The Poll.ai

2025 season · through week 6

Nobody voted on this.

Ranked by how hard each record was to get against the schedule that produced it. No preseason poll, no reputation, no conference name.

Switch below and watch which teams move.

Just WinALTERNATE LENS. The same games, ranked on different beliefs.
  1. 1BYUpoll has it 115-05-0Gap +47.61 in 6key 0.77Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 1 and 112 of 136 in 90% of them.
  2. 2Indiana5-05-0Gap +42.81 in 9key 0.95Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 1 and 93 of 136 in 90% of them.
  3. 3Miami5-05-0Gap +44.21 in 17key 1.23Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 2 and 88 of 136 in 90% of them.
  4. 4Missouripoll has it 225-05-0Gap +51.71 in 3key 0.42Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 2 and 108 of 136 in 90% of them.
  5. 5Navypoll has it 265-05-0Gap +52.91 in 2key 0.39Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 2 and 120 of 136 in 90% of them.
  6. 6North Texaspoll has it 135-05-0Gap +47.71 in 5key 0.70Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 3 and 107 of 136 in 90% of them.
  7. 7Ohio State5-05-0Gap +44.61 in 8key 0.91Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 3 and 97 of 136 in 90% of them.
  8. 8Oklahoma5-05-0Gap +49.01 in 6key 0.77Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 4 and 105 of 136 in 90% of them.
  9. 9Ole Misspoll has it 145-05-0Gap +55.11 in 5key 0.71Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 7 and 123 of 136 in 90% of them.
  10. 10Oregon5-05-0Gap +40.11 in 8key 0.89Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 3 and 72 of 136 in 90% of them.
  11. 11Texas Techpoll has it 25-05-0Gap +33.81 in 12key 1.09Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 4 and 68 of 136 in 90% of them.
  12. 12UNLV5-05-0Gap +61.11 in 5key 0.71Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 31 and 130 of 136 in 90% of them.
  13. 13Texas A&Mpoll has it 35-05-0Gap +48.91 in 11key 1.05Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 7 and 112 of 136 in 90% of them.
  14. 14Georgia Tech5-05-0Gap +56.01 in 6key 0.79Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 21 and 125 of 136 in 90% of them.
  15. 15Memphispoll has it 66-06-0Gap +46.61 in 8key 0.88Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 2 and 91 of 136 in 90% of them.
  16. 16Illinoispoll has it 85-15-1Gap +18.91 in 6key 0.80Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 8 and 98 of 136 in 90% of them.
  17. 17Alabama4-14-1Gap +9.01 in 5key 0.71Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 1 and 77 of 136 in 90% of them.
  18. 18Vanderbilt5-15-1Gap +1.11 in 3key 0.54Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 5 and 47 of 136 in 90% of them.
  19. 19Virginia5-15-1Gap +11.81 in 4key 0.55Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 7 and 106 of 136 in 90% of them.
  20. 20Georgia4-14-1Gap +11.71 in 3key 0.50Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 5 and 116 of 136 in 90% of them.
  21. 21Houston4-14-1Gap +16.31 in 3key 0.48Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 12 and 118 of 136 in 90% of them.
  22. 22Utah4-14-1Gap +4.51 in 3key 0.48Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 5 and 81 of 136 in 90% of them.
  23. 23South Florida4-14-1Gap +15.41 in 3key 0.41Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 10 and 128 of 136 in 90% of them.
  24. 24Western Kentucky5-15-1Gap +20.11 in 3key 0.40Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 19 and 131 of 136 in 90% of them.
  25. 25Michigan4-14-1Gap +9.41 in 2key 0.39Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 4 and 93 of 136 in 90% of them.

The right-hand number is how hard that season was to pull off against that exact schedule. One in 6 put BYU on top. One in 2 was enough for 25th. Below about 1 in 5 it stops separating teams, which is what the smaller key underneath is for. The bar under each name is where the model kept landing that team out of 136, every bar on one scale. A typical bar this week spans 104 places.

2025 week 6 · bdd682d1 · code b61a958 · Just Win · method cadaeb94 · 136 teams ranked

The tinted rows are the teams this lens moves at least five places, and each one carries both ranks.

Every column

The board above is the thirty-second version. Below it is everything the run published: the résumé, the power rating, the gap, the rank interval, and where the finished season puts each team.

