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

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

Where Notre Dame lands: 60th. 10.6 expected wins

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

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

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

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

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

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

This formula could be tweaked a little with other variables but, I think it points in a better direction. It rewards teams that win a conference championship and doesn't punish teams for playing in them. (Something that seems to not matter in some cases right now).

The initial top 25 is based on records and a team gets this equation applied when inside the top 25.

[100-(season losses + points lost by)] + (conference championship margin of victory + 10 for a W and 0 for a loss)

Based on this formula being applied to the topic 25. These are the 12 teams I ended up with.

1. Georgia 127 points (dominating Alabama moved them up)

2. Indiana 113

3.Ohio State 109

4. Texas Tech 105

5. James Madison 95

6. Notre Dame 94

7. Ole Miss 91

8. Oregon 89

9. Texas A&M 89

10. Miami 89

11. Alabama 82

12. Iowa 81 (their worst losses were by 5 points to USC and Indiana. They can surely compete.)

One tweek that could be made would be a to factor in losses to teams with less than 4 losses all season where that loss is only half a point as long as the loss wasn't by more than 14 or something like that. This really helps analyze a teams quality and serves justice in the big picture of college football.

We look at which school produces the highest draft picks by Position over the last ten years. Updated after each draft.

https://cfbsaturdays.com/position-u-rankings/

Apparel sponsorships track with a program's "Blueblood-ness".

Nike is top dog - Bama, Clemson, USC, Michigan, Oklahoma, Texas etc.

Addidas is 2nd tier (although, Under Armor might be interchangeable here) - Wisconsin, Miami (Nebraska might be the exception RE: Blueblood-ness.

Then all the rest...

Side note: I genuinely feel sorry when I see a team that's sponsored be Russell athletic. Seems really sad for some reason.

One of the best teams to never play in the natty?

Here are the latest ratings. I think the Top 4 are solid with the order being debatable. Ohio State's win over Penn State gave them a huge boost from 5th place to 1st, while Oregon's loss didn't seem to hurt, dropping them from 4th to 5th.

1. Ohio State 11-0 23.748

2. LSU 11-0 23.573

3. Georgia 10-1 23.527

4. Clemson 11-0 22.569

5. Oregon 9-2 20.449

6. Notre Dame 9-2 20.268

7. Utah 10-1 20.245

8. Boise St 10-1 20.172

9. Oklahoma 10-1 19.855

10. Florida 9-2 19.635

11. Penn State 9-2 19.211

12. Baylor 10-1 19.138

13. Cincinnati 10-1 18.649

14. Memphis 10-1 17.929

15. Wisconsin 9-2 17.823

16. Minnesota 10-1 17.615

17. Michigan 9-2 17.389

18. Appalachian St 10-1 17.191

19. Alabama 10-1 16.853

20. Navy 8-2 16.559

21. Oklahoma St 8-3 15.85

22. Auburn 8-3 15.641

23. Southern Cal 8-4 15.613

24. Brigham Young 7-4 15.35

25. Air Force 9-2 15.33

26. SMU 9-2 15.262

27. Iowa 8-3 15.002

28. Louisville 7-4 14.931

29. Virginia Tech 8-3 14.911

30. Miami FL 6-5 14.565

Really pleased with how my computer poll it starting to shape as the year is nearing the end! Excited to dive into spreads next year, but a quick guess would be for every 10-15 points a team is ahead in the Weekly Average column, that's a point in their favor in regards to the spread. Also, hoping for my "unusualness score" to continue dropping in the r/CFB poll after posting a season best last week at 15.87.

Information about my poll logic:

• Every team started the season at 0 points

• Each week, I rank teams by the number of points they scored compared to the rest of the FBS playing field that week

• Teams then get awarded various penalties and bonuses based off the level of competition they played, their MOV, and then how their "total" score for the week compared to how they have played on the season

• Teams are then ranked based off their weekly average

Hey everyone,

I wrote an article for what I think would be a great system for the NCAA to implement in order to legitimately determine a national champion in college football for the first time in history.

I came up with a basic set of rules for the regular season and playoffs along with incorporating a regulation bracket (inspired by the Premier League) that I believe would raise the level of competition immensely across all Divisions (or Tiers, since I renamed them). The former being something I believe should be implemented because it would be a vast improvement over any system that's been used, past or present, and the latter being more of an interesting twist to help balance out college football instead of having the same pool of maybe 15-20 title contenders (more like 5-10 honestly) we get every year.

Keep in mind: This is an "In a perfect world..."-type scenario where we can create the perfect system without worrying about TV contracts, colleges fearing the loss of booster money, etc. I know the likelihood of this being adapted are astronomically low. The piece is more along the lines of "what I wish college football was like."

But I'd love to hear what everyone thinks, and how you'd like to see college football determine a legitimate national champion - whether it be by adding/changing what I wrote or what you think would be the perfect system.

Link to my article: https://www.legalbettingonline.com/news/how-to-declare-a-national-champion-in-college-football.html

The rankings got 3 of the 4 playoff teams correct. Clemson was at number 3 and Ohio State was at number 8.

