing spot.

Second, age and consistency matters when it comes to production. Any player that had a single breakout season—or played against weaker competition—should be met with scrutiny. This is doubly true if they did so as an older prospect, for both wear-and-tear and credibility reasons; an older dude with more experience will obviously be better equipped to mow down freshmen. This is the Cam Skattebo conundrum, one with no easy answers, but at least is enough to arm us with a health dose of skepticism.

Yet if there’s one idea I want everyone reading this to grasp, it’s this: predicting prospect success is still hard. In a lot of senses, it’s meaningfully easier to suss out which RB’s will do well, especially given how highly college production correlates with future success. But it’s still noisy enough that the real game shouldn’t about who you think will be good, but what price you’re willing to pay for a given prospect.

Hopefully, all this has equipped you with enough tools to find some winners in your dynasty league drafts. I once again want to thank the illustrious Peter Howdy, who you can find here on Bluesky. You can similarly find me on Twitter (capn_cc) and Bluesky, and feel free to subscribe to this Substack (free!) for more in-depth fantasy content.

There’s an old adage about driving cars off lots I’m sure you’ve heard, but it sounds better coming from legendary TV wiseass Bertram Gilfoyle. The minute you convert a liquid asset like cash into something like a car, it immediately starts depreciating in value. There’s a reason that collectors don’t bandy about their Mickey Mantle rookie cards in public, for example, or actually play games with their copies of Black Lotus.

Fantasy Football—and real life sports, for that matter—presents a similar conundrum. NFL podcasters talk a lot about a general manager “clock”, that once you take a QB with a high draft pick, you’ve now imposed expectations on yourself that didn’t exist before. A similar conundrum exists for fantasy GM’s, who, by converting picks into actual players, are now tied to them, for better or worse.

Every fantasy sports manager faces this challenge, including even the most NBA-pilled Sam Hinkie wannabe who’s loath to actually make their picks, preferring to punt them away into future years. At some point, even that guy has to choose who he wants on his roster, like it or not.

Many prospects are losing bets

The problem is that fantasy football assets are a lot like cars, except worse: whereas most rookies hang around for at least three years on pro rosters, many fantasy prospects lose a significant portion of their value over the first year alone.

This isn’t universally true, as the graph above shows. The median Tight End and QB prospect increases in KeepTradeCut value over their first year, while the median back or receiver often sinks. Unless noted otherwise, values in this article are from single-QB, no TE premium leagues, comprising the 2023 and 2024 rookie classes.

Zooming out to year three of a player's career, however, things become a bit bleaker. Granted, the chart above is only for 2023 rookies, and the data here largely reflects quirks of that draft class. The TE dip is guys like Dalton Kincaid crashing down to earth, while the risers are Tucker Kraft and Brenton Strange, perhaps confirming the notion that TE’s take a while to develop. The RB falloff also speaks to the weakness of the ‘23 RB class outside of Bijan and Gibbs. It was full of guys with a chance to overtake an aging or questionable starter, yet many never did so.

Asset Stability

In the first bar graph from this post, we saw that many QB’s and TE’s gained value heading into the following year’s draft. Notably, the box-and-whisker plot above also shows how few TE’s decreased in value going into Year 2. The biggest decrease is less than 1,000 points of KeepTradeCut value, suggesting that, at least over the first year of a player’s career, tight ends have a high floor.

Quarterbacks are similarly stable, at most losing 1,500 points of value by Year 2. QB’s also accrue more value: the median QB gains 500 points of value, while the 75th percentile QB adds 1,600 points. Many factors cause this: QB’s tend to improve in Year 2, are given more leeway than other prospects, and the ‘23 and ‘24 classes were strong. In any case, we can decisively claim that good QB prospects are safer assets.

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Comparatively speaking, Running Backs and Wide Receivers have lower floors (about a -2000 point decline). Receivers are slightly less volatile, with a smaller range of outcomes, a better 75th percentile outlook, and a median change of zero. The median RB, by comparison, is the only position to decline in value year-over-year. Overall, RB’s and WR’s are both volatile assets, while receivers have more consistent upside.

Adjusted Liquidity Index (ALI)

All this is useful, but we’re still ignoring why people invest in RB’s and receivers. After all, nobody wants an asset that has a low floor and hard-to-realize upside.

This is where liquidity matters, giving us our Adjusted Liquidity Index (ALI). ALI utilizes additional recent-trade data from the KeepTradeCut trade database. We want to see how frequently year-two players are traded, relative to their value.

You don’t just want to draft guys who are nominally valuable, but guys you can actually move, which matches the book definition of liquidity. This may sell top prospects short, but there are real-life parallels here. You’ll get many offers for Brock Bowers, for example, if you put him on the block, but it’s harder to get satisfactory ones, much like getting book value for a multi-million dollar mansion is easier said than done.