Every figure the cards carry, for the 25 on the board, with nothing to click.
#teamrecordhow unlikely1 in this manyexpectedwins this schedule asked forbeat that bypowerrating (rank)gapplaces from the pollrangein 90% of replayshindsight
1BYU5-01 in 63.7+1.312.4 (18)+47.61 to 1120.0
2Indiana5-01 in 93.4+1.617.2 (9)+42.81 to 930.0
3Miami5-01 in 173.1+1.915.8 (11)+44.22 to 880.0
4Missouri5-01 in 34.2+0.88.3 (38)+51.72 to 1080.0
5Navy5-01 in 24.2+0.87.1 (50)+52.92 to 120-8.0
6North Texas5-01 in 53.7+1.312.3 (19)+47.73 to 107+1.0
7Ohio State5-01 in 83.4+1.615.4 (12)+44.63 to 97+1.0
8Oklahoma5-01 in 63.6+1.411.0 (26)+49.04 to 105+1.0
9Ole Miss5-01 in 53.6+1.44.9 (58)+55.17 to 123+1.0
10Oregon5-01 in 83.5+1.519.9 (3)+40.13 to 72+1.0
11Texas Tech5-01 in 123.4+1.626.2 (1)+33.84 to 680.0
12UNLV5-01 in 53.7+1.3-1.1 (102)+61.131 to 1300.0
13Texas A&M5-01 in 113.2+1.811.1 (25)+48.97 to 112+3.0
14Georgia Tech5-01 in 63.6+1.44.0 (64)+56.021 to 1250.0
15Memphis6-01 in 84.3+1.713.4 (16)+46.62 to 910.0
16Illinois5-11 in 63.4+1.69.0 (35)+18.98 to 980.0
17Alabama4-11 in 52.7+1.318.7 (5)+9.01 to 770.0
18Vanderbilt5-11 in 33.9+1.123.0 (2)+1.15 to 47-7.0
19Virginia5-11 in 43.9+1.112.0 (21)+11.87 to 106-5.0
20Georgia4-11 in 33.0+1.011.5 (23)+11.75 to 116-2.0
21Houston4-11 in 33.0+1.06.3 (53)+16.312 to 118-7.0
22Utah4-11 in 33.0+1.018.0 (7)+4.55 to 81-4.0
23South Florida4-11 in 33.3+0.76.5 (52)+15.410 to 128+5.0
24Western Kentucky5-11 in 34.2+0.80.9 (83)+20.119 to 131-10.0
25Michigan4-11 in 23.2+0.811.2 (24)+9.44 to 93-2.0
Show the full table: top 25 of 136, every columnALTERNATE LENS. The same games, ranked on different beliefs.
#teamunderline: 90% rank interval · league median width 104how unlikely1 in this many
1BYUBig 125-0·Power 12.39 (18)·Gap +47.61Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 1 and 112 of 136 in 90% of them.0.7681 in 6
2IndianaBig Ten5-0·Power 17.16 (9)·Gap +42.84Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 1 and 93 of 136 in 90% of them.0.9551 in 9
3MiamiACC5-0·Power 15.80 (11)·Gap +44.20Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 2 and 88 of 136 in 90% of them.1.2291 in 17
4MissouriSEC5-0·Power 8.34 (38)·Gap +51.66Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 2 and 108 of 136 in 90% of them.0.4221 in 3
5NavyAmerican Athletic5-0·Power 7.13 (50)·Gap +52.87·▼8Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 2 and 120 of 136 in 90% of them.0.3921 in 2
6North TexasAmerican Athletic5-0·Power 12.34 (19)·Gap +47.66·▲1Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 3 and 107 of 136 in 90% of them.0.6961 in 5
7Ohio StateBig Ten5-0·Power 15.42 (12)·Gap +44.58·▲1Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 3 and 97 of 136 in 90% of them.0.9111 in 8
8OklahomaSEC5-0·Power 11.03 (26)·Gap +48.97·▲1Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 4 and 105 of 136 in 90% of them.0.7701 in 6
9Ole MissSEC5-0·Power 4.85 (58)·Gap +55.15·▲1Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 7 and 123 of 136 in 90% of them.0.7101 in 5
10OregonBig Ten5-0·Power 19.90 (3)·Gap +40.10·▲1Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 3 and 72 of 136 in 90% of them.0.8901 in 8
11Texas TechBig 125-0·Power 26.21 (1)·Gap +33.79Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 4 and 68 of 136 in 90% of them.1.0871 in 12
12UNLVMountain West5-0·Power -1.11 (102)·Gap +61.11Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 31 and 130 of 136 in 90% of them.0.7071 in 5
13Texas A&MSEC5-0·Power 11.13 (25)·Gap +48.87·▲3Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 7 and 112 of 136 in 90% of them.1.0501 in 11
14Georgia TechACC5-0·Power 4.04 (64)·Gap +55.96Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 21 and 125 of 136 in 90% of them.0.7851 in 6
15MemphisAmerican Athletic6-0·Power 13.42 (16)·Gap +46.58Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 2 and 91 of 136 in 90% of them.0.8851 in 8
16IllinoisBig Ten5-1·Power 8.99 (35)·Gap +18.90Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 8 and 98 of 136 in 90% of them.0.8021 in 6
17AlabamaSEC4-1·Power 18.71 (5)·Gap +9.02Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 1 and 77 of 136 in 90% of them.0.7071 in 5
18VanderbiltSEC5-1·Power 23.03 (2)·Gap +1.07·▼7Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 5 and 47 of 136 in 90% of them.0.5411 in 3
19VirginiaACC5-1·Power 12.02 (21)·Gap +11.82·▼5Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 7 and 106 of 136 in 90% of them.0.5501 in 4
20GeorgiaSEC4-1·Power 11.55 (23)·Gap +11.68·▼2Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 5 and 116 of 136 in 90% of them.0.4971 in 3
21HoustonBig 124-1·Power 6.31 (53)·Gap +16.32·▼7Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 12 and 118 of 136 in 90% of them.0.4841 in 3
22UtahBig 124-1·Power 17.97 (7)·Gap +4.46·▼4Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 5 and 81 of 136 in 90% of them.0.4761 in 3
23South FloridaAmerican Athletic4-1·Power 6.52 (52)·Gap +15.44·▲5Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 10 and 128 of 136 in 90% of them.0.4091 in 3
24Western KentuckyConference USA5-1·Power 0.86 (83)·Gap +20.11·▼10Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 19 and 131 of 136 in 90% of them.0.4031 in 3
25MichiganBig Ten4-1·Power 11.23 (24)·Gap +9.44·▼2Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 4 and 93 of 136 in 90% of them.0.3911 in 2

