Connectivity report · 2025 week 8

What this ranking is standing on.

The poll opened in Week 5. This is week 8; 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 Connecticut StateSouthern 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 TechVMIValdosta StateValparaisoVanderbiltVillanovaVirginiaVirginia StVirginia TechVirginia University Of LynchburgWagnerWake ForestWarner UniversityWashingtonWashington StateWebber InternationalWeber StateWest GeorgiaWest LibertyWest VirginiaWestern CarolinaWestern Connecticut StWestern IllinoisWestern KentuckyWestern MichiganWestern OregonWilliam & MaryWisconsinWoffordWyomingYaleYoungstown State
Every team in the fit is a node; every game played through week 8 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 315 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.315
games played952
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.315 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.96 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.117

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 96.5how unlikely1 in this many
1IndianaBig Ten7-0·Power 19.79 (4)·Gap +40.21Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 1 and 55 of 136 in 90% of them.1.7161 in 52
2Texas A&MSEC7-0·Power 13.40 (13)·Gap +46.60Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 2 and 85 of 136 in 90% of them.1.4731 in 30
3Ohio StateBig Ten7-0·Power 20.31 (3)·Gap +39.69Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 1 and 58 of 136 in 90% of them.1.3481 in 22
4BYUBig 127-0·Power 12.84 (17)·Gap +47.16Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 3 and 91 of 136 in 90% of them.1.3311 in 21
5Georgia TechACC7-0·Power 9.71 (33)·Gap +50.29·1Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 8 and 97 of 136 in 90% of them.1.2631 in 18
6AlabamaSEC6-1·Power 15.86 (7)·Gap +12.40·1Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 1 and 72 of 136 in 90% of them.1.0071 in 10
7GeorgiaSEC6-1·Power 11.28 (24)·Gap +14.10·1Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 4 and 103 of 136 in 90% of them.0.7841 in 6
8South FloridaAmerican Athletic6-1·Power 12.06 (18)·Gap +13.75·1Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 4 and 89 of 136 in 90% of them.0.7601 in 6
9Texas TechBig 126-1·Power 21.16 (1)·Gap +5.04·5Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 1 and 56 of 136 in 90% of them.0.7461 in 6
10OregonBig Ten6-1·Power 20.72 (2)·Gap +3.17·3Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 1 and 54 of 136 in 90% of them.0.6641 in 5
11OklahomaSEC6-1·Power 13.13 (16)·Gap +10.23·1Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 2 and 87 of 136 in 90% of them.0.6431 in 4
12NavyAmerican Athletic6-0·Power 8.14 (47)·Gap +51.86·14Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 8 and 107 of 136 in 90% of them.0.6411 in 4
13LouisvilleACC5-1·Power 13.47 (12)·Gap +10.84·2Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 4 and 100 of 136 in 90% of them.0.6211 in 4
14MiamiACC5-1·Power 14.65 (9)·Gap +8.28·4Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 2 and 91 of 136 in 90% of them.0.5851 in 4
15VirginiaACC6-1·Power 9.92 (31)·Gap +12.62·4Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 10 and 101 of 136 in 90% of them.0.5491 in 4
16VanderbiltSEC6-1·Power 18.47 (6)·Gap +4.16·5Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 2 and 66 of 136 in 90% of them.0.5461 in 4
17Ole MissSEC6-1·Power 6.56 (51)·Gap +14.97·4Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 12 and 117 of 136 in 90% of them.0.5221 in 3
18IllinoisBig Ten5-2·Power 8.35 (42)·Gap +11.10·3Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 6 and 102 of 136 in 90% of them.0.5031 in 3
19HoustonBig 126-1·Power 6.76 (50)·Gap +14.28·1Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 12 and 118 of 136 in 90% of them.0.4981 in 3
20TulaneAmerican Athletic6-1·Power 5.02 (62)·Gap +15.65·2Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 14 and 120 of 136 in 90% of them.0.4961 in 3
21Arizona StateBig 125-2·Power 8.99 (36)·Gap +10.14·1Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 5 and 98 of 136 in 90% of them.0.4901 in 3
22James MadisonSun Belt6-1·Power 11.47 (21)·Gap +10.03·6Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 4 and 102 of 136 in 90% of them.0.4901 in 3
23North TexasAmerican Athletic6-1·Power 11.88 (19)·Gap +8.06·6Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 4 and 100 of 136 in 90% of them.0.4431 in 3
24Notre DameFBS Independents5-2·Power 13.22 (15)·Gap +4.69·8Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 2 and 91 of 136 in 90% of them.0.4391 in 3
25UNLVMountain West6-1·Power -0.27 (95)·Gap +20.26·8Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 36 and 129 of 136 in 90% of them.0.4361 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.

run 7a38e08e · published 2026-08-19 23:13:04 UTC · code e215160 · config 1ef7cf23…
q_ref 11.26 (Michigan) · β_w 7 · C 32 · h 5.132 · σ 16.473 · λ₁ 150 · λ₂ 0.5 · k 76.66 · w₁ 0.6243 · w₂ 0.0025