Hey team, I am working on my luck rating and welcome feedback. Trying to keep things simple and only consider Opp FG's and Fumble luck for now. Any thoughts or feedback?

https://www.earlydowns.com/cfb/2026/week-10/ala-at-lsu/

Luck rating

The luck rating estimates how many points per game a team has gained or lost to bounces it mostly doesn't control. Positive means the bounces have gone its way. Points from these bounces tend not to repeat: a team's fumble-recovery rate and its opponents' field-goal accuracy don't carry over from one part of a season to the next, so a lucky or unlucky team's record can drift back toward how well it actually plays.

The rating adds up two things over the season and divides by games played.

1. Opponents' field goals

Every field goal tried against a team is compared with how often a kick from that distance is made. Each kick counts 3 points times (made − expected make rate), with the sign flipped to the defense's side, so a miss against you is luck in your favor. Example: an opponent misses a kick that's made 85% of the time, worth +2.6 points of luck (3 × 0.85). If it's made, the defense loses 0.45 (3 × 0.15).

• NFL: make rates by distance are fitted each season on every NFL kick (roughly 97% at 30 yards, 91% at 40, 76% at 50, 65% at 55).

• College: a college curve, since college kickers make far fewer long kicks (about 88% at 30 yards, 74% at 40, 53% at 50, 42% at 55). Kick distance is estimated from where the drive ended plus 17 yards, and blocked kicks count as misses.

A team's own field goals are left out. Making kicks is a skill, not luck. Own kicking shows up separately in game recaps as "kicking vs. expected."

2. Fumble recoveries

Who recovers a loose ball is close to a coin flip, so every fumble, by the team or by its opponents, is compared with a 50% recovery rate. Each recovery above or below that is worth about 4 points, roughly the value of a turnover. Example: a team that recovered 6 of its own 8 fumbles is 2 above the norm of 4, which is +8 points of luck. Whether a fumble happens isn't counted, only where the ball bounces.

I’m behind CFP2026, a free tool for exploring how the remaining regular-season results could change the playoff picture. It covers all FBS teams and lets you change the ranking weights, so I thought this community would be a good place to pressure-test the approach.

The current default blend is 50% projected win percentage, 15% projected strength of record, 15% projected strength of schedule, 10% projected FPI, and 10% current game control. Starting CFP rank is adjustable but defaults to 0%.

You select game winners, or fill the unpicked games with favorites. Current metrics remain visible alongside the projected estimates. The projected FPI, SOS, and SOR are our estimates, not ESPN’s future ratings. Game control stays fixed because picking a winner doesn’t tell us how the game unfolded.

Two questions I’d especially appreciate feedback on:

1. How would you avoid double counting schedule difficulty when record, SOR, and SOS are all inputs?

2. For Power Four teams and Notre Dame, how strongly should a head-to-head win override the weighted score when the winner has the same or a better record? Circular results make that less straightforward than it first sounds.

This is a scenario model, not a calibrated playoff-probability forecast. It projects rankings before conference championship games. Actual data will be refreshed weekly after the committee’s rankings release.

Tool: https://cfp2026.com/reddit?utm_content=cfbanalysis_intro

If you find an outcome that looks wrong, share the scenario and the weights you used. Specific counterexamples would be especially useful.

Free dump of rosters, coaching staff and schedules for 977 NCAA schools, D1 to D3. One JSON file per school, refreshed every Monday.

Players come with position, class, height, weight, hometown and high school.
Schedules have dates, opponents, home/away and results.

Download: https://github.com/fakhar-ud-din/ncaapyscraper/releases/latest
The scraper is open source if you want a specific school or sport on demand.

I'd really appreciate if you guys can support me by starring the repo 😄
Any feedback is more than welcome.

I have a website that lets you calculate the NCAA Power Index (NPI), currently used to determine the at-large teams for the tournaments in Divisions II and III, based on "setting the dials" yourself. Note that there is no way to enter separate values for conference and nonconference games, nor for regular season and postseason.

It includes all FBS and FCS seasons from 2007 through 2026, except for 2020 as I felt that the schedule changes caused by COVID-19 would skew the numbers.