What this board believes football results are for

Just Win, in its own words

If point differential pays, teams will run up the score on an opponent who cannot stop them. Everybody has seen it happen and nobody enjoyed it. This recipe takes the incentive off the table the only way a poll actually can, by refusing to look: the compression scale drops to a single point against a 7 point win premium, so margin can move a game's contribution by at most 1 point in 8 and beating someone by 70 is worth almost exactly what beating them by 1 is worth. There is nothing left to chase in the fourth quarter. The table is then ordered by the wins based resume, which asks one question and only one, what quality of team would have been expected to produce this record against this schedule. That is Colley's principle arrived at from the other direction, and the cost of it is written down below in full, because this recipe throws away real information on purpose and a reader is owed the size of what it threw away.

what it costs

  • It throws away information that is genuinely there. A 70 point win and a 1 point win are not the same event and this recipe values them within about 15 percent of each other. That is the whole objection to Colley and it lands.
  • It cannot move an unbeaten team, at all, ever. Expected wins approaches n from below, so an undefeated team has no finite root and every one of them lands on exactly the published bracket of +60, which is not a function of the schedule. The retroactive re-ranking that is this project's most differentiated product therefore cannot move an unbeaten team by a single place, in any week, in any direction. If September turns out to have been harder than it looked, this recipe cannot say so.
  • No team with a loss can ever be ranked above an unbeaten team. Not rarely. Never, by construction, in every season measured. In 2021 that forced a 13-0 Cincinnati to number one ahead of Alabama, Georgia and Michigan, a position no independent judge reached.
  • THE TIE-BREAK DOES NOT WORK AT THIS COMPRESSION, and the consequence is visible on the first four lines of the board. Under the house constants the margin aware resume separates teams sitting on the saturation bound, which is the one job it is kept for. At C = 1 that variant compresses too, so every unbeaten team saturates on BOTH columns and the order among them falls through to the last key in the sort, which is the team name. In 2023 the top four under this recipe is Florida State, Liberty, Michigan, Washington, in alphabetical order. That is not a ranking of those four teams. It is the absence of one, and the recipe has no way to produce a ranking of them because it has thrown away the only information that would distinguish them.
  • Compressing to a single point is far outside anything the tuning campaigns searched. Campaign 1's grid opened at C = 18 and campaign 2 only widened it upward. This constant is a value judgement taken to its logical end, not a fitted number, and nothing in this repository claims it predicts well.

what it changed

  • margin.beta_w7
  • margin.c1
  • publication.headline_orderingL4_resume

configs/recipes/just-win.toml

Full MeritMargin counts for exactly what it is worth. Nothing is protected.alternate lensThe House PollMargin in the engine, schedule odds in the headline. The published poll.the published poll

same evidence, different valuesEvery board on this page was fitted on the same 728 games, and the digest of that exact frame is dfd44ad43376 under all three. Switching lens changes the constants above and nothing else. Method digest for this one: cadaeb945e90.

Where next

  • Tune it yourself Move one setting and watch the board move.
  • The revision What this week looked like once the season ended.
  • Connectivity How much of the field was comparable, and what held it together.
  • Methodology Every constant, every baseline, and the lines that say FAIL.
run bdd682d1 · published 2026-08-15 17:16:55 UTC · code b61a958 · config bf0fa1a3…
q_ref 11.13 (Texas A&M) · β_w 7 · C 1 · h 5.755 · σ 17.762 · λ₁ 150 · λ₂ 4.0 · k 81.98 · w₁ 0.7236 · w₂ -0.8842
reproduce: uv run cfbpoll rank --season 2025 --through-week 6 --out out/
Close

rank 7

Ohio State

5-0 · Big Ten. The model never reads conference names.

1 in 8how hard that record waskey 0.91
3.4wins this schedule asked for
+1.6how far past it they came

where the model can honestly pin this team, out of 136

Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 3 and 97 of 136 in 90% of them.

3 to 97 · 94 places wide

power15.4, ranked 12
gap+44.6
resume60.0, ranked 9
hindsight6 · +1.0 against this rank

This team’s wins-based resume hit the bound the model puts on how much one result can be worth, so the figure above understates it.