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.

  1. 1Miami5-05-0Gap +45.01 in 14key 1.15Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 2 and 86 of 136 in 90% of them.
  2. 2Texas Tech5-05-0Gap +34.41 in 13key 1.10Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 1 and 70 of 136 in 90% of them.
  3. 3Texas A&M5-05-0Gap +49.31 in 10key 0.99Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 3 and 93 of 136 in 90% of them.
  4. 4Indiana5-05-0Gap +43.71 in 9key 0.96Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 3 and 91 of 136 in 90% of them.
  5. 5Ohio State5-05-0Gap +45.21 in 8key 0.92Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 3 and 96 of 136 in 90% of them.
  6. 6Memphis6-06-0Gap +46.01 in 8key 0.89Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 5 and 92 of 136 in 90% of them.
  7. 7Oregon5-05-0Gap +40.81 in 7key 0.86Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 4 and 69 of 136 in 90% of them.
  8. 8Illinois5-15-1Gap +17.91 in 6key 0.79Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 4 and 96 of 136 in 90% of them.
  9. 9Oklahoma5-05-0Gap +49.51 in 6key 0.78Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 5 and 103 of 136 in 90% of them.
  10. 10Georgia Tech5-05-0Gap +55.21 in 6key 0.77Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 9 and 124 of 136 in 90% of them.
  11. 11BYU5-05-0Gap +47.61 in 5key 0.73Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 6 and 109 of 136 in 90% of them.
  12. 12UNLV5-05-0Gap +59.61 in 5key 0.70Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 31 and 129 of 136 in 90% of them.
  13. 13North Texas5-05-0Gap +47.31 in 5key 0.69Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 6 and 104 of 136 in 90% of them.
  14. 14Ole Miss5-05-0Gap +55.41 in 5key 0.69Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 8 and 122 of 136 in 90% of them.
  15. 15Alabama4-14-1Gap +8.41 in 5key 0.69Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 1 and 77 of 136 in 90% of them.
  16. 16Virginia5-15-1Gap +11.11 in 3key 0.54Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 2 and 107 of 136 in 90% of them.
  17. 17Vanderbilt5-15-1Gap +0.41 in 3key 0.52Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 1 and 46 of 136 in 90% of them.
  18. 18Georgia4-14-1Gap +11.91 in 3key 0.50Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 2 and 114 of 136 in 90% of them.
  19. 19Houston4-14-1Gap +15.41 in 3key 0.48Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 11 and 115 of 136 in 90% of them.
  20. 20Utah4-14-1Gap +4.21 in 3key 0.47Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 2 and 80 of 136 in 90% of them.
  21. 21Iowa State5-15-1Gap +11.11 in 3key 0.42Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 5 and 109 of 136 in 90% of them.
  22. 22Missouri5-05-0Gap +52.21 in 3key 0.41Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 15 and 107 of 136 in 90% of them.
  23. 23Western Kentucky5-15-1Gap +18.01 in 3key 0.40Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 18 and 130 of 136 in 90% of them.
  24. 24LSU4-14-1Gap +12.61 in 3key 0.40Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 10 and 112 of 136 in 90% of them.
  25. 25South Florida4-14-1Gap +14.41 in 3key 0.40Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 11 and 125 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 14 put Miami on top. One in 3 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 103 places.

2025 week 6 · a671ed1d · code e215160 · The House Poll · method 061a6d4e · 136 teams ranked