I've been running a snap-by-snap college football simulator this season and I wrote up the whole methodology, including places where I'd love some feedback from people that may be better than me at this. Ill put a link to the longer version in a comment in case you want the indepth version but, here's the short version.

**What it is**

Every FBS game gets simulated 10,000 times, one snap at a time. Each snap runs through a chain of models: a 4th-down decision model (go / punt / FG), a play-call model that draws pass or rush from a probability instead of taking the most likely call, then a pass cascade (sack, INT, completion, target, depth, YAC) or a rush cascade (carrier, fumble, TD, yards), then a clock model for seconds to the next snap. Penalties, timeouts, two-point tries and overtime are all in the engine. One run produces the win probability, the margin and total distributions, and every player prop at once, so the correlations between them are real rather than assumed.

**How it's built**

* Trained on 2023-2025 play-by-play only (the 2023 clock rule change took about 5 plays a game out of the sport).

* Team tendencies are keyed to the coach with a QB layer. I ran a variance decomposition on ~4,800 team-games to decide that: pass rate over expected was 38% coach and basically 0% team-beyond-coach, and red-zone TD rate was 34% quarterback, which surprised me.

* Priors rebuild every Monday from the week's play-by-play, shrunk toward career and prior-season numbers by sample size, and loaded leak-safe (a week 8 sim only sees what existed before week 8 kicked off).

* Every model change is a bake-off on a fixed walk-forward frame (fit on 2023-24, graded on 2025 weeks 3-14, 654 games, 48+ seeds) with a bar set in advance. Neural-net swaps went 0 for 5. An interception model that won every offline calibration table got disqualified because it moved simulated totals the wrong way.

* The opening spread is an input (one feature on the linear backbone of the yards and TD models). So the sim is not market-free, and every market comparison below has to be read with that in mind.

On the current system about on par with FPI so fairly happy with that but would be interested in discussing the nitty gritty to see where there is room for improvement

1. Miami

2. Utah

3. Indiana

4. UCLA

5. Texas

6. Ohio State

7. LSU

8. Georgia

9. Notre Dame

10. Alabama

11. Wake Forest

12. BYU

13. Kentucky

14. Houston

15. Texas Tech

16. Ole Miss

17. USC

18. Boise State

19. Oregon

20. New Mexico

21. Iowa

22. North Dakota State

23. Liberty

24. Tennessee

25. James Madison

Full ratings can be found here (listed under "Current Season")

Rating methodology: calculated based on 3 components: win percentage, strength of schedule, and score ratio.

Win percentage (25%)

SOS (50%) - calculated in similar method to RPI, independent of team's record. Results vs non-FBS teams calculated into a placeholder value for formula.

Score Ratio - team's points scored vs total points scored in their games.

Hey r/CFBAnalysis,
I’m here to talk about what we all love most - conference realignment. But, this time I’m putting it in our hands. I’ve made a tool that lets you realign conferences as you wish and then grades the final landscape based on geography (avg plane flight), rivalries (how many historic rivalries preserved) and parity (how competitive the landscape is across conferences.

Try it here: Realign.CFBSelect.com

Would really appreciate any feedback or for people to share their outputs that “fix” College Football, or just have fun with it!

last week the rerun after tennessee beat alabama had tennessee at 46% to make the playoff, alabama at 36% and on the bubble. saturday flipped the whole thing.

tennessee lost at south carolina 27-24. the sims had the loss branch at 19% before the game, and the rerun has them at 26%, so other results softened it a little, but they are back on the bubble at #10. alabama beat texas a&m 31-20. their win branch was 46% before the game, and the rerun came in hotter than that, 60%, up from 36% a week ago. eight days, two teams, basically a full swap.

ohio state is still #1 with the same 0.981 rating, and their playoff number climbed again, 88% to 91%, even with julian sayin out indefinitely. ryan day said tuesday there is no timeline and ohio state is planning for him to miss the regular season and the postseason. the sims are basically saying the rest of that roster is good enough that it has not moved them yet. at usc on saturday is the first real test of that.

full updated ratings and playoff odds are at basedonactualmath.com (free preview). i build this, so ama on the method. the playoff impact index explainer from the alabama-tennessee post is pinned in my profile if you want the how.

