Hi y’all, Im looking for open data sources for game film across multiple teams. Any recommendations?

Hello,

I would like to create a dataframe where each row corresponds to a single column with the normal columns such as gameid, home team, away team, and similar to the format of the 'Games and Results' section, have each different stat category be represented with home rushing attempts, etc

Here is the code I have (stat is the list where all the data from team game stats is stored in stat

I have also attached the output for the first index in the stat list to give an idea of the format (this will be at the very bottom)

stat = []

respons = games_api.get_team_game_stats(year=2016, week=10)

stat = [stat,respons]

I greatly appreciate any help with this as I have tried chatgpt and bard to help out with the formating, but to no avail.

(These are the columns for the Games and Results table I also have, these are the sorts of columns I want)

Id Season Week Season Type Completed Neutral Site Conference Game Attendance Venue Id Home Id Home Team Home Conference Home Division Home Points Home Line Scores[0] Home Line Scores[1] Home Line Scores[2] Home Line Scores[3] Away Id Away Team Away Conference Away Division Away Points Away Line Scores[0] Away Line Scores[1] Away Line Scores[2] Away Line Scores[3] Home Point Diff Total Points

(The below code is an index of the list which contains all the games)

{'id': 400868954,

'teams': [{'conference': 'American Athletic',

'home_away': 'home',

'points': 28,

'school': 'Navy',

'school_id': 2426,

'stats': [{'category': 'rushingTDs', 'stat': '4'},

{'category': 'passingTDs', 'stat': '0'},

{'category': 'kickReturnYards', 'stat': '38'},

{'category': 'kickReturnTDs', 'stat': '0'},

{'category': 'kickReturns', 'stat': '2'},

{'category': 'kickingPoints', 'stat': '4'},

{'category': 'fumblesRecovered', 'stat': '0'},

{'category': 'totalFumbles', 'stat': '2'},

{'category': 'tacklesForLoss', 'stat': '1'},

{'category': 'defensiveTDs', 'stat': '0'},

{'category': 'tackles', 'stat': '24'},

{'category': 'sacks', 'stat': '1'},

{'category': 'qbHurries', 'stat': '2'},

{'category': 'passesDeflected', 'stat': '0'},

{'category': 'firstDowns', 'stat': '21'},

{'category': 'thirdDownEff', 'stat': '8-13'},

{'category': 'fourthDownEff', 'stat': '4-5'},

{'category': 'totalYards', 'stat': '368'},

{'category': 'netPassingYards', 'stat': '48'},

{'category': 'completionAttempts', 'stat': '5-8'},

{'category': 'yardsPerPass', 'stat': '6.0'},

{'category': 'rushingYards', 'stat': '320'},

{'category': 'rushingAttempts', 'stat': '56'},

{'category': 'yardsPerRushAttempt', 'stat': '5.7'},

{'category': 'totalPenaltiesYards', 'stat': '1-5'},

{'category': 'turnovers', 'stat': '0'},

{'category': 'fumblesLost', 'stat': '0'},

{'category': 'interceptions', 'stat': '0'},

{'category': 'possessionTime', 'stat': '33:53'}]},

{'conference': 'FBS Independents',

'home_away': 'away',

'points': 27,

'school': 'Notre Dame',

'school_id': 87,

'stats': [{'category': 'fumblesRecovered', 'stat': '0'},

{'category': 'rushingTDs', 'stat': '0'},

{'category': 'passingTDs', 'stat': '3'},

{'category': 'kickReturnYards', 'stat': '61'},

{'category': 'kickReturnTDs', 'stat': '0'},

{'category': 'kickReturns', 'stat': '3'},

{'category': 'kickingPoints', 'stat': '9'},

{'category': 'tacklesForLoss', 'stat': '4'},

{'category': 'defensiveTDs', 'stat': '0'},

{'category': 'tackles', 'stat': '24'},

{'category': 'sacks', 'stat': '0'},

{'category': 'qbHurries', 'stat': '0'},

{'category': 'passesDeflected', 'stat': '1'},

{'category': 'firstDowns', 'stat': '21'},

{'category': 'thirdDownEff', 'stat': '9-13'},

{'category': 'fourthDownEff', 'stat': '1-1'},

{'category': 'totalYards', 'stat': '370'},

{'category': 'netPassingYards', 'stat': '223'},