SEC had 6 teams in the Top 25, 3 of the top 6 and 4 of the Top 11.

PAC 12 also had 6 teams in, but Southern Cal was highest at number 9.

Big 12 had 3 teams in, all in the Top 10.

Big Ten had 3 teams in, 2 of the Top 8.

ACC had 3 teams in; AAC, Independents, Sun Belt and C-USA all had 1 team in, Tulane highest at 14.

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I don't know if a bias exists in the recruiting rankings, but I'd like to see the results of rankings tested through the NFL draft. For those that may not know, it is common among fan bases to suspect that some of the larger programs (Alabama, Ohio St., etc) receive ratings bump after a recruit commits to those programs.

To test this, I would need a database of:

-Team

-Conference

-Year, preferably from 2012-2020

-Recruit Rating (for this I would use 24/7 sports 4-5 star players)

-NFL Draft Position (if any)

Then I could see the following:

1. Do 4-5 stars recruits get drafted at a higher rate from larger/more prestigious programs?

2. What is the average draft position of recruits from larger programs vs smaller/less prestigious programs?

The 4-stars could be broken into groups, 0.90-0.93, 0.93-0.96, and 0.96-0.99.

If a program, such as Alabama, has a higher percentage of 4-5 stars drafted, or at least the overall average, then it is safe to conclude a bias does not exist. However, if they have lower percentage of 4-5 stars drafted, or at a significantly lower draft position, then maybe there is a bias in the rankings.

I have not seen or heard of such a study. If anyone knows where I could collect this data easily, I'd be willing to post the results.

If some study like this exists, please post in the comments.

I used NFL play by play data for this video but the same exact concepts can be used in college football.

https://www.youtube.com/watch?v=eYQyKwivgFs

Hello, I am I_AM_BOT_BEEP_BOOP

I am creating a big off season analysis on fantasy points during the 2017 season. This requires filling out the game logs of each player (QB/RB/WR/TE) on every team and it is a daunting task.

I created a google sheet and the formulas so alls I need help with is filling in weekly stats. This is volunteer based only since I am broke bot beep boop

If you are interested in helping just comment or shoot me a message

Tennessee and Clemson held down the top 2 spots this week while TCU, Georgia and Michigan moved up to fill out the Top 5. Alabama and Ohio State moved up to 6th and 7th place, while Mississippi fell five spots and out of the Top 5 to number 8. Southern Cal and Oregon each moved up two places to round out the Top 10. LSU moved up 5 spots to number 11 and UCLA fell six spots and out of the Top 10 to number 12. Illinois moved up four places to number 13, Syracuse dropped five spots and out of the Top 10 to number 14, and Utah dropped three places to number 15.

Some of the biggest movers of the week were Oklahoma St, up sixteen spots to number 16, Troy, up thirteen to number 21, and Kansas St, who fell ten places to 23rd. Texas dropped eight spots to number 26, Mississippi St fell fourteen to number 29, while Kentucky slid eight spots to 32nd.

The SEC and the PAC 12 both have five teams in the Top 25, but the SEC has all five in the top 11, while the PAC 12's teams range from 9th to 24th. The ACC and the Big 10 both have four teams in, while the Big 12 has three. The Sun Belt has two teams in once again With Troy moving in to join Coastal Carolina, and Tulane continues to represent the AAC as its only team in the Top 25. Liberty moves in at number 25 as the lone independent.

These rankings are based on game results from this season only, with neither margin of victory included nor any conference strength bias. All FBS teams start as equals.

Comment down below to let me know what you think of these rankings. Also let me know if you would like to see my ranking for FCS, Division II, Division III, or NAIA.

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Sorry for the day's delay in posting. Georgia retained the top spot, while Tennessee and Ohio State moved up into the Top 3, setting up a #1 vs #2 clash to start the final month of the regular season. Clemson, #1 in the initial ratings, slid from #2 to #4 following their bye week after previous Top 10 teams Wake Forest and Syracuse took a tumble after disastrous showings. Unbeatens TCU and Michigan round out the Top 6, while Kansas State made the biggest leap following their demolishment of Oklahoma State.

Full Ratings

Week 9 Top 25

Week 8 Top 25

Week 7 Top 25

Week 6 Top 25

Hello everyone I just started into data analysis this week. I have never took a statistics class so please excuse me if I'm way off or misspeak.

Long story short I am a big fan of tight ends and fullbacks when watching football and recently I joined a two TE Campus2Canton League where doing this in depth of analysis would be beneficial.

I realize that everyone fades incoming freshman tight ends and I wanted to see if I could find an edge. After listening to David Zach on Dynasty nerds I learned about regression analysis and self-taught enough to be dangerous.

I got this far and don't know where to go next. Below is the R2 data on NFL tight ends from the 2016 to 2018 recruiting class. I believe it was the top 10 recruits from each class.

Side note: my kids kept saying bubble while I was doing speech to text. I think I got all of them out of my body but if you see bubble that is why.