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
1Miami5-01 in 143.2+1.815.0 (10)+45.02 to 86-1.0
2Texas Tech5-01 in 133.4+1.625.6 (1)+34.41 to 70-1.0
3Texas A&M5-01 in 103.3+1.710.7 (22)+49.33 to 93+2.0
4Indiana5-01 in 93.4+1.616.3 (8)+43.73 to 91-1.0
5Ohio State5-01 in 83.4+1.614.8 (11)+45.23 to 96-1.0
6Memphis6-01 in 84.3+1.714.0 (13)+46.05 to 92-9.0
7Oregon5-01 in 73.5+1.519.2 (3)+40.84 to 69+3.0
8Illinois5-11 in 63.5+1.58.6 (33)+17.94 to 96+1.0
9Oklahoma5-01 in 63.6+1.410.5 (24)+49.55 to 103+1.0
10Georgia Tech5-01 in 63.6+1.44.8 (56)+55.29 to 124-1.0
11BYU5-01 in 53.7+1.312.4 (18)+47.66 to 109-2.0
12UNLV5-01 in 53.7+1.30.4 (83)+59.631 to 129-4.0
13North Texas5-01 in 53.7+1.312.7 (16)+47.36 to 104+4.0
14Ole Miss5-01 in 53.7+1.34.6 (58)+55.48 to 122+4.0
15Alabama4-11 in 52.8+1.217.7 (5)+8.41 to 77+3.0
16Virginia5-11 in 33.9+1.111.4 (20)+11.12 to 107-5.0
17Vanderbilt5-11 in 33.9+1.122.3 (2)+0.41 to 46-5.0
18Georgia4-11 in 33.0+1.010.2 (25)+11.92 to 114-2.0
19Houston4-11 in 33.0+1.06.1 (51)+15.411 to 115-9.0
20Utah4-11 in 33.0+1.017.0 (7)+4.22 to 80-7.0
21Iowa State5-11 in 34.1+0.98.1 (36)+11.15 to 109+7.0
22Missouri5-01 in 34.2+0.87.8 (38)+52.215 to 107-2.0
23Western Kentucky5-11 in 34.2+0.81.8 (72)+18.018 to 130-13.0
24LSU4-11 in 33.2+0.86.6 (47)+12.610 to 112+5.0
25South Florida4-11 in 33.3+0.76.3 (49)+14.411 to 125+7.0
Show the full table: top 25 of 136, every columnthe published poll
#teamunderline: 90% rank interval · league median width 103how unlikely1 in this many
1MiamiACC5-0·Power 15.05 (10)·Gap +44.95·1Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 2 and 86 of 136 in 90% of them.1.1541 in 14
2Texas TechBig 125-0·Power 25.56 (1)·Gap +34.44·1Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 1 and 70 of 136 in 90% of them.1.0991 in 13
3Texas A&MSEC5-0·Power 10.74 (22)·Gap +49.26·2Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 3 and 93 of 136 in 90% of them.0.9911 in 10
4IndianaBig Ten5-0·Power 16.31 (8)·Gap +43.69·1Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 3 and 91 of 136 in 90% of them.0.9581 in 9
5Ohio StateBig Ten5-0·Power 14.85 (11)·Gap +45.15·1Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 3 and 96 of 136 in 90% of them.0.9181 in 8
6MemphisAmerican Athletic6-0·Power 13.97 (13)·Gap +46.03·9Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 5 and 92 of 136 in 90% of them.0.8891 in 8
7OregonBig Ten5-0·Power 19.24 (3)·Gap +40.76·3Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 4 and 69 of 136 in 90% of them.0.8571 in 7
8IllinoisBig Ten5-1·Power 8.64 (33)·Gap +17.95·1Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 4 and 96 of 136 in 90% of them.0.7911 in 6
9OklahomaSEC5-0·Power 10.53 (24)·Gap +49.47·1Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 5 and 103 of 136 in 90% of them.0.7831 in 6
10Georgia TechACC5-0·Power 4.77 (56)·Gap +55.23·1Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 9 and 124 of 136 in 90% of them.0.7661 in 6
11BYUBig 125-0·Power 12.36 (18)·Gap +47.64·2Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 6 and 109 of 136 in 90% of them.0.7291 in 5
12UNLVMountain West5-0·Power 0.44 (83)·Gap +59.56·4Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 31 and 129 of 136 in 90% of them.0.7011 in 5
13North TexasAmerican Athletic5-0·Power 12.71 (16)·Gap +47.29·4Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 6 and 104 of 136 in 90% of them.0.6911 in 5
14Ole MissSEC5-0·Power 4.59 (58)·Gap +55.41·4Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 8 and 122 of 136 in 90% of them.0.6901 in 5
15AlabamaSEC4-1·Power 17.71 (5)·Gap +8.36·3Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 1 and 77 of 136 in 90% of them.0.6871 in 5
16VirginiaACC5-1·Power 11.42 (20)·Gap +11.06·5Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 2 and 107 of 136 in 90% of them.0.5401 in 3
17VanderbiltSEC5-1·Power 22.28 (2)·Gap +0.40·5Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 1 and 46 of 136 in 90% of them.0.5241 in 3
18GeorgiaSEC4-1·Power 10.21 (25)·Gap +11.88·2Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 2 and 114 of 136 in 90% of them.0.4961 in 3
19HoustonBig 124-1·Power 6.09 (51)·Gap +15.43·9Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 11 and 115 of 136 in 90% of them.0.4821 in 3
20UtahBig 124-1·Power 17.04 (7)·Gap +4.23·7Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 2 and 80 of 136 in 90% of them.0.4701 in 3
21Iowa StateBig 125-1·Power 8.10 (36)·Gap +11.14·7Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 5 and 109 of 136 in 90% of them.0.4231 in 3
22MissouriSEC5-0·Power 7.83 (38)·Gap +52.17·2Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 15 and 107 of 136 in 90% of them.0.4151 in 3
23Western KentuckyConference USA5-1·Power 1.81 (72)·Gap +18.04·13Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 18 and 130 of 136 in 90% of them.0.4051 in 3
24LSUSEC4-1·Power 6.59 (47)·Gap +12.57·5Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 10 and 112 of 136 in 90% of them.0.4021 in 3
25South FloridaAmerican Athletic4-1·Power 6.25 (49)·Gap +14.39·7Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 11 and 125 of 136 in 90% of them.0.4001 in 3