{'category': 'completionAttempts', 'stat': '19-27'},

{'category': 'yardsPerPass', 'stat': '8.3'},

{'category': 'rushingYards', 'stat': '147'},

{'category': 'rushingAttempts', 'stat': '29'},

{'category': 'yardsPerRushAttempt', 'stat': '5.1'},

{'category': 'totalPenaltiesYards', 'stat': '7-47'},

{'category': 'turnovers', 'stat': '0'},

{'category': 'fumblesLost', 'stat': '0'},

{'category': 'interceptions', 'stat': '0'},

{'category': 'possessionTime', 'stat': '26:07'}]}]}

Hello,

I was wondering if anyone has any advice on getting game by game data for college football games. I am pretty unexperienced in web scrapping and api stuff, and so far the only real data I can get easily is just points for each team and quarter points from collegefootballdata.com in the Games and Results section.

What I really want is not really just points, but having statistics like home rush yards, away rush yards, away time of possession, home time of possession, home turnovers, away turnovers, etc.

Does anyone have any idea as to any website I can use that will allow me to get this data? I currently have a key from sportsradar.com for collge football, but am not really sure how to get the data I need from this.

Thanks in advanced for anyone willing to help.

Hi everybody. I'm trying to produce a table in which each row represents a player and contains that player's name, their high school recruiting rating, and their transfer portal recruiting rating. I want the table to be populated with only players that have a non-null value for both the hs rating and the transfer portal rating. I keep running into an error telling me that the key "_name" is not valid when pulling from the recruiting dataset. The code where I create the data-pulling functions is below. I'd really appreciate any feedback!:

def fetch_recruiting_data(year):

return recruiting_api.get_recruiting_players(year=year)

def fetch_transfer_data(years):

transfer_data = []

for year in years:

transfer_data.extend(players_api.get_transfer_portal(year=year))

return transfer_data

Function to create the table

def create_player_table(recruiting_years, transfer_years):

Fetch data

recruiting_data = []

for year in recruiting_years:

recruiting_data.extend(fetch_recruiting_data(year))

transfer_data = fetch_transfer_data(transfer_years)

Convert to DataFrame

recruiting_df = pd.DataFrame(recruiting_data)

transfer_df = pd.DataFrame(transfer_data)

Assuming '_name' is the correct attribute for player names

if not recruiting_df.empty and not transfer_df.empty:

recruiting_df['full_name'] = recruiting_df['_name'].str.strip()

transfer_df['full_name'] = transfer_df['FirstName'].str.strip() + " " + transfer_df['LastName'].str.strip()

Filter data to include only entries with non-empty ratings

recruiting_df = recruiting_df[recruiting_df['_rating'].notna()]

transfer_df = transfer_df[transfer_df['_Rating'].notna()]

Perform an inner join to ensure only players with both ratings are included

merged_df = pd.merge(recruiting_df, transfer_df, on='full_name', suffixes=('_recruit', '_transfer'), how='inner')

Calculate rating difference

merged_df['rating_difference'] = merged_df['_Rating'] - merged_df['_rating']

Select and rename columns

result_df = merged_df[['full_name', '_rating', '_Rating', 'rating_difference']]

result_df.columns = ['Player Name', 'HS Recruiting Rating', 'Transfer Portal Rating', 'Rating Difference']

return result_df

else:

return pd.DataFrame() # Return an empty DataFrame if no data available

I’m trying to learn how to build my own Strength of Schedule ratings for teams offenses and defenses. Does anyone know a website that would help get me started with this? Most I run across have been using the opponents WL%, but I want to build it for both sides of the ball individually.