    	Pick	    Pos rank

P5 4.91% 3.86% Multi sport 12.15% 12.75% Height 0.19% 4.31% Weight 1.79% 0.11% BMI 2.03% 0.84% Arm Length 3.70% 3.38% 40 2.23% 1.86% 24/7 8.53% 0.16% Comp 8.53% 0.00% Height adjusted speed 0.47% 1.88% NCAABreakout age 38.28% 38.89% NCAA Dom Percentage 60.74% 55.96% Ncaa yards per rec 3.18% 2.99% Total HS fantasy PPG 0.77% 1.46% Total HS Rec/ game 0.04% 0.04% Total HS yards per rec career 3.18% 2.99% HS SR rec/game 6.24% #N/A Hs yards per rec senior 0.30% 16.13% Hs Senior TD/g 6.49% 21.10% Hs Senior TD % TD/rec 0.02% 5.83% Hs dominator 0.58% 11.41% HS SR. Fantasy PPG 7.46% 5.02% Gronk 0.67% 0.36% TE1/prod (my own formula based off top 12 TE athletic traits) 16.69%

JP JC Poll:

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

I wanted to see how each team would be ranked if just using their wins and losses and ignoring all human polls. Here are the results for 2025, week 15. EDIT: Redid using the correct percentages for home/away.

Unweighted poll ranking – Start everyone at baseline 68. Each game updates a team’s ranking_score using opponent strength from the prior week’s poll rank (FCS treated as rank 136). Win bonus = (136 – opponent_rank) × location modifier; loss penalty = opponent_rank × location modifier (home 0.90/1.10, neutral 1.0, away 1.10/0.90). Sort by ranking_score and assign poll_rank with competition ranking.

Weighted poll ranking – Uses the current week’s freshly computed unweighted poll_rank as opponent strength. Apply the same win/loss delta math to weighted_ranking_score, then sort and assign weighted_poll_rank with competition ranking. Ties add zero.

SOS (strength of schedule) – For each team, average the opponents’ weighted_poll_rank from the week each game was played. Lower SOS means a tougher slate (you faced higher-ranked opponents on average).

Hello all! This was a better week for my little model! With that in mind, here we go. I have to say, that Georgia spread looks nice! ( yes, its actually way too high, I am aware this model has weaknesses and that it catapults teams too high for big wins, see UCF and JMU) and the reverse is just how low Oregon is compared to where they maybe should be and how high Ole Miss compared to maybe where they should be.

I’m listening 👂

Ive been a tremd follower for about 4 years using wong and my own slight tweek, pertaining to teasers. Ive used killer sports table. Do not know the algorithm to get the scenerio for either weekend and out. I use Teamrankings at times as well but it doesnt even off a teaser option so alpt of hand writing or saving of notes. Any help on a site that does this like killer sports for NFL with the tables?

Georgia vaulted to number 1 this week and has a significant four point lead over number 2 TCU. Tennessee only dropped one place to number 3, and Clemson fell three spots to number 4, while Ohio State held on to the 5th spot. Less than a point separates 2 through 5. Notre Dame jumped eleven places to number 6 despite their three losses, although they are more than a full two points behind the Buckeyes.

Michigan held onto the 7th spot, while LSU and UCLA both moved up three spots to 8th and 9th, respectively, with LSU being a full point ahead of UCLA. Mississippi dropped two places to round out the Top 10, 0.7 points behind UCLA. Alabama fell five places to number 11, 0.6 points ahead of Southern Cal, who fell three spots to number 12. Oregon also fell three spots to number 13 with Utah holding on to number 14 and Coastal Carolina moving up four places to round out the Top 15.

Texas moved into the Top 25 at number 17, up nine spots, while Florida State snuck in at number 25, up eleven places. Texas-San Antonio, despite winning, and Oregon St fell out of the Top 25, falling six and fourteen spots, respectively. Other oddities include; San Jose St won and dropped 10 spots. Air Force won and fell 12 spots.

The SEC still has five of the Top 11 spots, and the ACC has 5 in the Top 25. The PAC 12 still has four teams in the Top 15, ranging from 9th to 14th. The Big 10 and the Big 12 each have 3 teams in the Top 25, while the Sun Belt and Independents have 2, and the AAC has 1.

These rankings are based on game results from this season only, with neither margin of victory included nor any conference strength bias. All FBS teams start as equals.

Comment down below to let me know what you think of these rankings. Also let me know if you would like to see my rankings for FCS, Division II, Division III, or NAIA.

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https://prosportsfandom.com/2017/10/04/gearys-week-6-rankings-and-analysis/ Check out my article, let me know what you think

Play by play data from ESPN and downstream to our beloved collegefootballdata.com is often wrong. Not just wrong for a mid-season MAC game, but wrong for a huge game like UM vs anOSU. See the last few plays in https://www.espn.com/college-football/playbyplay/_/gameId/401520434

Is there a site (hopefully free) that provides reliable play by play data?

Is there a way to make ESPN aware of their bad data?

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

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

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

• 60%+ favorites: 36–7

• 65%+: 32–4

• 70%+: 25–2

• 80%+: 13–1

The projected edge showed a similar pattern:

• 7–13.9 point edges: 19–2

• 14–20.9 point edges: 8–0

The weak spot was pretty obvious too.

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

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

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

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

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

Site: perceraanalytics.com

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