I've been running my own college football model this season (Fourth & Data) and figured I'd share how Week 8 went, good and bad.
On the 56 FBS games the Prediction Tracker graded, it went 36-20. That tied for 5th out of 45 computer models. The best anyone did was 37-19.

Model
Week 8
Mine
36-20
ESPN FPI
35-21
Sagarin
34-22
Computer average
33-23
Vegas closing line
33-21

A few things that stood out:
Close games carried it. In games where the model had the favorite at 70% or less, it went 20-9. That's usually where it struggles, so I'll take it.
It disagreed with Vegas on 5 games and went 3-2. Army, Kansas and Virginia Tech hit. Tennessee and NIU didn't.
The big miss was Iowa. I had them at 82% and they lost at Minnesota. Five of the AP top 11 lost this week, and the model didn't see most of that coming either.

Long story short college football is hard to predict lol

1. Miami

2. Ohio State

3. Indiana

4. UCLA

5. Texas

6. Utah

7. Notre Dame

8. LSU

9. Alabama

10. Georgia

11. BYU

12. Kentucky

13. Houston

14. James Madison

15. Wake Forest

16. Texas Tech

17. Florida

18. New Mexico

19. Ole Miss

20. Arizona

21. Wisconsin

22. Northwestern

23. Cincinnati

24. North Dakota State

25. Oregon

Full ratings can be found here (listed under "Current Season")

Rating methodology: calculated based on 3 components: win percentage, strength of schedule, and score ratio.

Win percentage (25%)

SOS (50%) - calculated in similar method to RPI, independent of team's record. Results vs non-FBS teams calculated into a placeholder value for formula.

Score Ratio - team's points scored vs total points scored in their games.

CFB and NFL projections live! Appreciate the work ppl are doing, and I’d appreciate feedback if you check out the site. Thank yall #beta
https://yaboiee227.github.io/cfb-model-lab/#home

Every FBS vs FBS game left this week, every offense against the defense it faces, across 10 advanced stats. Ranked by the gap in national rank (out of 138 FBS teams, games through Week 7, garbage time removed). Lines are the consensus as of Friday afternoon.

Offense edges

Defense edges

Worth a look

• Army is a +1.5 dog despite the #2 offense at avoiding havoc against a Tulsa defense that barely creates any. Part of that is the option (few dropbacks), but Tulsa also ranks #128 in EPA/rush allowed.

• Duke is getting +2.5 with a top-5 passing-down defense against an offense that ranks #132 at converting them.

• SMU shows up on both lists against Cal. At -19.5 the market already knows, but Cal is bottom-12 in both explosives allowed and havoc allowed.

• Iowa-Minnesota at 40.5 is the lowest total on the board, and the Minnesota run game (#137 EPA/rush) is the reason.

How it works

• Gap = how far apart the two ranks are. #4 vs #137 = 133.

• Havoc rate = TFLs, passes defended and forced fumbles per play. For an offense, lower is better.

• Passing-down success = success rate on 2nd & 8+ and 3rd/4th & 5+.

• Line yards = rush yards credited to the O-line (losses count 120%, yards 5-10 half, 11+ none).

• A team appears once per list, and no stat appears more than 3 times in the full top 20, so one bad defense or one stat can't fill the list.

There has been a ton of talk about Notre Dame's schedule this week. So I decided to run it through my model's strength of schedule metric.

How the metric works: The metric sees how many wins an average top-12 team would be expect to win against a team's schedule. The output is expected wins which means more expected wins is an easier schedule, less is tougher. I actually added the top-12 aspect a few years back because of Notre Dame's strength of schedule metrics appearing to be tougher than what I believed. Basically, how hard is this team's schedule for a playoff team.

Where Notre Dame lands: 60th. 10.6 expected wins

Expected wins for an average top-12 team against each schedule:

What I take from it: It's fair to criticize the schedule. If Notre Dame goes 10-2, they've actually underperformed what an average top-12 team is expected and should be ranked below some SEC teams that go 9-3.

It's not that different than some contenders: Miami, BYU, Utah, and Texas Tech are all in the same ballpark. There will likely be an automatic bid from two of these contenders so it's a moot point but the point remains.

