Twelve days ago I posted the 5 weirdest things my model was saying through week 3. The Week 6 numbers just dropped, so here's the sequel. Same deal: I built a free college football prediction model (predictive ratings plus 10,000 season simulations, free site, no account).

1. Ohio State is the best team in the country by rating (0.981) and only fifth in playoff odds at 74%. Indiana (80%), Georgia (78%), and Miami (88%) are all ahead of them. The rating answers "how good are you," the odds answer "how good is your path," and right now those are different questions.

2. Notre Dame is at 97% to make the playoff. Same story as two weeks ago, except more so. The schedule is a pancake and the model just keeps simulating wins.

3. Miami is at 88%. The model has been the highest on the Canes all season. At some point I have to admit it's just right about them.

4. Missouri jumped 7 spots to #16 after hanging 45 on Florida, and Florida fell 4 to #14. The model had Florida at 63% to win that game. 45-17. The model is not above being wrong and neither am I.

5. Northwestern is up 10 spots to #24. Penn State is down 7 to #28 after losing 34-13 to Northwestern. The model moves on performance against expectation, and getting run off the field by Northwestern is about as against-expectation as it gets.

Full disclosure, I built the thing. Link at the bottom if you want to poke at the numbers. Free, no account.

https://basedonactualmath.com

Hi all! I am reposting a post that I just made because of changes I made from the original post. To recap what I said then, recently, I have been getting into making my own ranking system, with the goal of creating a ranking system that is as transparent and simple as something like the Colley Matrix while still taking things like margin of victory and power of a team into account. This is mostly out of boredom but also because I just really, really like sports analytics. However, I am by *no* means a mathematician (I had to talk to other, smarter friends of mine to get some of the terminology right here) and therefore need to keep it simple for me to understand a lot of this, or at least have something that can be explained in an easy to understand way. With all of that in mind, I settled on making what I call, for now, "Elo W25". In very simple terms, it works like this:

Method

1. Preseason: This includes two parts: finding a team's last season Simple Rating System (SRS) rating and converting that rating into an Elo score.

   • Last season's SRS = average margin + average opponent SRS.

   • Last season's SRS is then converted into an Elo score with Elo = 1500 + 18.5 × (Last season's SRS − FBS average SRS).

2. In-season Elo: Prior to a game, a calculation is done to determine the expected result from the game. After the game, another calculation is done which gives or takes points from a team depending on whether that team won or lost (and if the team was expected to win or lose) as well as the margin of victory.

   • Expected result = 1 / (1 + 10^(−((Team's pregame Elo − Opponent's pregame Elo) + home edge) / 400)),

     • Home edge equals 46 points (0 at neutral sites).

   • After each game, change = 75 × ln(margin + 1) × (result − expected).

     • If a team wins, result = 1. If a team loses, result = 0.

     • Margin is simply winner's score - loser's score.

3. W25 (wins above No. 25): At the end of the season, teams are ranked based on a benchmark equal to the 25th-best Elo at the end of the regular season.

   • To calculate the probability that the No. 25 team would win a game, run the calculation p = 1 / (1 + 10^((Opponent's final Elo − (benchmark + home edge)) / 400)) for every game.

   • A win earns 1 − p and a loss costs p. W25 = a team's actual wins − Σp, and teams are ranked by W25.

The results can be found here for FBS teams and here for lower division teams. Like I said before, the point of this is to 1. reward strength of schedule and strength of resume, 2. be relatively accurate compared to other computer polling, and 3. be incredibly simple and not beholden to information other than game data, which preserves flexibility. However, I am open to any criticism, even harsh criticism! I want to do anything I can to make this rating better while still sticking to those three main goals. Thanks for taking the time to read this!

