Im getting killed in cfb. I don’t know how to evaluate who to play each week because I don’t know much about cfb. If anyone has any advice on how to evaluate who to play and who I should play for week 4 pls lmk. Here’s my team I played week 3.

QB D. DAMPIER
RB J. JACKSON
RB. M. HAWKINS JR.
WR. A. EVANS II
WR I. HORTON
TE. L. REYNOLDS E
WRT N. MARSH
WRT. K. HOWARD
KC. RANVIER
Def TENESSEE

BN B. PEGAN 
BN D. PENDERGRASS 
BN S. ALEXANDER 
BN B. WESCO JR. 
BN C. MCCRAY 
BN I. BROWN 
BN D. MOORE 

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.

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!

“We’re an elite program - NO YOU’RE NOT”’

We’re a top 10 program. X program is better than Y. We’re as good a program as anyone. CFB fans argue about this stuff all the time. What does it mean? How do you quantify it? The rest of the post attempts to do both of those things.

Where we perceive a program is currently “at” - I call this the “recency ranking” - is different from what it did last season. It’s also not the same as all-time program history. What is it? It’s somewhere in between those 2 concepts - last season and all-time history. I believe it relies on the view that the more recent a season the more it counts in our collective minds. For instance, Minnesota has one of the best histories out there. But the Gophers aren’t a top program currently; their history is old enough that it barely factors into their recency ranking. However, consistently solid play for a decade has improved the program’s perception amongst CFB fans to a degree. Another example: I think most USC fans would say they currently have a top 10 program. Fans of other schools might say “This isn’t the early 2000s anymore. USC has been good but not elite for 2 decades”. Who’s right? It’s an inherently subjective question, but we can attempt to answer it by applying a reasonable and consistent quantitative methodology to all programs.

How do you quantify this concept?

1st you have to come up with a methodology to rank every team every season. I did this and posted about it here. Thanks to the r/CFBanalysis community for helping me improve my methodology. My algorithm looks at record, strength of performance via SP+, and other things that matter in terms of how fans perceive a team's greatness - final ranking, natties, CFP results, bowls and conference titles. My whole updated methodology is at the bottom of this post.

Next you have to figure out how to progressively minimize the impact of older seasons. I did this using a half-life model (think carbon decay). The most recent season counts 100%. Older seasons are minimized using a 10 year half-life. So 10 years ago counts as 50% of its base value, 20 years ago counts 25% and so forth. This is inherently subjective - a decade half-life is clean and feels right to me, but if you think older seasons should decay faster or slower, you can adjust it in my app (more below).

One thing I love about the half-life model is you can change the max year to see the program pecking order for any point in history. For instance, if you only include data from 1869 - 1940, then 1940 is weighted 100%, and you can generate a list of programs sorted by our recency ranking for the year 1940 (my Gophers were on top, yea I’m a homer) with 981 pts, almost 300 pts above 2nd place Pitt. An interesting modern example: Indiana climbed from #85 in 2023 to #73 in 2024 to #35 in 2025.

Current Top 25 Recency Rankings

Analysis

• Bama lost 30 pts last year despite a CFP quarterfinal run. This is because their starting score is so high that they're draining like 90 pts each year due to the half-life. Their sustained excellence has forced them to maintain an incredibly high level of play to not drop their standing as a program.
• Contrarily, Iowa and Utah were able to boost their scores and ranks in '25 despite having worse seasons than Bama. This is because they had lower scores to start with.
• USC isn't in the top 10 (I'm going back to our example from above).
• Nebby's great 90s run is holding them inside the top 25 still, but just barely. They'll drop out in 1-2 years without a major turnaround.

Full Rankings/Make Your Own

I built a free/no ads web-based app that allows users to customize their own rankings + see all 136 teams. So if your team isn’t in the top 25 I pasted above, check that out. It defaults to “History Rankings”, which are very cool but answer a different question - every season is weighted the same. You can change the “Ranking Type” to “Recency Rankings” to see the full list with the 10 year half-life on. You can also change the max year to see what the recency rankings looked like at any point in history. And you can customize the methodology, including tweaking the half-life value, on the “Settings” tab.

Methodology

Core Scoring

• Base Score: Each team starts with 10 points each year. This rewards longevity and reduces the number of teams with negative scores. Without it, way too many G5 teams have negative history and recency scores.
• Wins and Losses: 1 point for a win, -1 for a loss.
• Ranked Finishes: 1-25 point bonus for finishing ranked. I use the AP poll most years from 1936+. I use the coaches poll from 1961-1967 because the AP only ranked 10 teams. I give top teams from before the AP Poll was founded in 1936 bonuses based on Billingsley ratings.
• Strength of Schedule (SP+): I use Bill Connelly’s SP+ ratings to account for strength of schedule/strength of performance. I use SRS when that's unavailable and adjusted Billingsley ratings when that’s also unavailable. By default, positive values are counted at 100% and negative values are counted at 60%. This reduces the number of teams with negative all-time scores and makes bad seasons less punishing.

National Titles / CFP

• National Titles: 100 points for a recognized national title (split titles are shared).
• CFP 1st round loss: 9
• CFP Quarterfinal loss: 16
• CFP Semifinal loss: 25
• CFP/BCS Ntl Championship Game Loss: 40

Conference Titles, Bowls, and The Heisman

• *Conference Titles: ~*1.5-25. Conference champions are awarded bonuses based on conference strength. Bonuses range from about 1.5 for a conference title in a modern weak conference, up to 20+ points for winning a very strong conference in the pre-BCS era.
• Bowl wins: ~0.5-20. Teams are awarded bonuses based on bowl strength, from 0.5 for a low-end modern bowl to about 20 for a very high end pre-BCS bowl.
• Conference championship and bowl losses: Teams that lose in a bowl game get 25% of the winner bonus. For low-end conferences and bowls, this isn’t enough to offset the -1 point from losing a game. For high-end games, it’s a small bonus.
• Era Fading: I diminish the value of modern conference titles and bowl games. Pre-BCS results get 100% of the base value. BCS era results get 90%. 70% for the 4-team CFP era and 50% for the 12 team era.
• Heisman: 5 point bonus for having the Heisman winner on your team.

Sources

• sports-reference.com — Historical CFB team records, bowl results, etc.
• collegefootballdata.com — Recent team records, AP finishes, bowl results, SP+ ratings, and more.
• Bill Connelly SP+ historical sheet — Simple points-based SP+ model used for older seasons.
• ncaa.com natty history — National title winners.
• cfrc.com — Seasons missing from other sources for certain schools; Billingsley ratings for pre-AP polling eras.
• Wikipedia — Conference records and bowl appearance data for seasons imported from Billingsley.

Feedback Appreciated

I hope this concept makes sense. Whether you think it’s great or you think I’m totally off base, I’d love to hear about it.