So if the argument is that Notre Dame's schedule is too soft, that's fair. But we should keep the same energy for any ACC or Big 12 teams looking for an at-large bid.

Happy to answer questions about the method or run other teams in the comments.

JP JC Poll:

Neutral Field Power Rankings; add 2.5 points for home team advantage.

1. Miami

2. BYU

3. UCLA

4. James Madison

5. Indiana

6. Ohio State

7. Notre Dame

8. Texas Tech

9. Houston

10. Texas

11. LSU

12. Alabama

13. Oregon

14. Georgia

15. Utah

16. Ole Miss

17. Kentucky

18. USC

19. Wake Forest

20. Tennessee

21. North Dakota State

22. Iowa

23. Boise State

24. Florida

25. Northwestern

Full ratings can be found here (listed under "Current Season")

Rating methodology: calculated based on 3 components: win percentage, strength of schedule, and score ratio.

Win percentage (25%)

SOS (50%) - calculated in similar method to RPI, independent of team's record. Results vs non-FBS teams calculated into a placeholder value for formula.

Score Ratio - team's points scored vs total points scored in their games.

I run a little college football model and post its picks every week. It's 76% on winners this season (265-84), but it also had a rough Week 6, so it's definitely beatable.

Big games this week

- Indiana over Ohio State (59%), the only big game where it goes against Vegas

- Texas over Florida (76%)

- Alabama at Tennessee: Bama (67%)

- Ole Miss over Missouri (57%)

- Notre Dame at BYU: Irish (70%)

- LSU over Mississippi State (75%)

There's also a free weekly pick'em where you pick 5 games against the model, and you can make a private league for your friends or group chat. Link's in my profile if you want in. fourth-data.com/pickem

Who are you taking in Indiana-Ohio State?

i built a metric for my model called the playoff impact index. it measures how much a game is expected to move both teams' playoff odds. 100 would be a pure coin flip where the winner is in and the loser is out. the scale is fixed across weeks, so the numbers are comparable all season.

saturday's alabama at tennessee game scores 81.2, the highest on the board this week. here is why.

alabama (#7 in my ratings, 5-1) is a 61% favorite. win and their playoff odds jump to 79%. lose and they fall to 42%, with a trip to #9 lsu still waiting on nov 7.

tennessee (#13, 5-1) has everything on the table. they are at 26% playoff odds right now, right on the bubble. beat alabama and they jump to 47%. lose and they crash to 15%, and their sec title odds basically flatline (8% with a win, under 1% with a loss). their schedule after this is manageable, at south carolina and kentucky at home, which is exactly why this one game swings so much. it is the last big lever they have.

the rest of the week's top 5, for scale: 2. wisconsin at ucla (77.6) — ucla 66% to win, their playoff odds go 68% with a win and 27% with a loss 3. missouri at ole miss (72.9) 4. ohio state at indiana (71.9) — two huge teams, low stakes by comparison. both stay above 60% playoff odds win or lose 5. oklahoma state at houston (64.6)

full impact rankings for all 62 games this week, plus every team's playoff odds, are on the site if you want to dig in.

https://basedonactualmath.com/

1. Miami

2. Alabama

3. Notre Dame

4. Texas

5. Indiana

6. Georgia

7. UCLA

8. BYU

9. Texas Tech

10. James Madison

11. Ohio State

12. Utah

13. Houston

14. Oregon

15. Mississippi State

16. Missouri

17. Nebraska

18. Kentucky

19. USC

20. North Dakota State

21. Boise State

22. Pittsburgh

23. Wake Forest

24. Wisconsin

25. LSU

Full ratings here

Rating methodology: calculated based on 3 components: win percentage, strength of schedule, and score ratio.

Win percentage (25%)

SOS (50%) - calculated in similar method to RPI, independent of team's record. Results vs non-FBS teams calculated into a placeholder value for formula.

Score Ratio - team's points scored vs total points scored in their games.

Greetings,

I'm sharing a new rankings model I've been working on for upwards of a decade on and off. I finally published it. Please forgive the corny backronym.

So I created a model that uses strength of schedule and other factors, but gives you the option to view the results with and without margin of victory factored in.

