Thirty Calls for the 2026 NFL Season

Thirty calls. Five futures leans, ten win-total model numbers, fifteen player-prop projections. That's the whole piece — not a power ranking, not a hype sheet, just the places where the model's read on this season sits far enough from the crowd's to be worth writing down. The through-line is simple: the NFC West is shaping up as the strongest conference division in football, one AFC team is quietly underpriced, and two of the most popular Super Bowl tickets in circulation are ones the model wants no part of.

One honesty note before anything else. The simulation that powers these calls freezes its ratings before the season and never updates them — it cannot see a September injury, a Week 6 hot streak, or a December tailspin. That's the trade-off for using twenty-seven years of results to build the ratings in the first place. The historical base rate for the preseason favorite winning the Super Bowl is 15.4% (every season from 1999 through 2025). When you see Seattle's number below, treat it as a ceiling, not a target. A second note: this is the first season we have publicly journaled these futures leans. There is no graded market-relative record to cite. The record starts today.

The leans here are disagreements with the de-vigged market — the books' cut stripped out so all probabilities sum to 100% — that clear the 3-percentage-point threshold the board requires before anything gets published. The best available price is the number you'd actually see; the edge is model minus market, in percentage points.

1. Seattle Seahawks — VALUE at +1200. The model gives Seattle a 25.1% chance to win the Super Bowl; the de-vigged market prices them at 6.8%, an 18.3-point gap. That is the largest disagreement on the board by a distance. Again: 25.1% is the simulation's upper bound, not a confident point estimate — a strong preseason favorite gets priced hot in a frozen-priors system. The honest read is that Seattle is meaningfully underpriced, not that they're a coin flip to lift the trophy.

2. Denver Broncos — VALUE at +2000. The model puts Denver at 11.3%; the market has them at 4.0%. That 7.3-point gap is the second-largest value signal on the board, and it arrives on a team the win-total board (see Call 6) projects for eleven wins and a 75.4% division-title rate. The price says people haven't connected those dots yet.

3. Houston Texans — VALUE at +2000. Houston's model share is 9.2% against a market-implied 4.3%, a 4.9-point gap. The win-total board projects them for 11.15 wins and a 67.7% shot at the AFC South title. Two AFC teams in the top three value spots is the model's quiet argument that the conference is more balanced than the market believes.

4. Los Angeles Rams — FADE at +550. The market prices the Rams at 13.9% — the most expensive ticket on the board. The model gives them 8.3%, a 5.6-point gap in the other direction. At +550 you are paying a premium the simulation does not think is earned. The player board has two Rams prominently placed, which makes this a harder call to explain at a bar, but the team-level number is the team-level number.

5. Kansas City Chiefs — FADE at +1700. The Chiefs are the most jarring entry: the market still prices them at 4.9% implied, while the model puts them at 0.6%. That 4.3-point gap on a team this famous is the board's bluntest statement about competitive cycles. The price reflects a dynasty; the model reflects a current roster.

Four more teams clear the 3-point threshold as smaller fades: the Baltimore Ravens (model 3.5%, market 6.9%, −3.4pp, best price +1300), the Cincinnati Bengals (0.3% vs 3.6%, −3.3pp, +2200), the Los Angeles Chargers (1.2% vs 4.5%, −3.3pp, +1800), and the Dallas Cowboys (0.2% vs 3.2%, −3.0pp, +2500). All four are journaled fades on the same board — smaller disagreements than the five above, in the same direction.

One important framing note, stated once and not repeated: no market win-total line exists on our feeds as of this writing — every cycle since August 11 has come back empty. What follows are the model's projected win means, not edges measured against a posted number. When lines post, the board will journal them and flag wherever the model's number sits outside a meaningful range. Until then, these are conversation-starters, not actionable disagreements.

The model selected the ten teams whose projected mean sits furthest from any half-number — the calls least likely to flip on a minor re-rating. A projection of exactly eleven wins says something; 10.46 is closer to the coin-flip zone and earns less conviction. Here are calls 6 through 15.

6. Denver Broncos — 11 wins (p10: 9, p90: 13). Denver projects 90% to make the playoffs and 75.4% to win the AFC West. The floor is genuine — even a rough season most likely still ends in double digits — and the ceiling pushes toward thirteen. This is the win-total board's cleanest number.

7. Jacksonville Jaguars — 10 wins (p10: 7, p90: 12). The Jaguars project for a 73.8% playoff rate despite only a 27.2% division-title shot — a wild-card team in most timelines. Ten wins in a division-race context is a comfortable projection with a plausible downside of seven.

