The Poll.ai

Connectivity report · 2025 week 6

What this ranking is standing on.

The poll opened in Week 5. This is week 6; the schedule graph below is what the ranking is standing on.

The schedule graph

Abilene ChristianAdrianAir ForceAkronAlabamaAlabama A&MAlabama StateAlbany State GAAlcorn StateAllenAmerican InternationalApp StateArizonaArizona StateArkansasArkansas StateArkansas-Pine BluffArmyAuburnAustin PeayBLUEFIELDBYUBall StateBaylorBentleyBethune-CookmanBoise StateBoston CollegeBowie StateBowling GreenBrownBryantBucknellBuffaloButlerCal PolyCaliforniaCampbellCentral ArkansasCentral ConnecticutCentral MichiganCentral State (OH)Central WashingtonChadron StCharleston SouthernCharlotteChattanoogaCincinnatiClemsonCoastal CarolinaColgateColoradoColorado StateColumbiaCornellCumberland (TN)DartmouthDavidsonDaytonDelawareDelaware StateDrakeDukeDuquesneERSKINEEast CarolinaEast Tennessee StateEast Texas A&MEastern IllinoisEastern KentuckyEastern MichiganEastern WashingtonEdward WatersElizabeth City StateElonFayetteville StateFerrumFloridaFlorida A&MFlorida AtlanticFlorida InternationalFlorida StateFordhamFresno StateFurmanGardner-WebbGeorgetownGeorgiaGeorgia SouthernGeorgia StateGeorgia TechGramblingGreensboro CollegeHamptonHanover CollegeHarvardHawai'iHoly CrossHoustonHouston ChristianHowardIdahoIdaho StateIllinoisIllinois StateIncarnate WordIndianaIndiana StateIowaIowa StateJackson StateJacksonville StateJames MadisonKansasKansas StateKennesaw StateKent StateKentuckyKentucky ChristianKentucky StateLSULafayetteLamarLane CollegeLangston UniversityLehighLibertyLincoln (CA)Lincoln (PA)LindenwoodLittle RockLong Island UniversityLouisianaLouisiana CollegeLouisiana TechLouisvilleMaineMaristMarshallMarylandMassachusettsMcKendreeMcNeeseMemphisMercerMercyhurstMerrimackMiamiMiami (OH)MichiganMichigan StateMiddle TennesseeMiles CollegeMinnesotaMississippi StateMississippi Valley StateMissouriMissouri StateMonmouthMontanaMontana StateMorehead StateMorehouse CollegeMorgan StateMurray StateNC StateNavyNebraskaNevadaNew HampshireNew HavenNew MexicoNew Mexico StateNichollsNorfolk StateNorth AlabamaNorth CarolinaNorth Carolina A&TNorth Carolina CentralNorth DakotaNorth Dakota StateNorth TexasNortheastern StateNorthern ArizonaNorthern ColoradoNorthern IllinoisNorthern IowaNorthern MichiganNorthwesternNorthwestern StateNotre DameOhioOhio StateOklahomaOklahoma StateOld DominionOle MissOregonOregon StatePacePenn StatePennsylvaniaPittsburghPortland StatePrairie View A&MPresbyterianPrincetonPurdueRhode IslandRiceRichmondRobert MorrisRutgersSE LouisianaSMUSacramento StateSacred HeartSaginaw Valley StateSam HoustonSamfordSan DiegoSan Diego StateSan José StateSavannah StSouth AlabamaSouth CarolinaSouth Carolina StateSouth DakotaSouth Dakota StateSouth FloridaSoutheast Missouri StateSouthernSouthern IllinoisSouthern MissSouthern UtahSt. AnselmSt. Francis (PA)St. Thomas (MN)StanfordStephen F. AustinStetsonStonehillStony BrookSul Ross StateSyracuseTCUTarleton StateTempleTennesseeTennessee StateTennessee TechTexasTexas A&MTexas SouthernTexas StateTexas TechTexas WesleyanThe CitadelThomas More CollegeToledoTowsonTroyTruman StateTulaneTulsaTuskegeeUABUAlbanyUC DavisUCFUCLAUConnUL MonroeUNLVUSCUT MartinUT Rio Grande ValleyUTEPUTSAUpper Iowa UniversityUtahUtah StateUtah TechVMIValparaisoVanderbiltVillanovaVirginiaVirginia StVirginia TechVirginia University Of LynchburgWagnerWake ForestWarner UniversityWashingtonWashington StateWebber InternationalWeber StateWest GeorgiaWest LibertyWest VirginiaWestern CarolinaWestern IllinoisWestern KentuckyWestern MichiganWestern OregonWilliam & MaryWisconsinWoffordWyomingYaleYoungstown State
Every team in the fit is a node; every game played through week 6 is an edge. Teams in different components have no chain of results connecting them at all. Not through a common opponent, not through anyone’s common opponent, not at any length. The graph is welded into one component, all 312 teams mutually comparable. That is the condition under which a ranking is a measurement rather than an extrapolation. Watching this knit together, week by week, is the content.

Diagnostics

teams in the fitEvery team with at least one game against an FBS or FCS opponent.312
games played728
connected componentsSeparate islands of the schedule graph. Two teams in different components have no chain of results connecting them at all, at any length.1
largest componentShare of the field that is mutually comparable through played games.312 teams (100.0%)
bridge gamesSingle games whose removal would split the graph in two, each holding at least two teams on. Every rating on the far side rests on that one result.0
fitted λ₂ (results core)Chosen by cross-validation every week. λ is a ratio of variances, a statement about how much the model does not know, containing no team-specific information whatsoever. It is large when the data is thin and falls as the season accumulates. Regularization stays a statement about variance and never becomes a reputation prior.0.500
median 90% rank-interval widthOut of 136 ranked teams, from 1,000 replays of this exact schedule. This is the number that says whether the season has settled.103 places
teams whose interval spans the league90% interval at least 90% as wide as the whole field.0
teams no closer than three hops to the top tenThey have not played a top-ten team and share no opponent with one. Their position relative to the top of the poll is an extrapolation.144

These are the measures for the weeks before the poll opens, published whether or not they are flattering. The one worth understanding is the penalty the model puts on itself: chosen fresh every week by testing, identical for every team, and carrying nothing about any particular one. Pulling an unknown team toward the middle is a statement about the model’s own ignorance, which is why it can produce a number in week 2 without being told which conferences are supposed to be good.

Bridge games

No bridge games this week. Every link in the graph has a second path around it, which means no single result is load-bearing for a whole cluster of ratings. That is a good week, and it is the first thing that stops being true when you go looking at week 2.

What would have to be true

The graph is welded: every team is connected to every other through some chain of results. That is the condition under which a ranking is a measurement rather than an extrapolation, and it is why the opening week is week 5 and not week 1.

Every one of these is worth more to this poll than its television slot suggests. They are the games that turn two separate islands of results into one comparable field, and no ranking that exists today will tell you which they are.

The provisional table

The estimator runs from week 1. The headline poll does not. What is behind this click is real output from the real model on the real schedule, and it is not the poll. It is labelled provisional on every row, on purpose, because the difference between computing something and being willing to publish it as a ranking is the entire point of this page.

Show the provisional table: 136 teams, ranked by schedule oddsThis week is past the start line: this is the 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

Top 25 of 136. The full table, the résumé column, the schedule receipts and the sort controls arrive with the week 5 launch.

How this works, and where it is weak →

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
reproduce: uv run cfbpoll rank --season 2025 --through-week 6 --out out/