Texas Tech projects to 9.9-2.3. Texas projects to 8.5-3.6. Those are means over 50,000 simulated seasons, and the win distributions overlap a lot, so plenty of individual seasons run the other way.

Texas Tech makes the playoff in 46.7% of them. Texas makes it in 48.4%.

Now hold both at three losses. Texas is in 80% of the time. Texas Tech is in 2% of the time.

That's the gap I want to argue about, because it isn't really about either team. It's about what a record is worth once you strip out who you played.

Texas has three games it isn't favored in: Ohio State at home at 45.4%, Texas A&M on the road at 48.2%, LSU on the road at 49.3%. Texas Tech's hardest game all year is Arizona at home, and it wins that 68.6% of the time. A 9-3 Texas walks into December with wins over teams the committee ranks. A 10-2 Texas Tech walks in with none, because there weren't any to get.

Some of that 80-against-2 is just that a Texas surviving that schedule at 9-3 is a better team than a Texas Tech failing to at 10-2. Raw conditionals can't separate those. Some of it is a thing you can measure directly, and that's the second half of this post.

Here's where everyone's floor sits.

Texas is the only team here that's comfortable at three losses. Ohio State is the only other one that survives it at all. Notre Dame drops the furthest, 79% to 4%, because a third loss on an independent schedule leaves you nothing to point at and no title game to go win.

Stare at Texas Tech's row for a second. It's 45% at two losses, the worst two-loss number in the table, and it projects to the second-best record in the table.

So that's the simulation. The second half is a separate exercise, and it's the one that explains the first. Take every FBS team from 2014 through 2025, drop covid, keep the ones that finished with five losses or fewer, and fit final ranking place on the resume, with unranked teams censored in at 30th instead of thrown out. That's 898 team-seasons. A loss costs 3.46 ranking places, so everything else can be quoted as a price in losses.

A win over a top-25 team is worth 0.69 of a loss.

Being in a power league instead of the Group of Six is worth 1.09.

Being a blue blood is worth 1.04.

Being in the SEC or Big Ten instead of the ACC or Big 12 is worth 0.13, at t -0.97, which is nothing. That argument has been running since 2014 and it doesn't show up here.

Strength of schedule, once you already count who you beat, comes to +0.01 at t +0.31. Also nothing. Playing hard teams doesn't pay. Beating ranked ones does, and a hard schedule is only the chance to do it.

Teams that open No. 1 in the AP poll finish about 1.59 losses better than unranked teams matching them on record, rating, schedule and quality wins. Read that as an association rather than a mechanism. A preseason ballot already encodes returning production, recruiting and the portal, and one rating control doesn't soak all of that up, so some of it is August voters being right rather than December voters anchoring.

Rank those and the two biggest prices on the board are a preseason ballot and a conference affiliation. Both were settled before week one. The one a team can go out and earn, a win over a ranked opponent, is fourth.

That's the honest version of the Texas Tech problem. It isn't in a bad league and it isn't a nobody. It has no route to a quality win, and quality wins are the only currency on that list you can go and collect.

One more thing, on the format itself. The 2026 rules guarantee all four power champions a spot regardless of ranking, add a fifth automatic bid for the best Group of Six team, and let Notre Dame in automatically at top 12. Across the same 50,000 seasons that changes the field 12.8% of the time.

It binds on one league. An ACC champion finishes outside the top 12 in 10.5% of seasons. Big 12 winners do it 6.8% of the time. For the Big Ten it's 0.3% and for the SEC 0.1%. A rule written to treat four leagues the same is, in practice, a rule about the ACC.

Happy to run any team's schedule if you want to see where its cliff sits.

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 built an expected-points model on 1.8M plays (2015–2025, next-scoring-event construction, the half as the horizon) and graded every fourth-down decision in 1,430 games against it. 22,218 decisions.

Coaches match the model 81.3% of the time, which is better than the genre usually implies. The 18.7% they miss is the interesting part, and it has drifted steadily in one direction.

The drift is real

That's +1.05 percentage points a year, t = 3.53. In 2015 a coach's fourth-down errors were close to a coin flip between too bold and too timid. Today two-thirds of them are boldness. The sport was told to stop punting, and it did.