8. Atlanta Falcons — 7.97 wins (p10: 5, p90: 11). The model essentially calls eight wins and the rounding is honest — this team lives in the 7-to-9 range in the vast majority of simulations, with a 34.4% playoff rate and a 29.3% division-title chance. The Falcons are a good-not-great team until the model sees something different.

9. Miami Dolphins — 7.03 wins (p10: 5, p90: 10). The Dolphins are the mirror image of Atlanta on the win curve: the model calls seven and means it. A 2.8% division-title rate is the real story here — Miami projects as a wild-card-or-out franchise in a division with two double-digit-win projections ahead of them.

10. Baltimore Ravens — 10.05 wins (p10: 7, p90: 13). Ten wins, a 56.9% division-title rate, and 74.3% playoff odds. The Ravens are a settled projection — the band is wide enough to respect variance, but the mean is basically a division-winner call. The futures fade on the board's also-flagged list is about Super Bowl price, not about whether Baltimore wins the AFC North.

11. Green Bay Packers — 8.92 wins (p10: 6, p90: 11). Nearly nine wins, but a 13.2% division-title rate that tells you the NFC North is crowded. The Packers make the playoffs in 42.9% of simulations — slightly better than a coin flip — mostly on wild-card paths.

12. New York Jets — 5.11 wins (p10: 3, p90: 7). The Jets are the board's clearest low-end call: 1.4% playoff odds, 0.4% division-title odds, and a ceiling of seven wins that the simulation rarely reaches. Five wins is the expectation; three is a real scenario.

13. Pittsburgh Steelers — 8.88 wins (p10: 6, p90: 11). The Steelers sit almost on top of Green Bay in the win distribution — nearly nine wins, 46.3% playoff odds, 23.2% division-title odds. Like Green Bay, the projection respects the team while acknowledging the division math doesn't favor them.

14. Cleveland Browns — 7.14 wins (p10: 5, p90: 10). Seven wins with a 5.0% division-title rate. The Browns project as a fringe team in a hard division — 13.4% playoff odds means they need things to break right.

15. Houston Texans — 11.15 wins (p10: 9, p90: 14). The Texans carry the AFC's highest projected win mean, a 67.7% division-title rate, and 89.9% playoff odds. The floor of nine wins is the kind of floor you build a franchise around.

Two other things worth naming before leaving this section. Four divisions look functionally decided in the model: the Philadelphia Eagles (79.9% division odds, 10.46 projected wins), Denver (75.4%), Seattle Seahawks (71.8%, 12.28 wins), and Houston (67.7%). Two of those races are in the NFC and two in the AFC — four division crowns the model considers close to decided, which makes the wild-card scrambles elsewhere the season's real drama.

The crowded-division problem is real in 2026. The San Francisco 49ers project for 9.47 wins and a 60.4% playoff rate on only a 7.5% division-title chance — they are probably a good team going nowhere in their own division. The Kansas City Chiefs (8.3 wins, 10.0% division odds), the Los Angeles Chargers (8.64 wins, 14.4% division odds), and the Cincinnati Bengals (8.48 wins, 14.9% division odds) are all in similar situations. Nine wins and a division runner-up finish is not a failure; it's just a scheduling story the model can see before September.

Same framing note as the win totals: no season-prop lines are posted on our feeds yet. These fifteen calls are the model's projected season totals — numbers to hold in your head when the market opens and offers you something to compare them to. The record on these calls starts when lines post and the board journals the gaps.

One concept to understand once before the numbers start: every projection here is injury-adjusted. The mean is what a player is expected to produce across a seventeen-game schedule after pricing in the realistic chance he misses some of it — league-population absence rates by position, not any player-specific medical history. The mean_full_17g is the full-season pace if he plays every game. The gap between those two numbers is the injury tax. A player with a mean of 3,653 yards and a full-season pace of 4,798 yards is being docked about 1,145 yards by the model for the statistical likelihood of missed time. That is not pessimism; it is arithmetic.

Passing Yards

16. Jared Goff, Detroit Lions — Injury-adjusted passing yard mean: 3,653. Full-season pace: 4,798. Band: 998 at the 10th percentile to 5,122 at the 90th. Goff leads the board in injury-adjusted passing yards, which means the model sees him as both productive and durable enough at population rates to hold the top spot. The median outcome of 4,238 yards is a legitimate top-five season.

17. Matthew Stafford, Los Angeles Rams — Passing yard mean: 3,590. Full-season pace: 4,715. Band: 970 to 5,171. Stafford sits 64 yards behind Goff on the injury-adjusted mean but has a slightly wider upside ceiling — 5,171 at the 90th percentile is the board's highest passing-yard ceiling. The Rams appear four times on this board, which is the model's way of explaining why the team-level fade (Call 4) is about price rather than talent.

