I posted this to r/CFB and someone recommend I come here to post it and this is the first I'm hearing of this subreddit so now I'm excited for other football number nerds.

I'm looking for some opinions on a new computer poll that I created. It's similar to the BCS poll but I'm using Quadrants just like with the basketball Net Rankings. I'm not going to post the results currently because you're not going to like them which is why I am asking on your opinions for how much to weight the following items:

Item 1: This is what I'm using as the different Quadrants for 1-4 and for Home, Neutral, and Away. **I'm using 135 teams because any FCS school is being considered #135 and a Q4 win or loss**

Item 2: This is what I'm currently using as the weighted averages and how much of a factor it plays. This is what I'd like everyones opinions on. If there's a metric I don't have listed, please let me know what it is and why you think that should play a vital roll in the rankings.

The formula that I'm currently using is below. Will be curious if I add metrics or change weights to see how things play out:

NET = (WP*55%)+(SoS*20%)+(Eff.*15%)+(SoR*10%)+(Q1W*40%)+(Q2W*30%)+(Q3W*20%)+(Q4W*10%)+(Q1L*10%)+(Q2L*20%)+(Q3L*30%)+(Q4L*40%)

Any and all helpful opinions are welcomed.

Thanks!

Years ago, I wrote a script that implements a very simple formula to rank teams in an unbiased manner.

• You get 1 point for every team beaten by a team you beat
• You lose 1 point for every team that beat a team that beat you

The nice thing about this is it rewards playing good teams without having to base what a "good team" is on personal opinion. If a team has won a lot of games, beating them earns you more points. If a team has lost a lot of games, losing to them penalizes you more. Either beating a winless team or losing to an undefeated team will not impact your score.

This year the rankings have been very controversial, more so than usual, primarily due to the SEC cannibalizing itself. So I decided to break out this script again and see what it reveals. The following are the top 25 according to this formula.

I also scaled the points to the number of games played since I noticed some teams were getting an unfair advantage due to having played 11 games instead of 10. That is why some teams have decimal values.

#1. Oregon -- 11-0 -- 54.54545454545455 points

#2. Alabama -- 8-2 -- 48.0 points

#3. Ohio State -- 9-1 -- 44.0 points

#4. Boise State -- 9-1 -- 43.0 points

#5. Texas -- 9-1 -- 43.0 points

#6. Georgia -- 8-2 -- 42.0 points

#7. Indiana -- 10-0 -- 41.0 points

#8. SMU -- 9-1 -- 41.0 points

#9. Notre Dame -- 9-1 -- 39.0 points

#10. Miami -- 9-1 -- 38.0 points

#11. Penn State -- 9-1 -- 38.0 points

#12. Colorado -- 8-2 -- 38.0 points

#13. Army -- 9-0 -- 37.77777777777778 points

#14. BYU -- 9-1 -- 36.0 points

#15. Texas A&M -- 8-2 -- 35.0 points

#16. Iowa State -- 8-2 -- 31.0 points

#17. Ole Miss -- 8-2 -- 31.0 points

#18. Kansas State -- 7-3 -- 31.0 points

#19. Tulane -- 9-2 -- 30.909090909090907 points

#20. South Carolina -- 7-3 -- 29.0 points

#21. Clemson -- 8-2 -- 28.0 points

#22. Tennessee -- 8-2 -- 28.0 points

#23. Washington State -- 8-2 -- 28.0 points

#24. Syracuse -- 7-3 -- 26.0 points

#25. Texas Tech -- 6-4 -- 26.0 points

I don't think anyone will be surprised by Oregon at the top. Alabama at #2 was a little surprising to me, but they do have a couple ranked wins which is more than pretty much anyone else. Boise State gets some recognition, which they probably should considering their only loss is a close loss to the #1 team which is more than practically anyone else can say. Ultimately there's very little separating anyone which is quite different from what I saw in previous years but also seems accurate to how this season is going.