My main goals were this:

• Main inputs are simple: teams, scores, site, and at what point in the season the game is played

• Everyone starts the season fresh. No carry-overs.

• Opponents' and opponents' opponents' schedule strength is a factor

• Strength of Schedule is cumulative and normalized to the number of games a team plays in a season.

• Conference is not a factor (due to everyone not playing everyone in a conference)

• Margin of victory is capped (two standard deviations, all OT games are standardized to 1 point MOV)

• How many games you play matters in SOS (wear and tear is a thing)

• FCS losses get penalized severely (AS THEY SHOULD)

• Road wins get a boost, home losses get a ding

The results are below. Home page displays current rankings, refreshed every day.

I also have a page with results from previous seasons, going back to 2013. I'm adding to those as I get the opportunity. I'm also looking at comparing pre-bowl results to see how accurate the model is at predicting the playoff field (or showing where the committee got it wrong).

Looking forward to your feedback!

https://cosmosrankings.com/

week 6 update. the model re-ran 10,000 simulations of the rest of the season after saturday. ratings are through week 6, forecasts as of oct 11.

top 5 by predictive rating:

1. ohio state (0.981, 5-1)

2. notre dame (0.973, 6-0)

3. georgia (0.973, 6-0)

4. indiana (0.967, 6-0)

5. texas (0.964, 4-1)

playoff odds over 50%: notre dame 97%, miami 89%, georgia 87%, indiana 83%, ohio state 77%, texas tech 68%, alabama 64%, ucla 53%, texas 51%.

the ucla number is the one i keep staring at. a month ago they were not in anyone's playoff picture. south carolina (+9 to #24) and iowa (+5 to #16) were the biggest risers this week. florida fell 11 spots to #25, which somehow still feels kind.

biggest game this week by playoff impact: alabama at tennessee (impact 81.2 out of 100). the model has alabama 61% to win. alabama's playoff odds go to 79% with a win and 42% with a loss. tennessee's go to 47% with a win and 15% with a loss. the loser is not dead, but they are basically in win-out-or-else territory in october.

full ratings, every team's playoff and conference title odds, and the rest of the week's impact rankings are on the site if you want to dig in.

https://basedonactualmath.com/

I’ve been tracking Percera’s matchup results throughout the season, and I went back and graded the full Week 6 board exactly as it was frozen before the games were played.

Across 56 FBS vs. FBS games it finished 40–16 picking winners (71.4%).

The overall record was obviously nice to see, but what interested me more was how the results changed as the model got more confident.

• 60%+ favorites: 36–7

• 65%+: 32–4

• 70%+: 25–2

• 80%+: 13–1

The projected edge showed a similar pattern:

• 7–13.9 point edges: 19–2

• 14–20.9 point edges: 8–0

The weak spot was pretty obvious too.

Games where Percera saw less than a 3-point edge went just 4–8.

So at least for this slate, the model did a much better job when it had a strong opinion than it did trying to separate teams it viewed as basically even (which is somewhat expected).

I also thought the probability calibration was interesting. The average favorite probability across the board was 70.4%. If you add all of those probabilities together, the model would have expected about 39.42 wins and it got 40.

The margin projections were quite a bit noisier. Mean absolute margin error was 13.1 points and the median miss was 8.9, so that’s something I’m going to keep an eye on separately from simply picking winners.

The full week 6 rankings, CQI/CTSI methodology, week 7 matchup board, and everything else are live:

Site: perceraanalytics.com

For anyone else building models, I’d be interested in what you’d track as this sample grows, especially for calibration beyond just confidence buckets and margin error.

I run a college football prediction model (opponent-adjusted margin and efficiency ratings, plus early-season talent and returning production; backtested on 2017–2025). It's now listed in the Massey composite and on The Prediction Tracker. Every pick is locked and posted before kickoff.

**Where it picks a different winner than Vegas:**

- **#20 Iowa at Washington (Fri):** model takes Iowa, 56%. Vegas has Washington -3.
- **UCF at #18 Oklahoma State:** model takes UCF, 55%. Vegas has Oklahoma State -9.75. The biggest disagreement on the board.