18. Dak Prescott, Dallas Cowboys — Passing yard mean: 3,300. Full-season pace: 4,334. Band: 879 to 4,855. Prescott's injury-adjusted mean trails Goff and Stafford by 350-plus yards, but the full-season pace of 4,334 still clears what most casual fans would call a strong year. The gap between his mean and his full-season pace — 1,034 yards — is the board's reminder that availability is a skill the market sometimes prices and sometimes doesn't.

Passing Touchdowns

19. Matthew Stafford, Los Angeles Rams — Injury-adjusted passing TD mean: 33. Full-season pace: 43.3. Band: 8 to 51. Thirty-three injury-adjusted touchdowns leads the board in this market. The median of 36 is the kind of number that wins fantasy championships and earns Pro Bowl votes. The 90th-percentile outcome of 51 is an all-time-caliber season; the 10th-percentile outcome of 8 is a reminder that wide bands mean wide bands.

20. Joe Burrow, Cincinnati Bengals — Passing TD mean: 28.9. Full-season pace: 38. Band: 7 to 45. Burrow's injury-adjusted mean trails Stafford by about four touchdowns, reflecting both a gap in projected volume and the model's standard absence overlay. The 45-touchdown ceiling is worth noting — if he plays a full year, the model sees elite-tier production.

Rushing Yards

21. Derrick Henry, Tennessee Titans — Injury-adjusted rushing yard mean: 1,556. Full-season pace: 1,770. Band: 831 to 2,132. Henry leads the board in rushing yards by 286 injury-adjusted yards over the second name on the list. The median outcome of 1,637 yards is a likely rushing title; the 90th-percentile outcome of 2,132 yards would be one of the best single seasons in recent memory.

22. Bijan Robinson, Atlanta Falcons — Rushing yard mean: 1,271. Full-season pace: 1,445. Band: 621 to 1,827. Robinson is the model's second-ranked rusher and the primary reason the Falcons' 7.97-win projection (Call 8) doesn't feel like a ceiling. The injury-adjusted mean of 1,271 still represents a top-five season in most years.

23. De'Von Achane, Miami Dolphins — Rushing yard mean: 1,215. Full-season pace: 1,382. Band: 655 to 1,653. Achane sits 56 yards behind Robinson on the injury-adjusted mean — close enough that the order could swap in-season. His 1,653-yard ceiling comes on a team the model projects at seven wins — the volume case rests on his role, not on game script.

Rushing Touchdowns

24. Derrick Henry, Tennessee Titans — Injury-adjusted rushing TD mean: 15.7. Full-season pace: 17.8. Band: 7 to 25. Henry leads this market by 4.4 touchdowns over the second name. The median of 16 rushing touchdowns is a number most backs don't reach in a healthy season. At the 90th percentile he scores 25, which would be historically unusual.

25. Zach Charbonnet, Seattle Seahawks — Rushing TD mean: 11.3. Full-season pace: 12.8. Band: 4 to 19. Charbonnet appears here as the model's second rushing-touchdown projection on a Seattle offense the board considers one of the league's best. Eleven injury-adjusted rushing touchdowns on a team projected for 12.28 wins tracks — good teams score more, and goal-line backs on good teams score often.

Receiving Yards

26. Puka Nacua, Los Angeles Rams — Injury-adjusted receiving yard mean: 1,447. Full-season pace: 1,706. Band: 611 to 2,119. Nacua leads the entire receiving board by 202 injury-adjusted yards over the second name. The full-season pace of 1,706 yards would be an all-time receiver season; even the injury-adjusted mean of 1,447 represents elite production. He also leads the receptions board — see Call 29.

27. Jaxon Smith-Njigba, Seattle Seahawks — Receiving yard mean: 1,245. Full-season pace: 1,468. Band: 558 to 1,746. Smith-Njigba sits second on the injury-adjusted receiving board on a Seattle team the model considers the league's strongest. The median outcome of 1,327 yards is a WR1 season by any standard.

28. Ja'Marr Chase, Cincinnati Bengals — Receiving yard mean: 1,228. Full-season pace: 1,447. Band: 550 to 1,721. Chase trails Smith-Njigba by 17 injury-adjusted yards — a negligible gap that will resolve in the actual season. His median of 1,308 yards and 90th-percentile ceiling of 1,721 reflect what the board already knows: he is one of the two or three most complete receivers in the league.