Positional Liquidity

What ALI does successfully capture is positional liquidity. If you compare the top 20 players by ALI and overall value above, you’ll see that RB’s are disproportionately represented in terms of top ALI, while QB’s suffer. Simply put, people are more willing—and able—to trade away second-year RB’s.

The 2024 class did, however, put many valuable QB’s on the map. These guys are, relative to their value, being traded less than you’d expect, though managers wanting to keep Jayden Daniels is understandable. Caleb Williams is also being traded less often than Drake Maye is in recent weeks; it could be that Caleb’s a sunk cost for some owners, while Maye drafters are happy to flip him for a huge profit.

Liquidity Index’s hottest commodities

Even considering the most liquid QB’s, however—like Bo Nix and Michael Penix—they’re still traded less frequently than WR’s or RB’s of equivalent value, barely cracking the top 25 in ALI. The metric is normalized to be out of 100, with our benchmark player being Dolphins RB Jaylen Wright; Tyrone Tracy is only at 148 because he literally broke the scale, so he gets to live in his own weird outlier space, instead of screwing up everybody else’s ALI by keeping him in.

As far as Wright goes, he’s being traded far more often than you’d expect for a mid-value RB that performed poorly as a rookie to be. Then again, this metric is tracking which players managers actually want to flip after their first year in the league. The value in our ALI is finding out what kind of players will be liquid assets a year from now, regardless of what direction their career’s headed.

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The answer to that, quite resoundingly, seems to be receivers and running backs, who dominate our top 10 above. This tracks, given they’re the lifeblood of fantasy football: people generally need more of each than they do other positions. It’d explain the lower trade velocity of QB’s, too, since QB’s are mostly being swapped for each other. Either way, if there’s one lesson to take away from my analysis, it’s this: mid-tier WR’s and RB’s are easier to move than most TE’s and QB’s.

Liquidity Index’s best late-round values

This piece actually began as an exploration of which late-round/UFA dynasty prospects are the best future investments. Though we’ve veered far from that course, what we’ve discovered still maps pretty well to that original problem statement.

My initial hypothesis going into this post was that Tight Ends would be stronger later-round/UFA selections, if only because you’re picking players with real pedigree, who tend to stick on rosters. Though RB’s still lead the pack (there’s that pesky Tracy again), there’s meaningful TE representation here, with many being traded a decent amount, while also boasting above-replacement KeepTradeCut values.

In this, we have the closest thing to a grand thesis for our article. Per the prior chart, you ought to spend mid-round picks on RB’s and WR’s if you want to be able to flip them the following year, regardless of how their value changes. Conversely, you should spend late-round picks/FAAB on RB’s and Tight Ends.

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Summary

This post is already running long, so let’s get down to brass tacks:

• Rookie QB’s and Tight Ends are stable, high-floor investments

• RB and Receiver Prospects are more likely to lose value a year out

• Mid-round RB’s & WR’s are highly liquid assets, even if they lose value

• RB’s & TE’s = most consistently liquid late-round bets

All this confirms some core fantasy truisms, yet with some interesting wrinkles. You should take a bunch of mid-round dart throws on receiver and running back, for example, not because they have high hit rates, but because teams need multiple starters at each position. This makes them a meaningfully more liquid asset than other positions, which matters a lot. All the nominal value your rookie QB accrued over a year is worth little if nobody’s willing to meet the asking price, for example.

The bar is slightly lower for late-round dynasty picks and UFA’s, where if you can scrounge any value from them, it’s a win. Tight ends are great bets here, since they are, in so many words, real prospects. While draft capital is only mildly helpful for choosing late-round dynasty picks—many successful RB’s defy their NFL Draft pick, for example—there seems to be a real signal when it comes to tight ends. Though we only have trade data for ‘24 prospects, late-round dynasty TE’s would look even better if we included the very deep ‘23 class, which saw huge upticks in value for Brenton Strange and Tucker Kraft—both top-80 picks—the following year.

Hopefully this gives you the tools to squeeze that tasty extra value out of your dynasty football draft this year. I plan to continue my write-ups on specific positions after the NFL Draft; until then, check out my pieces on which of this year’s receivers and running backs are overrated, and which might be sneaky value picks.

Earlier this week, I explored whether disagreements between average draft position (ADP) and analytical metrics, like Next Gen Stats’ (NGS) production score, can help explain why running back prospects succeed or fail. Today, I’m examining receivers through a similar lens.

Above is a chart (click to enhance) showing this difference between the analytical model (NGS Score) and the fantasy community (ADP). Notably, I’m using overall NGS Score, and not just the production-based score like I did for running backs. The Combine IQ website claims this production score is better. Yet their overall model—which includes other factors like athleticism—showed a stronger correlation with points scored in my sample (2016-2022 draft classes).

