Connectivity report · 2025 week 7

What this ranking is standing on.

The poll opened in Week 5. This is week 7; 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 7 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 played840
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.1.000
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.100 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.131

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 100how unlikely1 in this many
1IndianaBig Ten6-0·Power 20.04 (3)·Gap +39.96Ranked 1. The model replayed this schedule 1,000 times, and this team finished between 1 and 62 of 136 in 90% of them.1.6161 in 41
2Ohio StateBig Ten6-0·Power 17.81 (5)·Gap +42.19·2Ranked 2. The model replayed this schedule 1,000 times, and this team finished between 2 and 71 of 136 in 90% of them.1.3121 in 21
3Texas TechBig 126-0·Power 23.80 (1)·Gap +36.20·2Ranked 3. The model replayed this schedule 1,000 times, and this team finished between 1 and 47 of 136 in 90% of them.1.3041 in 20
4Texas A&MSEC6-0·Power 12.86 (16)·Gap +47.14·2Ranked 4. The model replayed this schedule 1,000 times, and this team finished between 3 and 101 of 136 in 90% of them.1.1991 in 16
5MiamiACC5-0·Power 16.63 (9)·Gap +43.37·2Ranked 5. The model replayed this schedule 1,000 times, and this team finished between 2 and 79 of 136 in 90% of them.1.1861 in 15
6BYUBig 126-0·Power 12.53 (17)·Gap +47.47Ranked 6. The model replayed this schedule 1,000 times, and this team finished between 4 and 90 of 136 in 90% of them.1.0101 in 10
7Georgia TechACC6-0·Power 7.53 (44)·Gap +52.47·4Ranked 7. The model replayed this schedule 1,000 times, and this team finished between 7 and 114 of 136 in 90% of them.0.9671 in 9
8AlabamaSEC5-1·Power 17.16 (6)·Gap +11.82·1Ranked 8. The model replayed this schedule 1,000 times, and this team finished between 1 and 89 of 136 in 90% of them.0.9441 in 9
9MemphisAmerican Athletic6-0·Power 13.29 (14)·Gap +46.71·9Ranked 9. The model replayed this schedule 1,000 times, and this team finished between 5 and 93 of 136 in 90% of them.0.8581 in 7
10Ole MissSEC6-0·Power 4.60 (63)·Gap +55.40·2Ranked 10. The model replayed this schedule 1,000 times, and this team finished between 20 and 119 of 136 in 90% of them.0.7691 in 6
11South FloridaAmerican Athletic5-1·Power 11.74 (22)·Gap +15.13·2Ranked 11. The model replayed this schedule 1,000 times, and this team finished between 4 and 100 of 136 in 90% of them.0.7681 in 6
12UNLVMountain West6-0·Power -0.77 (101)·Gap +60.77·4Ranked 12. The model replayed this schedule 1,000 times, and this team finished between 30 and 132 of 136 in 90% of them.0.7611 in 6
13GeorgiaSEC5-1·Power 11.88 (20)·Gap +13.69·1Ranked 13. The model replayed this schedule 1,000 times, and this team finished between 4 and 94 of 136 in 90% of them.0.7251 in 5
14NavyAmerican Athletic6-0·Power 7.57 (43)·Gap +52.43·9Ranked 14. The model replayed this schedule 1,000 times, and this team finished between 9 and 111 of 136 in 90% of them.0.6651 in 5
15UtahBig 125-1·Power 18.19 (4)·Gap +5.03·5Ranked 15. The model replayed this schedule 1,000 times, and this team finished between 1 and 64 of 136 in 90% of them.0.5961 in 4
16IllinoisBig Ten5-2·Power 8.31 (41)·Gap +11.71·3Ranked 16. The model replayed this schedule 1,000 times, and this team finished between 6 and 102 of 136 in 90% of them.0.5391 in 3
17VirginiaACC5-1·Power 11.75 (21)·Gap +10.95·5Ranked 17. The model replayed this schedule 1,000 times, and this team finished between 3 and 85 of 136 in 90% of them.0.5221 in 3
18VanderbiltSEC5-1·Power 21.18 (2)·Gap +1.78·7Ranked 18. The model replayed this schedule 1,000 times, and this team finished between 1 and 74 of 136 in 90% of them.0.5111 in 3
19OregonBig Ten5-1·Power 16.99 (7)·Gap +5.10·9Ranked 19. The model replayed this schedule 1,000 times, and this team finished between 2 and 75 of 136 in 90% of them.0.5071 in 3
20WashingtonBig Ten5-1·Power 11.69 (23)·Gap +9.85·5Ranked 20. The model replayed this schedule 1,000 times, and this team finished between 3 and 103 of 136 in 90% of them.0.5011 in 3
21LSUSEC5-1·Power 7.03 (51)·Gap +14.18·7Ranked 21. The model replayed this schedule 1,000 times, and this team finished between 12 and 105 of 136 in 90% of them.0.4941 in 3
22OklahomaSEC5-1·Power 10.69 (27)·Gap +10.91·5Ranked 22. The model replayed this schedule 1,000 times, and this team finished between 5 and 105 of 136 in 90% of them.0.4921 in 3
23HoustonBig 125-1·Power 7.13 (49)·Gap +13.86·5Ranked 23. The model replayed this schedule 1,000 times, and this team finished between 14 and 110 of 136 in 90% of them.0.4571 in 3
24TulaneAmerican Athletic5-1·Power 4.72 (61)·Gap +15.65·5Ranked 24. The model replayed this schedule 1,000 times, and this team finished between 14 and 124 of 136 in 90% of them.0.4551 in 3
25USCBig Ten5-1·Power 16.31 (10)·Gap +3.49·1Ranked 25. The model replayed this schedule 1,000 times, and this team finished between 3 and 82 of 136 in 90% of them.0.4091 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 06f93f3a · published 2026-08-19 23:12:37 UTC · code e215160 · config 1ef7cf23…
q_ref 10.84 (San Diego State) · β_w 7 · C 32 · h 5.517 · σ 17.319 · λ₁ 125 · λ₂ 1.0 · k 74.78 · w₁ 0.6672 · w₂ -0.0854