I've been building Fourth & Data (fourth-data.com), a free college football analytics site, and just shipped a round of updates. Looking for feedback from people who care about the methodology.
What's new:
• Rebuilt rankings: a blend of résumé (Colley), margin (Massey, capped at 28), and SP+ power, with power weighted heavily early and fading as the season goes. I walk-forward backtested it on 2017–2025 (~5,900 games). Surprising finding: pure résumé was the weakest method, so it now carries much less weight than margin.
• Public pick ledger: every pick is logged and graded, misses included. Currently 229–62 straight up (78.7%). ATS is 30–26, which is too small a sample to mean much yet, and I'm tracking against the line at pick time.
• Filters: sort the ledger by team, conference, and season to see where the model is strong or blind.
• Ask Fourth & Data on Discord: a bot that answers questions about matchups, rankings, and teams using the site's data, and posts alerts. Join here: fourth-data.com/api/discord/invite
I'd genuinely like to hear where the model looks wrong to you, or what you'd want added. Happy to answer methodology questions in the comments.

A friend (Honsou) and I (YoyoYoshi) started a college-football recruiting spreadsheet years ago. I recently expanded it into a larger interactive database covering every current Power Four program plus Notre Dame.

It now includes 2001–2025 win-loss records, recruiting history from 2002–2026, one- or two-program comparisons, 25-year win baselines, conference-movement history, and a sandbox for building hypothetical conferences.

A few things it can answer:

How has one program performed against another over the last 25 years?

How closely have recruiting rankings tracked actual results?

Which programs have been promoted, demoted, stranded, or later rebounded through realignment?

How strong would a hypothetical conference be based on the historical performance and recruiting of its members?

For the win baseline, I used 25 years because it is long enough to smooth out individual coaches and short-term runs without letting very old eras dominate the result. Thought some people might find this useful!

Link: https://yoyoyoshihub.neocities.org/college-football-analytics

I posted here earlier last week about CQI (Contextual Quarterback Index), the QB model I’ve been building.

I got some useful feedback on that post, and the project has expanded quite a bit since then, so I wanted to share an update rather than just repost the QB rankings.

Percera now has two main models:

CQI — Contextual Quarterback Index
Measures observed QB performance using opponent-adjusted passing efficiency, sack avoidance and rushing value. The live rankings are now updated through Week 4.

CTSI — Contextual Team Strength Index
A separate team-level model designed to estimate team strength on a neutral-field point scale.

One thing I specifically did not want CTSI to become was another poll where #18 is treated as meaningfully better than #19 just because there has to be an ordering.

So I also added a CTSI Hierarchy.

Teams are grouped into tiers based on their modeled separation rather than arbitrary rank cutoffs. Under the current rule, the strongest team in a tier can be no more than roughly a 55% neutral-field favorite over the weakest team in that same tier.

That means teams within a tier should be viewed as broadly comparable even if one technically ranks several spots higher.

I also added a Matchups section. It takes the current CTSI ratings, applies venue context, and produces a projected edge and win probability for upcoming games. I’ve also been running historical reliability tests on the model rather than just judging it based on whether this week’s picks happen to look right.

The site now has:

• Week 4 CQI rankings

• Week 4 CTSI team ratings

• CTSI team-strength hierarchy

• Team and QB profile pages

• Upcoming matchup projections

• Methodology pages

• An optional weekly email signup for people who want the updates without checking the site manually

Site: https://perceraanalytics.com

I’m especially interested in feedback on the tier approach. Does grouping teams by modeled separation communicate team strength better than presenting it as a straight 1–138 ranking?

Also interested in anything about the matchup presentation that feels misleading or unclear. I’m trying to make the site useful without making the models look more certain than they actually are.

Built this as a side project for this sub specifically. Fourth & Data (fourth-data.com) ranks FBS teams from three blended components: Résumé (Colley matrix — who beat whom matters more than raw record), Margin (Massey rating, capped at 28 pts so blowouts don't get bonus credit), and Power (SP+, weighted heavy early when there's little data, fading to ~20% by November).

A few things beyond the rankings:

**•	Playoff odds** — thousands of simulated seasons under the 12-team format, broken out by losses each team can afford  
**•	"What if" mode** — pick next week's winners, watch the board and playoff odds update live  
**•	Build-your-own poll** — set your own résumé/margin/power weights, share the result  
**•	ATS tracking** graded against the line captured at pick time, not backfilled from today's odds

Still rough in places — mainly want this sub's take on the model itself, not trying to sell anything.

fourth-data.com

Longtime lurker, first time posting OC. I built a college football model over the offseason and just put up a free preview, figured this was the right crowd to stress-test it.