The takeaway, like with running backs, is that ADP is better at predicting star-level success for wide receivers. While many ADP favorites became stars (upper right), NGS picks (left) give us fewer success stories. The dark blue dots represent the 2025 class, affirming the idea that many receivers in the 2025 class have lower ceilings.

When stats are bullish, but the community isn’t

Taking players where the NGS model was higher than consensus, a pattern starts to emerge. Many players above have strong 40 times, perhaps giving us insight into how NGS Score works. The Combine IQ site says 40 time is a “key metric”, meaning it could be more predictive than NGS Score itself (which the site currently ranks fourth).

This may be telling for 2025 prospects Jalen Royals and Jaylin Noel, whom the model likes more than the fantasy community. Both could see their profiles rise by the NFL draft, but there are also similarities to past busts seen above. While they aren’t pure burners like Andy Isabella or Tylan Wallace, speed’s still a big part of their appeal.

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There are also questions about how Noel and Royals’ skillsets translate to the NFL. Noel could be a vertical-slot a la Christian Kirk, and is deceptively productive at the catch point. Royals, too, is notably quick, garnering Jayden Reed comps. While those are useful archetypes, each has flaws, too. Analysts have raised physicality concerns for both Noel and Royals, though some have also noted Noel plays well above his size.

What to do with unproductive receivers?

We now bring back NGS production score to examine unproductive college players who were still relevant picks. By sorting to highlight the worst-scoring players, we do miss guys like DK Metcalf and George Pickens, though their success in spite of poor production is more exception than rule. Ultimately, players like Pickens and Metcalf underscore how often it takes a special athlete to overcome poor college production.

But what of Matthew Golden, 18th this on this list, who’s skyrocketing up boards due to his blazing 40 time? It’s harder to parse how he’ll turn out, given how varied the situations were for the busts ahead of him. A common thread is injury: Mims, Hamler, and Edwards saw collegiate injury issues persist into the NFL. Golden might fit in this bucket, having missed substantial time in his first two college seasons.

Notably, his college production was also much better than the other names on this list. It’s worth noting his early-entrant status could help excuse away his low production, too. The most likely case is his NFL production matches his profile, one of a good secondary option to a strong #1 receiver (a common refrain with this year’s class).

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The outlook isn’t so sunny for his teammate Isaiah Bond, however, with the worst production of the 2025 class. Now infamous for bragging he’d run his 40-yard dash in 4.2 seconds—and falling far short of that—Bond’s athletic profile accordingly suffers, with NGS grading him as a good, but not exceptional athlete. Add in character concerns, and the parallels between him and the biggest low-production busts are extremely disconcerting. He checks nearly every box, really, by being a speed-first player with personality questions and a worrisome injury history.

The 2025 class

Above are the three-year outlooks for the 2025 receiver class. Notably, the NGS model and ADP are generally in accord on this class, and will likely agree even more the closer we get to draft time. Expect Xavier Restrepo and Isaiah Bond, for example, to drop further down fantasy boards after the draft, based on underwhelming testing.

Again, this matches the impression of this class, with few players exhibiting elite potential. There are real questions about how these guys’ skillsets will map to the NFL, a far more unforgiving environment than college.

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Beyond players already discussed, Tre Harris and Jayden Higgins stand out. The model likes Higgins more than consensus, while it’s relatively lower on Harris. Even still, it’s easy to imagine either of these players having their name called first on draft night, underscoring just how closely grouped this class is in talent.

Finally, it’s worth shouting out Travis Hunter, whom the NGS model adores. Per NGS, he’s the clear top receiving prospect, a view shared by Reception Perception’s Matt Harmon, who sees Hunter as a real WR1 in a field of likely WR2’s. It’s also worth noting that the comprehensive NGS model is sunnier on Matthew Golden than their production-only model, underscoring the potential value of his athleticism.

Summary

Overall, while receiver success is harder to predict than with running backs, there are still a few lessons to be learned:

• If you’re picking an unproductive college player, he’d better be an athletic freak

• Speed-only receivers are the draft’s biggest pitfall

• Injury history matters, and shouldn’t be used to excuse poor production

Like with running backs, age-old fantasy truisms still hold as well. Opportunity matters, as some of the big wins for the NGS model demonstrate, such as the case of Dede Westbrook. Yet Westbrook is really an example of how deceptively hard opportunity is to quantify: nobody could’ve predicted Allen Hurns falling off a cliff, along with Allen Robinson tearing his ACL during Westbrook’s rookie year.