Thanks in advance for any help.

I am trying to pull Jayden Daniels college season stats using cfbd's get_player_season_stats method. I am not seeing a parameter that I can specify the player I am wanting to search.

Can I specify the player's season stats I am wanting to pull using get_player_season_stats or do I need to pull them all, then filter by player?

Hello everyone. I'm a new user who just started working with the API. I wanted to look up historical data for the pairwise matchups in FBS. For example, when I look up results from Iron Bowl from 1880-2050 (ensuring I get all matchups), via this command:

curl -X GET "https://api.collegefootballdata.com/teams/matchup?team1=Alabama&team2=Auburn&minYear=1880&maxYear=2050" -H "accept: application/json" -H "Authorization: Bearer TguaiqMfP0hHFgVL3dJ2/Nb5vKQmiJW/l2xPsjcyPpVbdP594UQ+3pRtTReXi5iF"

I get the following output:

{

"team1": "Alabama",
"team2": "Auburn",
"startYear": "1880",
"endYear": "2050",
"team1Wins": 49,
"team2Wins": 32,
"ties": 1,
"games": ... }

It's reporting a record of 49-32-1. However, Winsipedia has the record at 50-37-1: https://www.winsipedia.com/alabama/vs/auburn

A quick perusal of the game info from the .json vs the game results from the Wikipedia article on the Iron bowl shows that some games from the 19th century are missing, despite a provided start date of 1880. The FAQ states a start year of 1869, so I'm wondering where the discrepancy might be coming from. Maybe I'm missing something obvious?

Thanks in advance!

I'm able to do this for NFL data with Stathead, but they don't have this data for cfb. Anywhere I can pull this data for under $20/mo?

Was looking into making setting up a model based on win probability for next year, but could not find any way to accurately get trustworthy PBP data. I want to include FCS as well and ESPN does not carry PBP for a good portion of those games. There is PBP available from stats.ncaa.org that is reliable and there is a way to use down, distance, score, etc to get win probability so all I need is to be able to scrape data from that website into a workable table. R is preferred, but I'd learn Python if that's all that is out there. Would appreciate if anyone knows anything that could help.

Posted in here a few days ago, unable to pull data from collegefootballdata.com API to google sheets. Glad to say, I figured that part out and have had some fun playing around with all the new information at my fingertips. When it comes to importing certain datasets, I am running into an issue with the formatting. Spent all day working in conjunction with ChatGpt and have got nowhere.

​

I have made a dummy sheet to show the differences. The Sheet named "Lines" is what I am currently getting from my code. You can see the issue in column L where the information looks like this:

{spreadOpen=null, provider=William Hill (New Jersey), overUnderOpen=null, homeMoneyline=null, overUnder=54, formattedSpread=Kansas State -12, spread=12, awayMoneyline=null}

instead of:

I have another sheet named "CSV from CFB Data" as an example of what it should look like. Here is a link to the spreadsheet. Here is the code I am currently working with (API Key removed):

​

// Define functions for each menu item

function getLines() { // Invoke the common function with specific parameters importDataFromAPI("Lines", "https://api.collegefootballdata.com/lines"); } // Common function for making API requests function importDataFromAPI(sheetName, apiUrl) { // Open the spreadsheet by ID var spreadsheetId = "spreadsheet ID"; var spreadsheet = SpreadsheetApp.openById(spreadsheetId);

// Check if the sheet exists, if not, create it var activeSheet = spreadsheet.getSheetByName(sheetName); if (!activeSheet) { activeSheet = spreadsheet.insertSheet(sheetName); }

// Set the API key in the headers var headers = { "Authorization": "Bearer API Key*" };

// Set the request parameters var year = 2023; // Set the desired year var params = { method: "get", headers: headers, muteHttpExceptions: true };

try { // Make a GET request to the API var response = UrlFetchApp.fetch(apiUrl + "?year=" + year, params);

// Log the response content for troubleshooting
console.log("Response Content:", response.getContentText());