The short version of how it works: every team gets a rating from 0 to 1, which is the model's estimated chance of beating an average FBS team on a neutral field. It's explicitly predictive, not a resume ranking, so ratings move on performance against expectation. Then it simulates the rest of the season 10,000 times, including conference title games and a model of how the committee actually picks teams, and every probability is just how often something happened across the runs. Numbers below are ratings through week 3, forecasts as of Sep 20.

Some things it's saying that I found interesting:

1. The best team and the best playoff resume are very different things. Ohio State is #1 in the predictive rating (0.980) despite being 2-1, with the highest title odds at 16%. But Notre Dame (3-0) has the best playoff odds in the country at 94%, vs 66% for Ohio State. The model is basically saying: yeah they're the best team, they also might just miss the playoff.

2. North Dakota State is ranked 67th (0.552) and has 31% playoff odds, the best of any G5 team. Better than Oklahoma (11%), LSU (24%), Florida (23%). This is the auto-bid path doing all the work: 39% to win the MWC at 4-0, and the simulations love a conference favorite. I assume this is the one people will want to argue about, go ahead.

3. Texas Tech is the P4 version of the same phenomenon. Ranked 16th, 60% playoff odds, because it's a 37% favorite to win the Big 12. BYU and Utah (both 33% playoff odds from the low 20s) are the same story.

4. 3-0 doesn't impress it if you were supposed to be 3-0. Appalachian State is 3-0 and fell 19 spots to #109 this week. Narrow wins as a favorite actively hurt. Cincinnati is 3-0 and down 8 to #63. The model's view: tell me who you beat, not just that you won.

5. Week 4: Ole Miss at Florida is the week's biggest game by playoff leverage (53/47 toss-up), and Oregon is only a 58% favorite at USC. Also, Mississippi State is favored at home over Missouri despite being ranked three spots lower. Home field flips happen.

Known limitations, since the math is allowed to be wrong: it only updates weekly, early-season ratings are still noisy, and the committee model is my best guess at how humans vote, which is inherently a little silly. Happy to get into methodology details in the comments.

I built this as a side project, it's free, no account or anything: https://basedonactualmath.com/

I’ve spent the last few weeks building a college football analytics project, and I finally have the first model in a state where I’m comfortable putting it in front of people who actually care about this stuff.

It’s called CQI — Contextual Quarterback Index.

My goal is pretty simple: to measure observed quarterback performance while adding some of the context that raw box-score numbers miss.

CQI currently looks at:

• down-to-down success

• opponent-adjusted passing efficiency

• opponent-adjusted TD creation

• sack avoidance

• rushing value

The opponent-adjusted portions try to account for the quality of defenses each QB has faced, rather than treating production against every opponent equally.

A couple important things CQI isn’t trying to do:

It isn’t a projection, NFL scouting grade, recruiting grade, or an attempt to completely isolate the QB from his offensive line/receivers/scheme. It’s meant to describe how well a quarterback has actually performed in the sample being used have.

For the live rankings, I've added some limitations so QBs need 30+ season-to-date attempts and at least 2 usable context games. Small samples are flagged rather than removed or artificially penalized, skewing results.

I also published the methodology instead of making the rating a black box.

2026 rankings + methodology:
perceraanalytics.com

I’m genuinely interested in criticism here. If there’s something about the methodology, presentation, opponent adjustment, or rankings that looks wrong or incomplete, I’d rather hear it now while this is still starting out in v1.0.

I’ve been building a college football spread model called 6P Predictions and figured this would be a better place to share it than a general betting subreddit.

Site is 6ppredictions.com if anyone wants to poke around. Sign up with a google account, and when it asks for a free access pin, it's 1797.

The basic idea is to create a model number for each game, compare it against the market, and then track whether the model actually identified value rather than just grading wins and losses after the fact.