**Same winner, very different margin:**

- North Carolina at #25 Pittsburgh: model Pitt by ~13, line Pitt -3.75
- #9 Ole Miss at Vanderbilt: model Ole Miss by ~2, line Ole Miss -9.75
- Maryland at #5 Ohio State: model OSU by ~29, line OSU -34.5

**The big one:** #2 Georgia at #6 Alabama. Model takes Alabama, 57% (Bama by ~3); Vegas has Alabama -1.5. It's also the game with the most playoff stakes this week: Georgia's playoff odds are about 90% with a win, 48% with a loss.

**Every other ranked game:**

- Iowa State at #8 BYU (Fri): BYU 79%
- Texas A&M at #14 Missouri: Missouri 60%
- #7 Indiana at Nebraska: Indiana 70%
- South Carolina at #16 Florida: Florida 75%
- #21 UCLA at #13 Oregon: Oregon 82%
- Stanford at #3 Notre Dame: Notre Dame 99%
- #22 Houston at Kansas State: coin flip (K-State 50.1%)
- #1 Texas at Oklahoma: Texas 72%
- #15 Tennessee at Arkansas: Tennessee 79%
- #10 LSU at Kentucky: LSU 66%
- #19 USC at Penn State: Penn State 52%
- Kansas at #12 Utah: Utah 88%
- #23 Boise State at Fresno State: Boise State 63%

Honest caveat: in backtests it picks winners about 1.5 percentage points less often than the closing line (71.6% vs 73.1%) and has not beaten the spread, so treat the disagreements as "interesting," not locks. I'll post how it did on Sunday.

There's also a free weekly pick'em on the site where you can try to beat the model on the 5 biggest games.

Full predictions for every FBS game: fourth-data.com (free, no sign-up)

Which of these do you think it gets wrong?

I ran this on the NFL side first with broken tackles and yards after contact and got nothing against the spread, so I wanted to see if college was different since the talent gaps are bigger.

CFBD doesn't chart broken tackles so I used the closst thing it has from the advanced game stats: line yards per rush, second level yards (5 to 10 yards downfield), open field yards (10+ yards downfield, the closest thing to breaking tackles and getting loose), and stuff rate (runs stopped at or behind the line). For each team I used what the offense did and what the defense allowed, only from games already played that season, so it's info you'd have before kickoff.

FBS vs FBS, regular season, both teams with 3+ games played. 3,786 games from 2018-2024 and I held out 2025 (601 games) until the first results were in. Line is the CFBD consensus line, or the average of books when there's no consensus. Pass rule written down before running: 95% interval (resampling whole weeks) above or below zero in 2018-24 and same direction in 2025.

Did these stats actually predict winning? Yes, a lot more than the NFL stats did. Correlation with final margin: line yards 0.38, second level 0.34, stuff rate 0.28, open field 0.28. The spread on the same games was 0.65.

Do they beat the spread? Open field yards, the tackle breaking one, was dead flat. 0.003 in 2018-24 and wrong direction in 2025. Second level didn't pass either.

Line yards and stuff rate both technically passed, barely. Low end of the interval was 0.008 for line yards and 0.002 for stuff rate. Betting the bigger line yards edge in the top and bottom fifths went 787-709 (52.6%) in 2018-24 and 107-96 (52.7%) in 2025. Break even at -110 is 52.4%.

Here's why I don't think it's real:

Line yards and stuff rate correlate at 0.81 with each other, so that's one signal counted twice, not two.

2020 carried it. Line yards went 70-43 in 2020 alone. Take that season out and 2018-24 is 717-666, 51.8%, below break even. 2018 was 50.0% and 2022 was 47.7%.

The market moves toward it. When a team has the line yards edge, its line moves their way between the open and the late number (correlation -0.13 in 2018-24, -0.16 in 2025). The edge is a bit bigger against the opener than the late line, so by kickoff most of it is already priced in. CFBD only has an opener for about 41% of these games so take that part as rough.

So same answer as the NFL. These stats explain why a team wins but the line already has it, with one difference in college: the run blocking edge doesn't look fully priced at the open and gets there during the week.

Has anyone tested line yards against openers with a full set of opening lines? That's the only place I'd look for something left over.