To those interested, here is my code and the original post explaining it.

https://gist.github.com/sem42198/f12459f2e1914fbf76c94320297595fa

https://www.reddit.com/r/CFBAnalysis/comments/e4rfey/basic_way_to_determine_rankings/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Which resume/SOR ratings evaluate the season holistically based on historical priors? I'm envisioning a rating based on (for example) how many teams that played 8 top 40 teams (in SP+ or some independent rating system) won at least 6 games, along with how many teams that played 6 top 30 teams won 5, etc., incorporating results for each threshold. It seems relatively simple other than the data compilation, so I suspect one or more well-known systems does this, but I haven't found one on my own yet. It sounds like FPI (as just one example) is based on game-by-game likelihood of victory, which might give different results due to cross-game error correlation or other reasons.

I know this website exists because I've used it in the past, but I cannot find it for the life of me. Does anyone know where you can find historical week-by-week conference standings? The website I'm remembering was pretty basic, but had every week archived. It's driving me insane.

I am looking to improve on my CFB betting model and one area that needs significant improvement is in the early part of the season. I would like to improve this by looking at the offseason win total markets to get a better initial power rating. Does anyone know if there is historical data on the CFB offseason win total markets anywhere?

I can’t seem to locate it at the moment but there was a website that allowed you to review play by play data for NFL games and then you could review the individual plays to go along with it. Does anything of the sort exist for CFB?

I'm using CFBFastR, and I'd like to be able to see the per-game and per-team versions of Success Rate, Explosiveness (through PPP), points per trip inside the 40 (finishing drives), field position, and turnover margin (i.e. Bill Connolly's Five Factors underlying SP+)

https://www.footballstudyhall.com/2014/1/24/5337968/college-football-five-factors

I can find a lot of them in CFBFastR. How do I get "Finishing Drives"? Do I need to write my own function of all the play by play data? Or does it exist?

So the the goal is to give every school an abbreviation with their logo in a small box. The box is only going to be 55 pixels wide, so I don't have a ton of room to work with. My max is really 4 letters. To give you an idea, here is a sample of what I am working on.

Imgur

Most abbreviations are fairly set in stone. Some of them are a little tougher. Everyone doesn't need to be completely unique since logos will be included, but the more variance is the better.

I appreciate any feedback!

Does anyone have what each team's SRS was following each week so far this season and would be willing to share? I usually grab it from (https://collegefootballdata.com/exporter/ratings/srs) but that only has season cumulative SRS.

Hopefully, someone else uses it in their model and has it saved by the week.

Thank you!

Has anyone developed a database with the following datasets/attributes? If not, is there any interest in collaborating to create one?
Historical college football results
Opening betting lines
computer model lines such as Massey and Sagarin (or others)
then looking at upcoming games with the same comparison?
Replicating for over/unders all of the above

Thanks

Does anyone know where I can find snap counts for free? Trying to see a breakdown of receivers for Alabama and having trouble finding it

Is there an alternative to ESPN for play by play data? There are no drives/plays for OSU vs Iowa.

I hate anOSU with a passion unknown to mankind, but FFS, how is there no data for a game played by a top 5 team? Is this some network contract bullshit, incompetency by ESPN or what?

I was checking pbp data using the following:

pbp <- cfbfastR::load_cfb_pbp(2024)

It is as if player_ids (eg. rush_player_id, reception_player_id, rush_player_name) were only recorded for the Alabama and WKU game. I spot checked (eg., went to a rush from Georgia vs. Clemson, and there was no player_id or name). Looks like everything position_reception and onward through target_player_id is only filled in for Alabama/WKU, otherwise, the cell says NA. The other columns have data for the other games.

Ran back and checked previous years...no issues.

Anyone encounter this?

I want to do some analysis related to how different formations (13 personnel, etc.) stack up against each other in terms of PPA/EPA. Is there anywhere I can find individual play formations? I, of course, could feasibly use collegefootballdata.com to scrape play-by-play stats, and manually add the observed formations. But, if someone else has already done that for me not gonna complain

On this page I can select more and then export and download all the data. I'd like to automate that process (Python if possible but not necessary). How do I do that? I'd like to download the csv automatically.

