Finding 2022's Fantasy Football League-Winning Wide Receivers Through Season Simulations
We, as fantasy football managers, can be heavily swayed by anecdotal evidence and personal experience, and that can cause us to ignore larger trends that will help us find long-term success.
Maybe you drafted Josh Allen early in your drafts last year and are convinced that an early-round quarterback is an absolute must to win your league. Maybe you selected Christian McCaffrey and swore off of early-round running backs and would rather play the late-round and waiver-wire guessing game for your backs (which, by the way, is harder to do than you might think).
And while median fantasy football projections help get your bearings as we enter draft season, they don't do a good job explaining the range-of-outcome reality that exists with fantasy prognostication.
Fantasy football is volatile. Injuries happen. Players are less (or more) efficient than expected. This means that -- with, say, a reasonable discrepancy in touchdown rate from expectation -- players can easily fall from a QB6 projection to a QB13 output, and we won't think twice about it.
But in order to put some math behind this and help uncover the actual odds certain players return us with top-tier fantasy results, I went ahead and simulated the NFL season 10,000 times to see which wide receivers (and quarterbacks, running backs, and tight ends) are most likely to lead the league in fantasy points. This can help us uncover who has tangible, predictable league-winning upside for the 2022 season.
The Process and Expectations
It's very easy to get stuck in neutral when constructing fantasy football projections. Yes, we want to be accurate, but trying to find a rigid answer to a rate stat isn't going to help us very much.
It'd help if we could trust those stats to be accurate, but they won't necessarily be.
A great example is San Francisco 49ers receiver Deebo Samuel. Last year, Samuel averaged 18.3 yards per catch, a top-four rate since 2012 among receivers with at least 100 targets. We can't expect that outlier rate to continue, so we need to figure out how to project him. Assigning him a regressed yards-per-catch rate is one way to go about it, but why not just embrace the true range of outcomes for that stat -- and all his others?
Over the years of projecting fantasy stats, I have realized there is much more value in painting with broader strokes for an exercise such as this.
To get started with this project, I went back and studied historical deviations and ranges of outcomes for players based on numberFire's preseason fantasy football projections. This helped me tailor projections for the simulations.
From there, I accounted for historical injury rates among positions, replacements for injuries when they occur in the simulations, and -- of course -- actual fantasy point variance because, even if a player plays all 17 games, he is unlikely to score his exact projection.
It's important that the simulation results resemble historical precedent. Otherwise, we're off base with either our baseline projections or our standard deviations.
Here is how receivers -- bucketed by average draft position (a proxy for initial fantasy rankings) -- have fared in meeting certain thresholds.
| ADP Bucket | Leader% | Top-5% | Top-12% | Top-24% |
|---|---|---|---|---|
| WR1 to WR12 | 5.8% | 25.0% | 50.0% | 65.8% |
| WR13 to WR24 | 1.8% | 11.0% | 26.6% | 53.2% |
| WR25 to WR36 | 0.8% | 4.8% | 11.9% | 32.5% |
| WR37+ | 0.0% | 0.3% | 2.5% | 9.2% |
What this is showing, for example, is that if you took all wideouts with a top-12 average draft position since 2012, 5.8% of them finished as the overall WR1, and 50.0% of them were top-12 performers.
Wide receiver is not a completely random position, and when the elite picks play the majority of the season, they generally finish well. The things that really alter finishing positions are injuries and, generally, touchdown variance. At a bunched-up position like this one, it doesn't take much to fall from the WR10 to the WR20 based on a handful of unrealized touchdown chances.
If our math (i.e. baseline projections and standard deviations) is accurate, the simulations should reflect these in a general sense.
If the model (or our instincts) implies that the WR5 this year has a 90% chance to be a top-24 receiver, it's not going to reflect reality.
Simulation Results and Analysis
Here are the results of the 10,000 season simulations and each wide receiver's average draft position in FanDuel's best ball fantasy football formats.
