The Poll.ai

2025 season · through week 7

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 66-06-0Gap +48.61 in 11key 1.03Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 1 and 95 of 136 in 90% of them.
  2. 2Indiana6-06-0Gap +40.61 in 41key 1.62Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 1 and 64 of 136 in 90% of them.
  3. 3Miami5-05-0Gap +43.71 in 17key 1.24Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 2 and 86 of 136 in 90% of them.
  4. 4Ohio State6-06-0Gap +42.91 in 19key 1.29Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 2 and 72 of 136 in 90% of them.
  5. 5Ole Misspoll has it 106-06-0Gap +56.71 in 6key 0.76Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 20 and 119 of 136 in 90% of them.
  6. 6Texas Tech6-06-0Gap +36.41 in 19key 1.29Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 2 and 47 of 136 in 90% of them.
  7. 7UNLVpoll has it 126-06-0Gap +62.71 in 6key 0.78Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 30 and 132 of 136 in 90% of them.
  8. 8Texas A&M6-06-0Gap +47.81 in 18key 1.24Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 5 and 104 of 136 in 90% of them.
  9. 9Georgia Tech6-06-0Gap +53.91 in 9key 0.98Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 16 and 114 of 136 in 90% of them.
  10. 10Memphis6-06-0Gap +47.81 in 7key 0.86Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 2 and 96 of 136 in 90% of them.
  11. 11Navy6-06-0Gap +54.01 in 5key 0.67Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 3 and 123 of 136 in 90% of them.
  12. 12Alabama5-15-1Gap +12.81 in 9key 0.97Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 3 and 91 of 136 in 90% of them.
  13. 13South Florida5-15-1Gap +15.81 in 6key 0.76Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 5 and 103 of 136 in 90% of them.
  14. 14Georgia5-15-1Gap +14.11 in 5key 0.72Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 6 and 97 of 136 in 90% of them.
  15. 15Utah5-15-1Gap +5.21 in 4key 0.59Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 3 and 65 of 136 in 90% of them.
  16. 16Vanderbilt5-15-1Gap +2.01 in 3key 0.53Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 3 and 77 of 136 in 90% of them.
  17. 17Virginia5-15-1Gap +11.61 in 3key 0.53Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 6 and 107 of 136 in 90% of them.
  18. 18Oregon5-15-1Gap +5.11 in 3key 0.52Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 3 and 78 of 136 in 90% of them.
  19. 19Oklahoma5-15-1Gap +11.41 in 3key 0.50Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 5 and 107 of 136 in 90% of them.
  20. 20Washington5-15-1Gap +10.21 in 3key 0.49Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 5 and 104 of 136 in 90% of them.
  21. 21Houston5-15-1Gap +15.01 in 3key 0.47Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 13 and 113 of 136 in 90% of them.
  22. 22LSU5-15-1Gap +14.81 in 3key 0.48Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 13 and 108 of 136 in 90% of them.
  23. 23Tulane5-15-1Gap +17.11 in 3key 0.45Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 16 and 125 of 136 in 90% of them.
  24. 24USC5-15-1Gap +3.61 in 3key 0.41Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 3 and 82 of 136 in 90% of them.
  25. 25Western Kentucky5-15-1Gap +19.51 in 2key 0.37Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 19 and 132 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 11 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 100 places.