Hi all,

I once used a formula that I saw somewhere that allowed you to calculate “expected third down conversion rate” based on the distance to go.

The idea was that you could calculate all the distances faced by, say, a single team in a single game, and come up with an expected third down conversion rate (ex 28.4%) that could be compared to the actual third down conversion rate (ex 4 of 16, 25%), allowing us to return a “marginal third down conversion rate” (ex, 25% - 28.4%, or -3.4%) to see how good a team is on third down accounting for distance faced.

I remember that it was a regression formula that used the log of distance, but I don’t recall the coefficients and googling isn’t helping.

Anyone familiar with this calculation?

JMU Put up 70 points in a 70-50 win over UNC. Read all about it!

https://twsn.net/2024/09/james-madison-scores-70-points-in-shootout-win-against-unc

CFB-Graphs.com isn’t available anymore, and I’m looking for a replacement for it. I’m not sure how they were coming up with the rankings, but I think they were basing them off opponent adjusted success rate. There were rushing and passing for both offensive and defensive ranks. Looking for somewhere that ideally offers these rankings on the same page so that it’s easier for me to scrape than having to view a new webpage for each team’s profile to find them, but I’ll take that if the former isn’t available. Thanks for your help.

Who has the 2024 College Football Schedule in Excel Format.
I know the PDF is created from the Excel. So who has it?

Hello everyone, I was looking through Game on Paper and noticed that the Oregon Ducks had a negative special teams epa in their game against boise (no image posts?) Here is a link to special teams EPA I was looking at. This really confuses me as they had both a kick return touchdown and a punt return touchdown in this game. Diving into the play by play data I see they have 'none' listed under ppa for the punt return touchdown in the game. Does anyone know why that is and why the ducks had a negative special teams epa in this game?

Hi all,

I've been making my own college football power ratings for several years now and for the most part I'll take a look at how others ratings I respect change over the course of the year to help me in making upgrades or downgrades to mine. I was just wondering for anyone else out there who felt inclined to share, how do you upgrade and downgrade a teams PR on a week to week basis? Is a lot of it based on how they performed against the spread that week? Or more in depth?

Cheers

Edit: title shoukd read RATINGS not rantings 🤦‍♂️

I have built a machine learning program that predicts the AP poll in real time. Along with that, I've thought of building a college rank em contest where you can use the predictive tool to see how the AP poll will likely vote, and then you can make your own changes. I have built out all of the infrastructure, now curious on who would want to participate.

Here is how it works:

1. The web page shows all of the projected scores from all games (Vegas sports books).

2. The user would update the scores they believe are wrong or want adjusted

3. The user runs the simulation and the model spits out the results of how the AP / College Football Selection committee poll would vote in that circumstance

4. The user can then move around the predicted outputs to fit the result they think is going to be the real outcome

5. The user could then submit their results. All submissions have to happen before noon kickoff on Saturday, and results will then get posted after the new rankings have been released.

I think it would be a lot of fun and a new twist on Pick Em. Would anyone else be interested in participating in this?

I am trying to make historical cfb teams in cfb25 and am working on the 2001 Miami hurricanes rn, I am trying to come up with a list of their roster but all the sites I found have different info and was wondering which one is the most reliable and that I should use any help would be greatly appreciated.

I'm working to improve my coding, and I've been doing a lot of webscraping lately. I'm going to save the Jupyter notebooks and .csvs to this dropbox if you want them.

https://www.dropbox.com/scl/fo/xqd8i4hxuigmkyqjaiyhl/AGQfJmJ8mHkxsgbfqUyXfqo?rlkey=wvxqwemm9lbanb9lr4ye6cghy&st=k8ontxfs&dl=0

This morning I scraped https://www.jhowell.net/. It has team records all the way back to 1869. The python parses each page, makes sure the column names and locations are consistent, and saves it to a single .csv. If James Howell is active on this site, I'd like to thank him for maintaining this over the years. It's been a great resource.

I've recently been working on a simple process to determine a spread between two opponents. Overall my process performs well enough relative to Vegas lines after teams have played 5 or so games. However, I've been wondering about what methods others use to ensure their models are as accurate as possible over the first few weeks of the season.