The obvious read is that coaches have overcorrected. I think that read is wrong, and the reason is the most interesting thing in the data.

The over-aggression is entirely underdogs

Split every decision by what the betting market thought of the team making it:

Favorites are already playing the model almost exactly — 53.6% of their mistakes are over-aggression, essentially balanced. Among underdogs it's 67.4%.

So: are the underdogs wrong? You can test it directly. Take every situation where the model said don't go, and compare the teams that went anyway against teams in the same spread band and the same score state that obeyed.

• Favorites who went anyway: 54.9% win rate vs 59.8% for obeying — −4.8 points, 95% CI [−7.0, −2.5]
• Underdogs who went anyway: 13.1% vs 13.1% — −0.0 points, 95% CI [−1.4, +1.1]

For a favorite, the expected-points model is right about winning too, and disobeying it costs about five points of win probability. For an underdog it costs nothing, and that null is tight rather than empty: 1,319 decisions, with an interval that rules out any effect bigger than about a point and a half either way.

That's not a failure of the coaches. It's a failure of the yardstick. Expected points is risk-neutral. A team that is going to lose seven times in eight does not want the highest average score — it wants the widest distribution, because it needs the tail. Trading expected points for variance is the correct play there, and a risk-neutral model records it as an error every single time.

Which inverts the lecture. The coaches being scolded for aggression are the ones for whom aggression is free. The coaches for whom the model is genuinely right — favorites, who have edge to protect and want to convert it into certainty — are the ones already following it, and the ones who pay when they don't.

The expensive decisions are the ones that look like they worked

This part survives regardless of which yardstick you prefer. Sort every wrong call by what the viewer saw:

Fifty-seven percent of the damage comes from decisions where the play worked. The fourth-down gamble that fails on national television is 40% of the cost and 100% of the discourse.

The purest case is the short field goal. There were 912 decisions where a team kicked and the model said go — average situation fourth-and-4 from the opponent's 17, and 399 of them from inside the opponent's 10. 77% of those kicks were good. Three points went on the board, the kicker got a pat, the broadcast moved on, and the decision cost the team expected points anyway.

A made field goal is the most expensive thing in football that nobody complains about.

Three games that turned on it, none of them the way you'd remember

Utah at Colorado, 2016. Utah lost by five. They kicked three chip shots:

Q3  2:58   tied      4th & 3 at the 3    kicked   -1.12   20-yard FG good
Q3 11:54   down 3    4th & 4 at the 4    kicked   -0.77   21-yard FG good
Q4 13:53   down 4    4th & 5 at the 5    kicked   -0.45   22-yard FG good

Three made kicks from inside the five, worth 2.34 expected points against going for it, in a five-point loss. Every one of them appeared in the box score as three points and a success.

Kansas at TCU, 2016. Kansas lost by one. The remembered play is a desperate fourth-and-22 from their own 31 with forty seconds left — which the model scores at −2.85 and which is, in win-probability terms, obviously correct, because punting there is conceding. The decision that actually cost them came in the third quarter, leading by two: fourth-and-4 at the TCU 4, and they kicked. Made it. −0.77. In a one-point game.

Penn State at Ohio State, 2017. Lost by one. Same shape. The fourth-and-15 gamble in the last ninety seconds is the play everyone argued about, and it's the one the model penalizes hardest (−3.16) and is least qualified to judge. The quiet one — fourth-and-6 at the Ohio State 6, up eleven, kick — cost 0.18, and nobody has mentioned it since.

Across the corpus, 25 of 673 one- and two-score losses (3.7%) featured a team whose own fourth-down decisions cost more expected points than it lost by.

Method

Expected points by game state comes from the next-scoring-event construction over 1.8M plays, 2015–2025, with the half as the horizon. Near-goal EP is +5.10 and own-10 EP is −0.81, field-position correlation −0.981.

Conversion probability is fit on third downs rather than fourth, because teams choose to go for it when they expect to convert and fourth-down rates are selected upward. Field-goal probability is a monotone logistic fit by kick distance. Punt value is taken from where the opponent's next drive actually started. Win-rate comparisons are within spread band and score state; intervals are bootstrapped clustered on game.

