Connectivity report · 2025 week 9

What this ranking is standing on.

The poll opened in Week 5. This is week 9; 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 9 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 played1065
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.93 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.113

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 93how unlikely1 in this many
1Texas A&MSEC8-0·Power 17.06 (8)·Gap +42.94Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 2 and 65 of 136 in 90% of them.1.7841 in 61
2IndianaBig Ten8-0·Power 23.21 (1)·Gap +36.79Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 1 and 46 of 136 in 90% of them.1.6871 in 49
3BYUBig 128-0·Power 15.95 (11)·Gap +44.05Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 3 and 71 of 136 in 90% of them.1.6771 in 48
4Ohio StateBig Ten7-0·Power 22.39 (2)·Gap +37.61Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 2 and 55 of 136 in 90% of them.1.3651 in 23
5Georgia TechACC8-0·Power 12.28 (27)·Gap +47.72·3Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 5 and 89 of 136 in 90% of them.1.2591 in 18
6AlabamaSEC7-1·Power 17.82 (7)·Gap +12.46·1Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 2 and 77 of 136 in 90% of them.1.2071 in 16
7GeorgiaSEC6-1·Power 13.46 (18)·Gap +13.49·2Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 4 and 97 of 136 in 90% of them.0.8211 in 7
8Ole MissSEC7-1·Power 9.49 (39)·Gap +16.60·1Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 7 and 101 of 136 in 90% of them.0.7861 in 6
9OregonBig Ten7-1·Power 20.17 (5)·Gap +5.29·3Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 1 and 50 of 136 in 90% of them.0.7211 in 5
10Texas TechBig 127-1·Power 21.34 (4)·Gap +5.78·3Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 1 and 58 of 136 in 90% of them.0.7181 in 5
11HoustonBig 127-1·Power 10.10 (37)·Gap +14.28·1Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 7 and 101 of 136 in 90% of them.0.6751 in 5
12NavyAmerican Athletic7-0·Power 9.14 (43)·Gap +50.86·13Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 8 and 97 of 136 in 90% of them.0.6481 in 4
13LouisvilleACC6-1·Power 13.46 (17)·Gap +12.00·2Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 6 and 92 of 136 in 90% of them.0.6401 in 4
14VanderbiltSEC7-1·Power 18.64 (6)·Gap +5.79Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 2 and 58 of 136 in 90% of them.0.6181 in 4
15MiamiACC6-1·Power 16.97 (9)·Gap +7.10·5Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 2 and 82 of 136 in 90% of them.0.6161 in 4
16VirginiaACC7-1·Power 10.22 (35)·Gap +13.36·1Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 9 and 107 of 136 in 90% of them.0.5871 in 4
17South FloridaAmerican Athletic6-2·Power 13.71 (16)·Gap +6.92·1Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 3 and 89 of 136 in 90% of them.0.5271 in 3
18MichiganBig Ten6-2·Power 13.99 (14)·Gap +5.74·1Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 2 and 87 of 136 in 90% of them.0.4851 in 3
19North TexasAmerican Athletic7-1·Power 13.35 (19)·Gap +8.06·3Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 4 and 86 of 136 in 90% of them.0.4851 in 3
20TulaneAmerican Athletic6-1·Power 6.60 (58)·Gap +14.97Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 14 and 115 of 136 in 90% of them.0.4821 in 3
21TexasSEC6-2·Power 12.88 (22)·Gap +7.15·3Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 4 and 93 of 136 in 90% of them.0.4711 in 3
22WashingtonBig Ten6-2·Power 13.98 (15)·Gap +5.34·1Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 3 and 88 of 136 in 90% of them.0.4671 in 3
23MemphisAmerican Athletic7-1·Power 12.52 (26)·Gap +8.24·5Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 4 and 88 of 136 in 90% of them.0.4511 in 3
24Notre DameFBS Independents5-2·Power 15.36 (13)·Gap +3.69·7Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 2 and 85 of 136 in 90% of them.0.4371 in 3
25UtahBig 126-2·Power 21.82 (3)·Gap -3.10·1Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 1 and 44 of 136 in 90% of them.0.4331 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 4c34ddec · published 2026-08-19 23:13:35 UTC · code e215160 · config 1ef7cf23…
q_ref 12.53 (Old Dominion) · β_w 7 · C 32 · h 4.864 · σ 16.105 · λ₁ 150 · λ₂ 0.5 · k 73.92 · w₁ 0.5837 · w₂ 0.0993