I presume that a good model would take into account returning production and recruiting, and would also steadily downweight prior season results as the season progresses. I'd love to hear what has and hasn't worked for people in the past.

Does anyone know if there’s a way to get stats allowed per team on collegefootballdata.com

One challenge of munging multiple data sources is the non-standard naming conventions and IDs assigned to teams. Does anyone have a key mapping of one data source to another? If it exists, I'd like to just use it rather than do the work myself. Because I'm lazy.

Week 0 games kick off TOMORROW with FSU taking on GT in Dublin, which means it's time for our annual computer model pick'em contest.

Here's the link for the contest: https://predictions.collegefootballdata.com

What are the rules?

There really aren't any. Heck, you don't even have to make a computer model as there'd be no way of knowing whether your picks are human or computer picked. You can pick as many or as few games as you like. You can even wait to start a few weeks into the season (as I am doing).

Any changes this year?

Nope, no changes this year.

How are picks tracked and scored?

Since not everyone submits picks for every game and due to noted variance on how well models pick from game to game (i.e. some games deviate from expectations more than others) we will be using the Vegas line as a baseline in scoring. In short, the official leaderboard will measure how well a model does relative to the Vegas line for each game across all the categories.

Here's an example:

Example Game

Vegas Line: -7
Model Prediction: -9
Final Score Margin: -10

Vegas Error: 3
Model Error: 1
Difference: -2

In this example, the model's error is 2 less than Vegas, so the model is credited with 2 error points under expected for this specific game and this is the value used by the leaderboard. In general, you want your error values to come under expected relative to Vegas since less error is good. You want straight-up and ATS percentages to be over expected because more correctly picked games is also good. The main leaderboard contains a more detailed explanation.

Is there a minimum picks threshold to appear on the "official" leaderboard?

Yes. You must have picked >70% of eligible FBS games for the scoring period, whether that be a specific week or the entire season.

Can we still have the legacy leaderboard so I can see raw values for things like straight up percentage, ATS percentage, MSE, and absolute error?

Yes, the legacy leaderboard is still available with the same filters for you to enter whichever parameters you like.

But my computer model won't be ready until week X.

Totally fine. You can join in as early or as late as you want. There are no requirements on anything. You don't need to pick every week. In fact, you don't even need to pick every game every week. To show up on the legacy leaderboard, you just need to have picked 70% of FBS games for the given week (or for the entire season for the overall leaderboard).

How will picks be scored? ATS? Straight up? etc

There will be several different metrics on the leaderboard for judging pick models:

• Straight up correct percentage
• ATS correct percentage
• Absolute error
• Mean squared error
• Bias

It's understood that people build pick models with different goals in mind and this is meant to reflect that and provide a means for you to see how your model stacks up against the community in various metrics. And there is absolutely no threshold for joining. Everyone from people just starting out all the way up to professional data scientists are welcome to join us.

Will there be any prize?

Not right now, but I'm open to any prize suggestions. This is mainly for pride and fun.

I don't want to participate but I'd like to follow along.

I'll be tweeting out weekly results from the CFBD Twitter account (@CFB_Data) and may make some posts here. You can also follow along on the website leaderboard: https://predictions.collegefootballdata.com/leaderboard

I have suggestions on format, features, prizes, or the general contest.

Suggestions for features to the site, prizes, or really anything pertaining to this are more than welcome. If you have them, please reply to the thread here.

Anyway, good luck with your models and I hope you join us!

I'm looking to start a new project using college football data, simply because I like college football and want some diversification on my project portfolio.

The issue is that I can't think of anything that hasn't been done already. The only idea I had would be to combine the aspects that every website does well, into one website. Because I'm often in the situation of jumping between websites to read different stats and analytics. But after brainstorming and thinking about that for a while, I came to the conclusion that doing that would be very out of scope, since I'm developing this on my own.

So that's why I'm here. If anyone wants to see a website idea be done, relating to cfb data or analytics, then let me know. It would help me greatly while brainstorming.