2025 week 7 · 646ba6e1 · 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
1BYU6-01 in 114.2+1.811.4 (23)+48.61 to 950.0
2Indiana6-01 in 413.6+2.419.4 (3)+40.61 to 640.0
3Miami5-01 in 173.0+2.016.3 (10)+43.72 to 860.0
4Ohio State6-01 in 193.9+2.117.1 (6)+42.92 to 720.0
5Ole Miss6-01 in 64.5+1.53.3 (70)+56.720 to 1190.0
6Texas Tech6-01 in 194.0+2.023.6 (1)+36.42 to 47-1.0
7UNLV6-01 in 64.5+1.5-2.7 (110)+62.730 to 132-1.0
8Texas A&M6-01 in 183.9+2.112.2 (18)+47.85 to 104+2.0
9Georgia Tech6-01 in 94.2+1.86.1 (55)+53.916 to 1140.0
10Memphis6-01 in 74.4+1.612.2 (16)+47.82 to 960.0
11Navy6-01 in 54.7+1.36.0 (57)+54.03 to 1230.0
12Alabama5-11 in 93.2+1.816.7 (8)+12.83 to 91-2.0
13South Florida5-11 in 63.5+1.511.1 (25)+15.85 to 103+1.0
14Georgia5-11 in 53.5+1.511.6 (20)+14.16 to 97-1.0
15Utah5-11 in 43.7+1.318.2 (4)+5.23 to 65-5.0
16Vanderbilt5-11 in 33.9+1.121.4 (2)+2.03 to 77-9.0
17Virginia5-11 in 33.9+1.111.6 (21)+11.66 to 107-6.0
18Oregon5-11 in 33.9+1.117.3 (5)+5.13 to 78+5.0
19Oklahoma5-11 in 34.0+1.010.5 (29)+11.45 to 107+1.0
20Washington5-11 in 34.0+1.011.3 (24)+10.25 to 104+4.0
21Houston5-11 in 34.0+1.06.4 (54)+15.013 to 113-3.0
22LSU5-11 in 34.0+1.06.4 (53)+14.813 to 108+3.0
23Tulane5-11 in 34.0+1.03.3 (68)+17.116 to 125+1.0
24USC5-11 in 34.2+0.816.4 (9)+3.63 to 82-3.0
25Western Kentucky5-11 in 24.3+0.70.4 (90)+19.519 to 132-7.0
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 100how unlikely1 in this many
1BYUBig 126-0·Power 11.41 (23)·Gap +48.59Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 1 and 95 of 136 in 90% of them.1.0341 in 11
2IndianaBig Ten6-0·Power 19.43 (3)·Gap +40.57Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 1 and 64 of 136 in 90% of them.1.6171 in 41
3MiamiACC5-0·Power 16.34 (10)·Gap +43.66Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 2 and 86 of 136 in 90% of them.1.2401 in 17
4Ohio StateBig Ten6-0·Power 17.10 (6)·Gap +42.90Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 2 and 72 of 136 in 90% of them.1.2871 in 19
5Ole MissSEC6-0·Power 3.30 (70)·Gap +56.70Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 20 and 119 of 136 in 90% of them.0.7631 in 6
6Texas TechBig 126-0·Power 23.63 (1)·Gap +36.37·▼1Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 2 and 47 of 136 in 90% of them.1.2891 in 19
7UNLVMountain West6-0·Power -2.69 (110)·Gap +62.69·▼1Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 30 and 132 of 136 in 90% of them.0.7821 in 6
8Texas A&MSEC6-0·Power 12.16 (18)·Gap +47.84·▲2Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 5 and 104 of 136 in 90% of them.1.2451 in 18
9Georgia TechACC6-0·Power 6.11 (55)·Gap +53.89Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 16 and 114 of 136 in 90% of them.0.9781 in 9
10MemphisAmerican Athletic6-0·Power 12.22 (16)·Gap +47.78Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 2 and 96 of 136 in 90% of them.0.8591 in 7
11NavyAmerican Athletic6-0·Power 6.05 (57)·Gap +53.95Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 3 and 123 of 136 in 90% of them.0.6731 in 5
12AlabamaSEC5-1·Power 16.72 (8)·Gap +12.83·▼2Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 3 and 91 of 136 in 90% of them.0.9651 in 9
13South FloridaAmerican Athletic5-1·Power 11.10 (25)·Gap +15.81·▲1Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 5 and 103 of 136 in 90% of them.0.7561 in 6
14GeorgiaSEC5-1·Power 11.64 (20)·Gap +14.13·▼1Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 6 and 97 of 136 in 90% of them.0.7241 in 5
15UtahBig 125-1·Power 18.18 (4)·Gap +5.22·▼5Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 3 and 65 of 136 in 90% of them.0.5931 in 4
16VanderbiltSEC5-1·Power 21.37 (2)·Gap +2.01·▼9Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 3 and 77 of 136 in 90% of them.0.5281 in 3
17VirginiaACC5-1·Power 11.58 (21)·Gap +11.64·▼6Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 6 and 107 of 136 in 90% of them.0.5351 in 3
18OregonBig Ten5-1·Power 17.33 (5)·Gap +5.11·▲5Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 3 and 78 of 136 in 90% of them.0.5151 in 3
19OklahomaSEC5-1·Power 10.53 (29)·Gap +11.45·▲1Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 5 and 107 of 136 in 90% of them.0.4951 in 3
20WashingtonBig Ten5-1·Power 11.34 (24)·Gap +10.15·▲4Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 5 and 104 of 136 in 90% of them.0.4871 in 3
21HoustonBig 125-1·Power 6.41 (54)·Gap +14.98·▼3Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 13 and 113 of 136 in 90% of them.0.4681 in 3
22LSUSEC5-1·Power 6.44 (53)·Gap +14.84·▲3Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 13 and 108 of 136 in 90% of them.0.4851 in 3
23TulaneAmerican Athletic5-1·Power 3.34 (68)·Gap +17.11·▲1Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 16 and 125 of 136 in 90% of them.0.4471 in 3
24USCBig Ten5-1·Power 16.42 (9)·Gap +3.60·▼3Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 3 and 82 of 136 in 90% of them.0.4081 in 3
25Western KentuckyConference USA5-1·Power 0.45 (90)·Gap +19.55·▼7Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 19 and 132 of 136 in 90% of them.0.3721 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 840 games, and the digest of that exact frame is 8834d28d912e 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 646ba6e1 · published 2026-08-15 17:17:22 UTC · code b61a958 · config bf0fa1a3…
q_ref 11.10 (South Florida) · β_w 7 · C 1 · h 5.549 · σ 17.287 · λ₁ 125 · λ₂ 4.0 · k 74.78 · w₁ 0.6847 · w₂ -0.7403
reproduce: uv run cfbpoll rank --season 2025 --through-week 7 --out out/
Close

rank 11

Navy

6-0 · American Athletic. The model never reads conference names.

1 in 5how hard that record waskey 0.67
4.7wins this schedule asked for
+1.3how far past it they came

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

Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 3 and 123 of 136 in 90% of them.

3 to 123 · 120 places wide

power6.0, ranked 57
gap+54.0
resume60.0, ranked 6
hindsight11 · 0.0 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.