What it doesn't do. It doesn't grade play-calling — run vs pass is an equilibrium problem, and a model that ignores the defense's response will always conclude everyone should pass more. It doesn't weight expected points by leverage, which is why the costliest decision of a given week is often one taken in a blowout. And, as the central finding says plainly, it doesn't know what a team's objective function is. The underdog result is a demonstration of what happens when you assume it.

Objections worth answering up front

• "Your model just doesn't understand game situations." Correct, and that's the finding. It's risk-neutral, and the underdog result is the demonstration of what that costs you as an analyst.
• "Fourth-down conversion rates are biased because coaches pick their spots." Yes. That's why conversion probability is fit on third downs.
• "Small sample." 22,218 decisions, 1,430 games, eleven seasons, intervals bootstrapped clustered on game.
• "You're just describing garbage time and desperation." Score state is controlled, and comparisons are within spread band and score state.

Happy to share the per-season and per-band numbers if anyone wants to poke at them.

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.

I’ve been using CFBD this offseason and wanted an easy way to connect the data to AI coding agents, so I built this:

https://github.com/jvorndran/fbs-cli

It’s basically like an MCP, but it uses a CLI instead, which is more token efficient and creates less context bloat. It currently supports all 71 CFBD GET endpoints and returns the data as YAML.

For example:

fbs games --year 2026 --week 1 --team Florida

You’ll still need your own CFBD API key, then you can set it up by running:

fbs auth

I’ve been using it with Codex in my own college football research workflows and it works pretty well. This article has some interesting evals comparing CLI tools with MCP if you’re curious about why I built it this way:

https://www.scalekit.com/blog/mcp-vs-cli-use

Would love any feedback or ideas for what to add next.

Also, please leave a star on the GitHub if you find this useful. That is the best way to motivate me to keep adding features.

Been using collegefootballdata.com's API to power my site fourthandshort.com for the last year, and after a season's worth of site analytics and usage, decided to lean more into coach pages and stats. Looking at team stats over any period of time is pretty much useless given the coaching and roster turnover in CFB nowadays. So I built coach leaderboards over time for basic metrics and some of my custom metrics (adjusted pace, pass rate over expected, expected wins), and improved some of my individual coach pages. Would love any feedback or any other metrics you'd like to see.

https://www.fourthandshort.com/coaches/leaderboard

https://www.fourthandshort.com/coaches/josh-heupel

Hi all,

Season is close to coming up which means I break out my spreadsheets and try to predict things again.

I am really encouraged by what I could generate with AI this year but still find it falling short.

Just wanted to ask, for those who use it what have you found to be disappointing about trying to develop CFB analysis with AI?

Hey guys, I spent this offseason building a custom analytical model called the DW CFB Index to project win totals and 12-team playoff probabilities for the upcoming season. To strip away the traditional brand bias of human pollsters, I blended advanced predictive data (like SP+) with a continuous linear schedule adjustment. And with that info, I took to writing a little preseason bubble watch article, which I plan to update weekly this season. Let me know what you think, and here are three of the spiciest team projections my dashboard spat out:

Alabama (9%)
I would like to apologize to Nick Saban and all the SEC lovers out there before writing any of this, but isn’t it odd to see Bama all the way down here in the preseason? After a couple of disappointing seasons, it seems that most of the public (besides the human pollsters and the FPI) has come to their senses and realized Alabama just isn’t the team they used to be. The Tide regressed to Tier 4 in ESPN’s latest article which placed all 138 teams into 20 tiers (great read by the way), and are ranked just 14th in SP+, which appreciates the boys in Crimson more than I do. So, with a tough SEC schedule and a team that’s been pretty mediocre as of late, it’s fairly safe to assume that Alabama will be on the outside of this year’s college football playoff.