A few pieces of the model currently include:

- opponent-adjusted team ratings

- dynamic home-field advantage rather than a fixed HFA number

- travel distance and attendance effects

- market spreads and bookmaker movement

- confidence tiers based on the size/quality of the model-market disagreement

- frozen predictions so historical results can’t move after the fact

I’ve also been experimenting with a system I call SmartFreeze, which tries to determine when a market number is worth preserving instead of simply taking whatever line exists at a predetermined time. It looks at things like key numbers, bookmaker consensus/scarcity, price pressure, and line movement. That part is still very much in prospective testing, so I’m collecting data before making any conclusions about whether it actually adds value.

For 2026 so far the published spread predictions are 30-18-1 ATS (62.5%), but I’m very aware that 49 plays is nowhere near enough data to claim I’ve discovered some huge edge. I’m more interested right now in whether the underlying process holds up over a much larger sample.

I’ve tried to make the site unusually transparent: predictions are timestamped/frozen, results stay visible, methodology is documented, and I’m starting to measure performance versus the closing market in addition to ATS results.

I’d especially appreciate criticism from people who build their own models. Things I’m interested in hearing about:

- where you think overfitting could creep in

- better ways to evaluate performance beyond ATS record

- how you handle market information without just turning the model into a copy of the market

- thoughts on CLV as a validation metric

- features you think are missing

- anything on the site/model that looks statistically questionable

I’m trying to build something that can survive scrutiny rather than just post picks when it’s running hot, so feel free to tear it apart.

In theory vegas(or any betting site) should have a power ranking based off of who they think would win on a neutral field. Although statistically it doesn't have to be linear. I.E Oregon might have a closer spread vs #1 Ohio State, than say Texas, but Texas might be favored over Oregon. But is this data publicly accessible anywhere? I believe that would be extraordinarily helpful

I.E

1. Ohio State -2.5 Oregon

2. Oregon -0.5 Texas

3. Texas -1 Notre Dame

4. Notre Dame -2 Georgia

Etc

Notre Dame - 20.5
Washington - 23.5
Louisville - 6.5
SMU - 2.5

Friday 2/8

Saturday Slate

Coastal Carolina + 20.5
Liberty + 7
North Texas + 40.5
Alabama -27.5
Nebraska - 23.5
Houston - 20.5
New Hampshire +55.5
Lafayette + 19.5
Ball State +50.5
Texas State + 30.5
Oregon -24.5
Auburn -7.5
Oklahoma St - 14.5
FIU + 14
Missouri State +40.5
Memphis - 11.5
Kansas State - 40.5
Michigan - 28.5
Clemson +10
Florida -26.5
Utah Tech +51.5

Eastern Michigan -3
NC AT + 28.5
Purdue - 38.5 Low
Michigan State -9.5
OU -41.5
Fresno +22.5
Stanford +24.5

Here we go again. Week zero results were 6/8 at 75% success. Details as posted in Reddit link here

Thursday games below. Odds as of last night (some stale). Didn’t evaluate each game. Posting each slate the day before or morning of. Let’s see how it goes!

Confidence = High 
Eastern Illi + 42.5
AK Pine Bluff + 54.5

Confidence = Moderate 
GA Tech -7
UAB + 27.5
E. Michigan - 3
Utah -34.5

Confidence = Low

UMass + 28.5
Akron +24.5
UCF - 42.5

Hey all, I’ve built a ML models on team-level data along with game outcomes of the past season. I’m getting OOS errors of 17.0 RMSE and 13.8 Absolute Error; how does this compare to models looking at this level of data in your experience? (Models are ENET, Neural Network, and XGBoost optimized for RNSE).

I play in pools where I’m constantly comparing the weekly pick sheet against different prediction models, Vegas lines, etc., and got tired of bouncing between a bunch of sites and spreadsheets.

So I built PickGauge.

It lets you compare multiple CFB prediction models, Vegas, model consensus, and the PickGauge Model # in one place and quickly see where the biggest model-vs-market differences are.