Yet we’re still just barely scratching the surface. I hope to provide further insights in upcoming post about what thresholds receivers must clear to make it in the league. Be sure to check out my past post on Running Backs, and as always, thanks for reading!

Fantasy sports, like any kind of game, are all about being just a bit better—or smarter—than the next guy. That fantasy football is an incredibly stupid, luck-driven endeavor makes this doubly difficult, especially as tout-driven analysis from the world of gambling bleeds into the fantasy sphere. Like most things in today’s uber-online age, where people are increasingly data-savvy, the advantage lies in the margins.

But how do we leverage this data, already pored over by thousands of heavily invested fantasy-football addicts? Well, in the case of dynasty fantasy football, there are areas where the market’s out of step with the data. By analyzing dynasty-league average draft position (ADP), I suspected that the fantasy community overrated certain players based on potential, when both scouts and analytics raised red flags.

Luckily, Next Gen Stats (NGS) just released a robust, easy-to-access trove of data: their Combine IQ database. It’s still a bit of a black box, with the Combine IQ site hinting that, for some positions, there are hidden factors even more predictive than NGS score. What the site does give us, however, is plenty to work with.

Analyzing Running Backs

It’s relatively easy, for example, to predict whether a college running back will thrive in the NFL. This is because his college production is, per NGS, the top predictor of NFL success. Intuitively, this tracks, since NFL teams usually draft a guy based on potential scheme fit and need. Thus, even if a productive college back has question marks, he’s likely headed to a situation conducive to sustained success.

Since NGS data is publicly available, however, what unique advantage can we gain? This is where Dynasty ADP helps: if we subtract ADP from a running back’s college production, we get cases where the fantasy community and analytics disagreed. The above graph (click to enhance) shows points RB’s scored—or, for 2025 RB’s (dark blue dots), are projected to score—over the first three years of their career.

The further you get from the center, the bigger the disagreement: analytics are more bullish on guys to the left, while the right side contains less-productive college backs who still got picked early. It’s a bit noisy, but the main takeaway is clear: when the two disagree, the fantasy community is often wiser than the NGS model.

Above are the biggest disagreements from 2016 to 2022, with 2025 backs thrown in as well. Kansas State RB DJ Giddens—expected to be the 14th running back taken in dynasty drafts—immediately stands out. Giddens may rise after the NFL draft, since I’m currently using Keeptradecut rankings to predict ‘25 ADP. Yet his possible future as a committee back—despite testing well at the combine—may be missed by the NGS algorithm, which only sees a very productive college back.

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If guys like Giddens—whom analytics likes, but the fantasy community is sour on—often fail, then what about the converse? High picks who analytics hates often succeed, though it’s still a useful red flag. See Zamir White—a combine warrior drafted by a bad Raiders team—and Ty Davis-Price, one of many RB’s the 49ers over-drafted. Clyde Edwards-Helaire, whom analytics strongly disliked, is another cautionary tale (first in ADP, but sixth in NGS). While he produced some value, ask anybody who picked him over Jonathan Taylor if they’re happy with their decision.

The 2025 RB Class

Above are, more or less, are the “draftable” 2025 backs. Right away, we have two backs that pop in Quinshon Judkins and Trevor Etienne, both ranking as top-20 disagreements in our sample. Etienne’s easy to explain, with his last name carrying his ADP. After the NFL Draft later this month, there’s a decent chance his stock drops.

Judkins is trickier, since other models like ESPN’s BackCAST are also low on him. BackCAST penalizes players sharing backfields, since a truly talented back should earn most of the carries. Still, both Judkins and teammate TreVeyon Henderson could succeed: BackCast is still high on Judkins, his lower rank more about the quality of this RB class. My own projections, too, see Judkins having star potential.

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Lastly, we have RJ Harvey, an divisive back out of UCF who, as the chart shows, the fantasy community is lower on than the NGS model. Some experts see a committee back, plagued by subpar vision. Durability concerns abound, too; while his potentially explosive profile is like catnip to fantasy players, he could be another cautionary tale where intangible, hard-to-codify football knowledge trumps deceptive college stats.

Summary

If there’s one thing I want to make clear, I still think the NGS production-based model is really good. This was more about trying to find blind spots in fantasy football analysis, which I think we did. At the very least, we’ve covered some new angles to consider when you’re handing in the proverbial card in your dynasty draft.

Do I think the findings here should be followed religiously? Probably not; the best strategies are still the obvious ones, like trading back, or taking players who fall. Yet it can, hopefully, help you sleep better at night, letting you take some late-round guys off your board, or assuaging concerns about your early-round picks flopping.

Anyway, I think this is more than enough ink spilled on running backs. Check back in the coming weeks for a similar post on the 2025 Wide Receiver class, as well as a deeper breakdown of what stats-based thresholds prospects need to clear.