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.

“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.

Hi r/cfbanalysis, I'm working on a larger write-up on this, but wanted to share the below project I was working on and check my process and rationale:

For whatever reason, I've always wondered about what kind of season it takes for a kicker to win the Lou Groza award.

To establish performance thresholds and build a predictive scoring model, I collected 28 data points apiece on 70 elite kickers from 2001-2025 (22 Groza winners and 46 runners-up/consensus All-Americans).

Full dataset

To establish a statistical floor, I looked at 17 key categories and found that winners outperformed runners-up in 15 of those areas on average. Looking at the average gap between winners and non-winners and filtering out some noise, five key categories emerged. For these, I established Minimum (historical floors that winners have hit, but as outliers) and Ideal (what 90% of winners have exceeded) thresholds:  

To see if this held water retroactively, I converted these thresholds into a 10-point scale:

• 1 point per Minimum threshold met
• 2 points per Ideal threshold met

This makes the max score 10, which has never been achieved (though a few have hit nine). Backtesting this from 2004-2025 we see:

• Winners earned 7.63 pts. vs. 6.52 for the runners-up on average
• Since 2015, the Groza winner has tied or outscored all runners-up every season
• Since 2006, no non-Groza winner has beaten the actual winner by more than one point
• Lowest winning score was 5 pts (2x, and both times the winner was outscored by the runner-up)
• When a 9-point kicker clearly outscores the field, they've won 100% of the time (5 of 5 instances). The only times 9-point kickers have lost were 2022 and 2012, when they tied with another 9-point kicker.
• Scoring 8 points puts a kicker in the mix, but it's often crowded and puts you at roughly a 50% chance even if you're the clear leader.
• Below 8 points, you're relying on weak competition or tiebreakers.

To summarize all of that--to seriously contend for the Groza, a kicker must:

1. Clear all 5 minimum thresholds above
2. Hit the Ideal thresholds in at least 3-4 categories
3. Score at least 8 Groza points

To make this a little easier to understand, I built an interactive calculator where you can input any kicker's stats and see their Groza Points score along with their historical win probability.

Curious to hear people's thoughts--look forward to holding this rubric up against the 2026 season and seeing how it aligns with the semi-finalist and finalist lists and correctly predicts the winner come December.

I've been gradually working on a passion project to rank programs and franchises based on historical performance. See where your team is ranked. It's free/no ads, and I'm interested in feedback - is the concept interesting or boring? What would you want to see added? I could add coaches, historical recruiting rankings, etc.

The landing page is sportsrank.app. The CFB rankings page is: https://sportsrank.app/app?league=CFB&tab=rankings.

Methodology
I have data going back to 1869 (sources below). Every meaningful result is assigned a points value:

• 10 point base season score. This rewards longevity and reduces the # of teams with negative all-time scores.
• 1 point for a win, -1 for a loss. This applies to all games - regular seasons and postseason.
• 1-25 point bonus for finishing ranked. I use the AP poll most years from it's inception in 1936 onwards. I use the Coaches Poll for 1961-1967 because the AP ranked 10 teams. I use Billingsley before 1936. I rank a max of 20% of the teams in my dataset for a given year, so that every team isn't ranked for early years where there weren't many teams.
• I add in Bill Connelly's SP+ ratings to account for strength of schedule / strength of performance. Most values range between -30 and 30 with a few outliers for exceptionally good and bad teams. I use SRS when that's unavailable and manipulated Billingsley ratings when that's also unavailable. I use the full value for ratings above 0. I use 60% of the value for negative ratings. This makes bad seasons less punishing and ensures only truly terrible programs like UMass have negative all-time scores.
• 100 points for a recognized natty (bonuses are shared for split titles).
• 9-40 points for losing in the CFP, depending on the round. To be exact, 9/16/25/40 for 1st round loss up through natty loss. BCS championship game losers also get a 40 pt bonus.
• Conference title bonuses based on conference strength. 1.5 point bonus winning a weak conference in the 12 team CFP era, up to about 25 for winning a very strong conference before the BCS. I use a formula that looks at both average SP+ rating for the entire conference and the avg of the top 3 teams that didn't win the conference to determine conference strength.
• Pts for bowl wins as well, from 0.5 for a low-end modern bowl to about 20 for a very high end pre-CFP bowl. I use the participants' records, final ranking, and SP+ rating to determine the prestige of the bowl game.
• I reduce the weight of conference titles and bowl wins gradually as we move from pre-BCS to the 12-team CFP era. They are worth 50% of the base value in the modern 12-team CFP era.
• Bowl and conference championship game losers get an appearance bonus that's equal to 25% of the winner bonus. For weak bowls/conferences, this generally isn't enough to counter the -1 from losing the game. It's a small net bonus for strong bowls and conferences.
• 5 point Heisman bonus.
• Main sources include collegefootballdata.com, sports-reference.com, and cfrc.com.

Key Features

• Rank every team based on any year range you want
• Group teams by conference, state, and more
• Create your own scoring system. You can tweak the values for anything I listed in the methodology section.
• Rank teams by other columns like ranked seasons and conference win %
• Click on a team to view season-by-season history.

Interesting Findings

• Bama is #1 all-time, followed by Michigan, Notre Dame, Ohio St, and Oklahoma.
• Army has the best all-time history of current G5 teams at #28, followed by rival Navy at #42.
• UGA is #1 in the NIL era (2021+).
• Yale dominated the 19th century, followed by Ivy League peers Princeton, Harvard, and Penn. Michigan was the best 20th century program followed closely by Notre Dame. Bama controls the 21st century (surprise), followed closely by Ohio St. There's a big gap to #3 UGA and #4 Oklahoma.
• Indiana is #67 all-time. The only program w/ a natty ranked below them is Rutgers, and their title was a shared one in 1869 (the 1st year of CFB, when there were only 2 teams lol).
• The active FBS program with the worst all-time history is UL Monroe, but UMass is making a beeline for the bottom.

After some back-and-forth with a couple of the gurus who were involved with the NCAA Power Index (well, their names were on one of the NCAA's documents), and some serious number crunching to make sure my numbers matched the NCAA's, I have developed a document that describes how to calculate it, complete with examples.

NCAA Power Index Calculation Method site

If anybody sees any glaring errors, or needs some help deciphering some of the numbers, let me know.

One thing I did discover while working on this: you can't lump FBS and FCS into a single ratings. There just isn't enough overlap to make the numbers work, and you almost always end up with an FCS team good enough to qualify for the CFP.

One thing I’ve been digging into lately is how much tempo skews the way we evaluate offensive performance in college football.

Raw stats (yards per game, points per game, etc.) obviously get inflated by faster teams, but even when looking at efficiency metrics, I still feel like tempo indirectly distorts perception.

For example:

• High-tempo teams create more total plays, more opportunities for explosive outcomes
• That can inflate things like success rate consistency over larger samples
• Meanwhile, slower teams might look less impressive on the surface despite being more efficient per play

I’ve been experimenting with looking more at:

• Yards per play vs total yardage
• Points per drive instead of points per game
• Success rate in neutral situations

But even then, it feels like there’s still some bias toward teams that push pace.

Curious how others here handle this:

• Do you heavily adjust for tempo when comparing teams?
• Any preferred metrics that better isolate “true” offensive strength?
• Has anyone found a reliable way to separate efficiency from play volume without losing too much signal?

Feels like this is one of those areas where small edges in evaluation can make a big difference, but I’m not sure there’s a clean solution.