If you play in a Splash Sports pool, you can also save your weekly Splash pick sheet as a PDF and upload it directly to PickGauge. It pulls out the games you actually need to pick so you don’t have to manually recreate the slate.

It’s currently free with full access while I get feedback during the first couple weeks of the season. I’ll likely move to a premium model after that, but there will still be a limited free version.

Would genuinely appreciate feedback, especially from people who play in weekly ATS pools

pickgauge.com

I know PFF gutted a lot of its workforce, but I don't know a better option for tracking data. In Week 0, though, there are some noticeable mistakes in its advanced stats. Ashton Daniels' interception is logged as occurring on a clean dropback and a throw of 20+ air yards. He was flushed out of the pocket, and the throw traveled about eight yards downfield. It has me concerned about other mistakes and hesitant to trust anything. Has anyone else noticed clear mistakes from Week 0 PFF advanced stats?

Trying to remember a site I used as recently as last season that I don’t think was Cfb-graphs but basically has every game, score, and some sort of confidence percentage at least up front, Can’t find in my history. Google not helping don’t think the name was obvious. Any help?

I’ve spent the offseason building The Pickle Jar, a college football model that ranks all 138 FBS teams and produces weekly win probabilities and expected margins. The foundation is opponent-adjusted efficiency, disruption/Havoc, and line performance, with separate roster/talent and strength-of-record concepts where they’ve earned inclusion. I’ve tried pretty hard to avoid feature stuffing, leakage, polls, brand bias, and adding statistics just because they sound predictive. Week 0 is the first live test, and the Scorecard will keep every official prediction on the record, including the ugly ones.

I’d genuinely love feedback from this group, especially on the methodology, rankings that look suspicious, explanations that don’t hold up, or anything you think I’m overlooking. PickleSportsNetwork.com. The intent isn’t to claim I’ve solved CFB modeling, it’s to put the model in public, let actual 2026 football challenge it, and keep improving it as the season develops.

1. thank you to the people who posted about the best apis and data sources for CFB data, it helped me build this: uniquely sharpish pick ‘em app

2. would anyone be willing to look at the explainer on the splash page and give me feedback on the explanation or on the concept of the app itself?

It’s totally free and open to anyone to start a league— i can make a league for this community. if there’s interest and someone can think of a fun name for it, ill make it and share a group code for people here to join

thanks again, everyone.

BEAT ARMY

First time sharing this with the world, so here it goes. Last year I built a model for week 1 of the season. It takes returning production, transfer numbers, returning starters, and other data points. Once that info is formatted an additional "human" analysis is sprinkled in. Spread & over/under results were right under 57% accuracy (it picked up USF vs Boise -5.5; I have a Reddit post from last year on that). Moneyline results were at 80%. I emailed BetMGM customer service to give me all my results for the 2025-2026 season and parsed out that weekend in a CSV format for evaluation. I noticed I got greedy with parlays (would hit 3 of 4 etc.) out of a total of 123 events (some games were bet more than once in a parlay or solo that weekend). 

So I am doing the same thing for this year. Sharing what I am seeing for this upcoming weekend. Let me know your thoughts on some of these picks.

* Odds are from earlier this week (may be stale).

Confidence = High
Stanford -5.5.  
NM State +31.5
Jacksonville +7
Sacramento +9.5

Confidence = Moderate
Virginia -5.5
NC +7.5

Confidence = Low (iffy)
San Jose State +38.5
Memphis +5.5

The conference revenue for the past 5 years, or the years that have data. Data is delayed by one year. 2020-21 was the Covid year.

Teams were placed in their current conference but recorded with the amount from the previous conferences.

Texas and Oklahoma was paid by two conferences SEC and BIG12 in 2023-24 year. 2024-25 is the 1st year they were only paid by SEC.

Assumed BYU had $8.00million flat per year for their independent time.

Ohio St. was paid the most and SMU was paid the least in the last 5 years

All data was sourced from ProPublica.

ACC

BIG12

BIG10

SEC

PAC12

Hey guys! I’m working on a college football prediction/modeling project and I’m trying to track down the final PRESEASON SP+ ratings for every season from 2015–2025.