What this board believes football results are for

The House Poll, in its own words

This is the poll this project publishes and this recipe changes nothing about it. Margin counts, and it counts less and less as it grows: the response is C*tanh(m/C) with C at 32 points plus a 7 point win premium, so the gap between a 3 point win and a 24 point win is large and the gap between a 45 point win and a 70 point win is almost nothing. There is no cliff anywhere for a coach to aim at, which is what tanh buys over a hard cap. Then the headline does not read margin at all. Teams are ordered by schedule odds, the probability that a team of published reference quality would have gone at least this well against this exact schedule, and the harder it was to do what you did the higher you go. Margin is in the engine because it carries real information about how good a team is. It is out of the headline because the headline is about what a team earned, and those are two different questions that deserve two different columns.

what it costs

  • An unbeaten team can finish behind a one loss team, and that will need explaining every single year. It is the direct consequence of the promise. The explanation is on the page: the tail probability, the reference team it was measured against by name, and the Power column beside it.
  • One published constant the wins based resume did not need. q_ref is the Power rating of the 25th ranked team that week, the least flattering defensible reading of ESPN's average Top-25 team. The study measured the ordering across a 16 point swing in it: Kendall's tau never fell below 0.985 and at most one team entered or left the top 25.
  • It loses forward ordering accuracy to Full Merit by about two points. Accepted, and for a stated reason: forward accuracy is a prediction metric and the headline poll is not the instrument this project ships for prediction. L3 Power is, and it beats all three orderings on that axis.
  • It is a compromise and it will satisfy neither end of the argument completely. Someone who believes margin is the only honest measurement will find the compression arbitrary, and someone who believes point differential should never pay will find 32 points of it far too generous. Both are reading the position correctly.

what it changed

Nothing at all. This is the poll this project publishes and this recipe changes none of it.

configs/recipes/house.toml

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: 061a6d4eda23.

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 a671ed1d · published 2026-08-19 23:12:11 UTC · code e215160 · config 1ef7cf23…
q_ref 10.21 (Georgia) · β_w 7 · C 32 · h 5.712 · σ 17.460 · λ₁ 150 · λ₂ 0.5 · k 81.98 · w₁ 0.7225 · w₂ -0.1349
Close

rank 1

Miami

5-0 · ACC. The model never reads conference names.

1 in 14how hard that record waskey 1.15
3.2wins this schedule asked for
+1.8how far past it they came

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

Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 2 and 86 of 136 in 90% of them.

2 to 86 · 84 places wide

power15.0, ranked 10
gap+45.0
resume60.0, ranked 5
hindsight2 · -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.