A caveat here is that a lot of these are going to feel "wrong" across positions. It feels wrong to think that there's a 50% chance that stud receiver won't even be in the top 24 by season's end, but based on historical trends -- and the mathematics behind the range of outcomes -- it's accurate. Some of them will get injured, and others will overperform.
The goal is to project 2022 not as the flowery, flawless preseason we all hope to see but as the utter mess that it always winds up being.
| Wide Receiver | FanDuel Best Ball ADP |
Leader % |
Top-5 % |
Top-12 % |
Top 24 % |
|---|---|---|---|---|---|
| Cooper Kupp | 1 | 17.9% | 47.1% | 67.8% | 83.0% |
| Justin Jefferson | 2 | 11.0% | 35.2% | 55.7% | 74.4% |
| Stefon Diggs | 5 | 8.6% | 32.5% | 54.7% | 73.9% |
| Davante Adams | 4 | 8.6% | 30.7% | 51.9% | 71.9% |
| Ja'Marr Chase | 3 | 7.7% | 30.1% | 51.6% | 71.6% |
| CeeDee Lamb | 7 | 6.6% | 26.3% | 47.7% | 68.8% |
| Deebo Samuel | 6 | 6.6% | 26.0% | 47.8% | 69.1% |
| Mike Evans | 9 | 3.6% | 18.5% | 38.4% | 60.7% |
| Tyreek Hill | 8 | 3.0% | 17.1% | 37.2% | 60.3% |
| Keenan Allen | 10 | 1.8% | 11.3% | 28.7% | 51.8% |
| D.J. Moore | 17 | 1.7% | 10.8% | 27.3% | 49.8% |
| Michael Pittman Jr. | 11 | 1.7% | 11.1% | 28.2% | 50.1% |
| Tee Higgins | 14 | 1.6% | 11.3% | 28.3% | 51.5% |
| A.J. Brown | 12 | 1.6% | 12.9% | 31.3% | 53.6% |
| Diontae Johnson | 30 | 1.4% | 10.9% | 27.2% | 49.3% |
| Terry McLaurin | 16 | 1.3% | 9.3% | 25.2% | 48.0% |
| Jaylen Waddle | 19 | 1.2% | 9.0% | 25.2% | 47.2% |
| Courtland Sutton | 15 | 1.1% | 9.9% | 26.0% | 48.2% |
| Michael Thomas | 21 | 1.1% | 9.3% | 24.6% | 46.0% |
| D.K. Metcalf | 18 | 1.1% | 9.5% | 25.3% | 46.4% |
| Brandin Cooks | 26 | 1.0% | 9.0% | 24.3% | 47.2% |
| Mike Williams | 13 | 1.0% | 9.4% | 25.3% | 47.8% |
| Allen Robinson | 24 | 0.9% | 7.7% | 21.7% | 44.2% |
| Jerry Jeudy | 31 | 0.8% | 8.7% | 23.5% | 46.0% |
| Chris Godwin | 25 | 0.8% | 6.6% | 19.7% | 41.3% |
| Rashod Bateman | 33 | 0.7% | 6.7% | 20.6% | 41.7% |
| Marquise Brown | 29 | 0.6% | 6.2% | 19.3% | 39.1% |
| Tyler Lockett | 43 | 0.6% | 4.5% | 16.2% | 34.8% |
| Gabriel Davis | 22 | 0.5% | 4.3% | 15.9% | 35.6% |
| Darnell Mooney | 34 | 0.5% | 5.6% | 17.8% | 37.8% |
| Amari Cooper | 35 | 0.5% | 5.6% | 17.2% | 37.8% |
| Amon-Ra St. Brown | 32 | 0.4% | 4.7% | 15.9% | 35.7% |
| DeVonta Smith | 44 | 0.3% | 2.9% | 10.6% | 29.3% |
| Robert Woods | 48 | 0.3% | 2.9% | 11.8% | 29.9% |
| Adam Thielen | 27 | 0.2% | 3.3% | 11.8% | 31.0% |
| Elijah Moore | 38 | 0.2% | 3.2% | 11.5% | 31.7% |
| Chase Claypool | 51 | 0.2% | 2.4% | 9.9% | 26.9% |
| Hunter Renfrow | 36 | 0.2% | 2.8% | 11.5% | 29.4% |
| Drake London | 39 | 0.2% | 2.6% | 10.1% | 26.6% |
| JuJu Smith-Schuster | 20 | 0.1% | 2.8% | 11.1% | 29.0% |
| Treylon Burks | 49 | 0.1% | 1.4% | 7.0% | 20.8% |