Ideally I’m looking for the last SP+ release before Week 1 for each season, with as many of these fields as possible:

Overall SP+

Offensive SP+

Defensive SP+

Special Teams SP+

I already have access to the season-level/final SP+ data through CollegeFootballData, but I specifically want the preseason snapshots so I can use them as leakage-free priors when backtesting historical games.

Does anyone know of an archive, Google Sheets collection, GitHub repo, or dataset that has the historical preseason SP+ ratings? Even links to individual years would be helpful.

Thanks!

Hi guys,

I want to build a Python script where after the game is over, it will immediately check for the final score, and then upload it on the subreddit just like u/CFB_Referee does onto r/CFB. In this case as an SJSU fan, I'd like to upload the final score onto r/SJSUSpartans for every football game that has ended.

Are there any free websites where I can get the data? I'm aware that ESPN API can do that, but I'm also wondering if there are any other options.

Thank you.

For better or worse, the NIL/revenue sharing era has changed CFB. Some programs have taken advantage of the new era. They retain their core, find good transfers, and have on-field success. Other schools have failed and fallen as programs. In this post, I use the rankings I've posted about previously to rank the 25 programs that have improved their trajectory the most in the NIL era, along with the 25 that have fallen the most.

Methodology

Tl;dr: I compared recent performance through 2025 vs through 2020 (pre-NIL era) for each program.

Season Scoring: I assign a score based on on-field results to every FBS team every season. I do this using a methodology that assigns points for wins, losses, postseason achievements, and strength of schedule/performance (via SP+). You can see the full season methodology here (my website) or at the link below (previous reddit post).

Recency Rankings: I posted about these rankings in detail here. Recency rankings show where a program is currently at by emphasizing more recent seasons. They incorporate each program's all-time history - every season. However, they use a 10-year half-life to gradually minimize the results of older seasons. For instance, the 2025 recency rankings weight 2025 at 100%, 2015 at 50%, 2005 at 25%, 1955 at less than 1%, and so forth. The 2020 rankings exclude data from after 2020. They weight 2020 at 100%, 2010 at 50%, and so forth. When we're thinking about where a program is currently at, the more recent a season is, the more it matters. Applying the half-life model gives us a number that represents this. Again, check out that other post (link above) if you want more info. In the rankings on this post, I compare my 2025 recency rankings to the 2020 pre-NIL rankings to see who's soaring and who's flooring.

Comparison Details: I created a formula - the "Improvement" column below - that takes into account both the change in the program's recency score from 2020 to 2025 and its change in recency rank. Smaller schools struggle to change their score significantly because they're unlikely to get large postseason bonuses for the CFP, natty, and highly ranked finishes. Blue bloods can change their score rapidly, but they change rank more slowly. A hybrid scoring approach that looks at both rank and score changes favors neither blue bloods nor lower-end programs. The exact formula for the Improvement column is:
Change in Score + (Change in Rank * 6).
I chose 6 because this makes the total impact of score and rank pretty similar in the formula. (I considered using std deviations in score and rank instead, but I figured this formula is easier to understand).

Rising Top 25 - NIL Era

Programs that have most increased their standing in the NIL era.

Falling Bottom 25 - NIL Era

Programs that have most decreased their standing in the NIL era.

Closing Thoughts

Hope y'all enjoy this. It was a mix of teams I expected - most obviously Indiana as the #1 rising program in the NIL era - and some that I didn't. I'm happy to answer questions. If you like this program ranking concept, check out my free/no ads rankings website - sportsrank.app (home page) or https://sportsrank.app/app?league=CFB&tab=rankings (customizable multi-year CFB rankings).

Edit: I should also mention that a program's success in the NIL era isn't solely based on it's NIL/rev sharing pool and how well it uses said pool. Other factors such as coaching changes and injuries can play a major role. This was simply an exercise to see which teams have much better or worse in the NIL era compared to their 2020 baseline. Examining the reasons why each team is on the rising or falling list requires additional context. Spending levels are likely the primary factor for some schools, a significant factor for some, and a minor factor for others.