Receptions

29. Puka Nacua, Los Angeles Rams — Injury-adjusted reception mean: 106.3. Full-season pace: 125.3. Band: 49 to 144. Nacua's injury-adjusted reception mean of 106 is the model's statement that he is the highest-volume receiver in the league when healthy. The median of 115 catches stands alone at the top of this board.

30. Ja'Marr Chase, Cincinnati Bengals — Reception mean: 105.5. Full-season pace: 124.4. Band: 48 to 143. Chase and Nacua are separated by less than one reception on the injury-adjusted mean. If there is a sharper illustration of how thin the line between first and second can be, the board hasn't found it. Both medians land above 113 catches; at the 90th percentile, both are at 143 or 144.

Verdicts without falsifiers are just takes. Here is what would move each part of the board.

On the futures leans: a quarterback injury to the starting signal-caller on Seattle or Denver re-deals both value calls immediately. The model prices team quality, not a backup's ceiling. Similarly, if the Rams lose Stafford for a meaningful stretch, the fade at Call 4 becomes less interesting — not because the price improves, but because the team's Super Bowl path narrows on its own. The Chiefs fade at Call 5 would weaken if there is evidence the model's roster evaluation is behind — specifically, if an offseason acquisition the ratings haven't fully absorbed materially upgrades the depth chart. Frozen priors are honest priors, not infallible ones.

On the win totals: every call in that section is a model number without a market counterpart. The moment lines post, the board will journal each one and tell you where the gap is. A model call that has no line to compare against cannot become a lean; it is just a projection. Some of these numbers will survive contact with the market; some will look like easy consensus. We will tell you which is which when the data exists to say so.

On the player board: the injury overlay uses league-population absence rates — the historical average for a player at each position. If a specific player has a documented injury entering the season that is not reflected in that population average, the mean should be treated as optimistic. The model does not know what team doctors know. The full-season pace number (mean_full_17g) is the right starting point if you have reason to believe a player will be more available than average; the injury-adjusted mean is the right starting point if you don't.

On the sim itself: the ratings are re-run when the preseason data justifies it. A large enough body of preseason-game evidence, or a significant roster transaction after this writing, could shift the underlying Elo priors and move any number on any board. The frozen-priors system earns its keep over a long run; it is not designed to be right about every individual week in August.

Finally: the most important falsifier for the futures leans is simply the season being played. These five calls get graded when a champion is crowned. Until then, they are a public record of where the model and the market disagreed on August 16, 2026 — nothing more, and nothing less.

How all of this was built, stated plainly.

The simulation. The win projections and Super Bowl probabilities come from 10,000 season simulations, each seeded identically (seed 20260907) so the results are reproducible. The underlying team ratings are built from every NFL result since 1999 — twenty-seven seasons of margin of victory, adjusted for opponent and home field. Each offseason, ratings are regressed one-third of the way toward the league average, which is the model's way of acknowledging that teams change and that extreme results tend to moderate. Home-field advantage is calibrated from the last ten completed seasons. Once the season starts, the ratings do not change — the model cannot see in-season form, injuries, or momentum. That is the trade-off described in the Big Board: twenty-seven years of signal, zero ability to update on what happens in October.

Validation. The simulation was run out-of-sample on every season from 2015 through 2025 — eleven seasons the model could not have trained on. The mean error was 2.338 wins per team. For comparison: simply using the prior year's win total as a forecast produced a mean error of 2.813 wins; assuming every team wins half its games (flat .500) produced 2.602 wins. The simulation beats both baselines by a meaningful margin. That is not a claim that it is precise; it is a claim that it is more useful than the alternatives.

The player projections. Season totals for passing yards, passing touchdowns, rushing yards, rushing touchdowns, receiving yards, and receptions are built by modeling each player's per-game distribution and summing across seventeen games. The injury overlay applies league-population absence rates by position — not any player-specific medical judgment — so every mean already reflects the realistic chance of missing games. The closed-form math behind these distributions was cross-checked against a full Monte Carlo simulation; every published figure agreed within 0.34 percentage points on every check.

What a lean is. The five futures calls in the Big Board are leans — the board's term for a public, tracked disagreement with the de-vigged market that clears the 3-percentage-point threshold. They are graded when the season ends. There is no money attached to any lean published here, and if sizing ever entered the picture it would be expressed in units (one unit equals one percent of a stated budget), never in dollar amounts. A lean is a logged opinion with a falsifier attached; it is not a recommendation to act.

The record. There is no graded, market-relative record to cite for these futures leans. This is the first season the board has publicly journaled them. The record starts with the five calls above, and it will be updated honestly — including the ones that don't land. The win-total and player-prop records start when market lines post and the board journals the gaps. Until then, they are model calls, not measured leans, and the distinction matters.