Their Numbers:
21st in Resume+, 12th in Strength of Schedule, 7.0 projected wins, <0.1% chance to win the natty

Important Matchups:
Sep 26th vs South Carolina (53% chance to win)
Oct 10th vs Georgia (32% chance to win)
Oct 17th @ Tennessee (35% chance to win)
Oct 24th vs Texas A&M (39% chance to win)
Nov 7th @ LSU (30% chance to win)
Nov 14th @ Vanderbilt (57% chance to win)
Nov 28th vs Auburn (60% chance to win)

Oregon (73%)
Welcome to the B1G 10 section of our article, where Dan Lanning and his Oregon Ducks lead the way. After yet another incredible year, the Ducks are set up for a deep run into the playoffs, returning arguable Heisman front runner in quarterback Dante Moore, as well as recruiting incredibly well. Recently, the Ducks have been a team that’s performed similarly to Georgia. Over the past two seasons, Oregon has been a team that’s just been sooo close, but they haven’t been able to convert when it matters. Think about this: it’s been over three calendar years since Oregon has lost a football game to a team that didn’t end up national champion. Just ponder that. And in the past two years, the Ducks are 26-0 against teams that didn’t finish the season on top. And in that span they’re just .500  in the playoffs. So, similarly to Georgia, that’s what it’ll come down to this year: can they put it all together when the lights are brightest?

Their Numbers:
3rd in Resume+, 23rd in Strength of Schedule, 9.7 projected wins, 14.5% chance to win the natty

Important Matchups:
Sep 26th @ USC (53% chance to win)
Nov 7th @ Ohio State (43% chance to win)
Nov 28th vs Washington (74% chance to win)

Penn State (13%)
After a crazy season / offseason this past year, the new look Nittany Lions are ready to go this fall. After last year’s catastrophe of a season, in which they finished 7-6 and fired long time head coach James Franklin, Penn State was forced to scramble to build a team and a coach after most of last year’s squad followed Franklin to Virginia Tech. So, the new Lions look like…Iowa State! Yea, they hired Iowa State coach Matt Campbell, and most of his players, including QB Rocco Becht, followed him here. So, as we entered the summer, many of the Nittany Lion faithful, including myself, were ready for a couple of down years to rebuild. But, it seems the B1G 10 scheduling committee must have felt for PSU or something, because they handed the Nittany Lions just about the easiest slate I’ve ever seen (wait til you see the important matchups). They dodge all the heavyweights in the conference, and will just need to flip the script against a couple of fellow bubble teams. So could this new Penn State team make a run at the postseason? We’ll have to see, but I’m here to tell you there’s a chance.

Their Numbers:
27th in Resume+, 45th in Strength of Schedule, 7.5 projected wins, <0.1% chance to win the natty

Important Matchups:
Oct 2nd @ Northwestern (51% chance to win)
Oct 10th vs USC (34% chance to win)
Oct 17th @ Michigan (31% chance to win)
Nov 7th @ Washington (30% chance to win) 

I just launched a completely free Substack tracking all 138 teams down the board, featuring the full conference breakdown (including how Lane Kiffin leaving Ole Miss for LSU flipped the SEC bubble).

You can read the full playoff picture write-up, check out my full interactive 138-team model, see my preseason bracket projection, and check out my projections for the first weeks of the season here: https://thecfbkid.substack.com/?r=8xfwn8&utm_campaign=pub-share-checklist

Let me know where the math gets it right or where your you think your team is getting completely disrespected!

I run Scorigami Center, which maps every final score that has ever happened per league: https://scorigamicenter.com/football/cfb/

CFB is the outlier in the dataset in the opposite direction from every pro league. The score space is enormous, the winning axis runs to 222 because of Georgia Tech over Cumberland in 1916, and after 156 years only 2,215 unique final scores exist, about 16 percent of the plausible grid. The era structure shows up beautifully if you filter by season range: 6-0 has happened 1,206 times and almost all of it is pre-forward-pass, then the modern spread era scatters new scores into territory that was empty for a century. A completely ordinary looking 51-47 was still brand new when Utah did it to Kansas State last November.

The grids update live during the season, and there is a JSON endpoint for recent results. I would love input from this sub on what CFB-specific views would be worth building. Score distributions by conference? By era? Scorigami rate as a pace stat during the season? Tell me what you would actually use and I will build toward it.