| Kadarius Toney | 45 | 0.1% | 1.5% | 8.5% | 23.6% |
| Jalen Tolbert | 57 | 0.1% | 0.8% | 4.9% | 16.7% |
| DeAndre Hopkins | 23 | 0.1% | 0.7% | 4.7% | 16.8% |
| Julio Jones | 42 | 0.1% | 1.5% | 7.3% | 22.6% |
| Christian Kirk | 40 | 0.1% | 1.5% | 8.5% | 24.1% |
| Michael Gallup | 66 | 0.1% | 1.3% | 6.9% | 20.9% |
| Jakobi Meyers | 65 | 0.1% | 0.5% | 3.7% | 14.8% |
| Kenny Golladay | 61 | 0.0% | 0.8% | 5.3% | 17.4% |
| Allen Lazard | 28 | 0.0% | 0.8% | 4.6% | 16.7% |
| DeVante Parker | 55 | 0.0% | 0.7% | 4.4% | 16.8% |
| Tyler Boyd | 50 | 0.0% | 0.6% | 4.1% | 15.4% |
| Brandon Aiyuk | 37 | 0.0% | 1.4% | 6.7% | 21.9% |
| Marquez Valdes-Scantling | 46 | 0.0% | 0.6% | 3.3% | 14.0% |
| Garrett Wilson | 56 | 0.0% | 0.4% | 2.9% | 12.8% |
Unsurprisingly, Cooper Kupp jumps way ahead of the field in odds to lead the position in fantasy points (17.9%), given his monstrous projection (303.5 half-PPR points) with nobody else above 270.0. That being said, the sims still show an 83.0% chance he's a top-24 receiver, leaving 17.0% of the simulated seasons where he finished outside that mark. Yes, he's the WR1, but among wideouts over the past 10 years with a top-three ADP, 83.3% were top-24 receivers. Those that weren't averaged 9.6 games.
Justin Jefferson (11.0%) is the other player with a double-digit chance to lead the position in fantasy points. He'd also fit in that top-three anecdote from above, but the simulations aren't bucketing players based on ADP but rather numberFire's actual projections.
Stefon Diggs (8.6%) and Davante Adams (8.6%) lead the third tier over Ja'Marr Chase (7.7%), but those five in total are the top-five receivers by average draft position.
In fact, the top-10 receivers by ADP are the 10 players to have the top-10 rates of WR1 seasons. Again, the randomness at receiver stems not from projectability but rather injuries and then outlier efficiency rates from there.
Notably, the sims (based on numberFire's fantasy football projections) are high on D.J. Moore (1.7%) relative to his ADP (WR17). Diontae Johnson (1.4% to be the WR1 and 10.9% to be a top-five receiver) is the WR30 in FanDuel's best ball drafts.
As usual, the receiver position has a tier comprising elite options at the top (roughly five players this year) and then declines gradually from there.
The sheer potential for Kupp, Jefferson, Diggs, Adams, and Chase (as well as CeeDee Lamb (6.6% to be WR1) and Deebo Samuel (also 6.6%) are worth prioritizing as early-round picks over less certain running backs starting in the middle of the first round.
After them, not much separates those projected -- roughly -- between WR13 and WR24 in terms of bankable upside, so maybe after the elite backs and receivers are taken, we can look to the dominant quarterbacks and tight ends.