// Check if the response is valid JSON
var responseData;
try {
  responseData = JSON.parse(response.getContentText());
} catch (jsonError) {
  console.error("JSON Parse Error:", jsonError);
  return;
}

// Check if the response contains an 'error' property
if (responseData.error) {
  console.error("API Error:", responseData.error);
  return;
}

// Access the data you need from the response
var data = responseData; // Adjust this line based on your API structure

// Clear existing data in the sheet
activeSheet.clear();

// Implement additional logic specific to 'getLines'
// This can include any specific processing you want to do with the 'data' array
// For example, you can log specific fields, manipulate the data, etc.

} catch (error) { console.error("Error:", error); } }

Again, mostly written by ChatGpt. The beginning is probably a little weird, that's just so I can run the script off a button I have added to the UI with a Custom Menu. The script works fine, other than the formatting for "lines". I have looked at this which is linked from CFB Data, but it hasn't helped me:

Responses

Response content type

application/json

successful operation

Example Value

Model

[

{ "id": 0, "season": 0, "week": 0, "seasonType": "string", "startDate": "string", "homeTeam": "string", "homeConference": "string", "homeScore": 0, "awayTeam": "string", "awayConference": "string", "awayScore": 0, "lines": [ { "provider": "string", "spread": 0, "formattedSpread": "string", "spreadOpen": 0, "overUnder": 0, "overUnderOpen": 0, "homeMoneyline": 0, "awayMoneyline": 0 } ] } ]

Any help would be much appreciated!

I have no experience writing any real code. I work with spreadsheets for my job so I am familiar and have built something of a CFB model all in Google Sheets. It has all been built on imports and formulas, with a few scripts/macros here and there but nothing very impressive.

​

I have spent a few hours trying to link CFBdata to my google sheets with the API, but have not had any luck. I will teach myself to code eventually but with a job and a <1 year old baby, just not happening right now.

​

Anybody able to help with this? Much appreciated in advance for any and all advice.

Essentially I am trying to find individual games actual duration. Not the total in-game time, but the actual time it took from kickoff to the final whistle. There was a website about a month ago I found that had that information in it's boxscore IU believe, but I didn't bookmark it at the time and have been racking my brain trying to find it again

How can I search cfbd data by player name? Alternatively, how can I generate a list of all player_ids and the associated names from year 2010+

Hey y'all,

As a Notre Dame fan, dealing with the option offense is a pretty big concern due to our yearly game against Navy plus occasional games against Army and Air Force. In discussions of these matchups by fans and analysts, you often find the claim that defensive experience against the option is an important factor: the more experience a defense has against the option, the better we can expect them to perform.

I'm working on a project that tests this claim, and I'd really appreciate some feedback! The project notebook can be found on my Github. I'm planning to include it in a data science portfolio, so it's written for a more general audience and contains a lot of code.

I looked at play-by-play data from collegefootballdata.com and found confirming evidence that prior experience does actually improve a defense's performance against the option. The results suggest that inexperienced defenses can expect to give up over a touchdown more per game against option offenses than their highly experienced counterparts.

Thanks!

https://www.sevenyardsback.com/post/the-points-after-2023-fbs-kicker-ranking

Hello everyone!

If you saw my last post, I ended up going with sports-reference.com to supply the data for my app. Now that I have the data, I am looking to use it to make hypothetical scores between past teams, think 2001 Miami against 2019 Alabama.

With sports-reference I was able to pull Total yards, both passing and rushing for both offense and defense (yards allowed). I also got Points per game and points allowed per game.

Now the final piece of the puzzle would be somehow adding the strength of schedule into the equation. Within in the data I have, I have a SRS and SOS score for each of the teams.

The way I am doing my current hypothetical games:

Team A Passing yards= (Team A Average Passing Yards+ Team B Average Passing Yards Allowed/2)
Team A Rushing yards= (Team A Average Rushing Yards+ Team B Average Rushing Yards Allowed/2)
And vice versa.
The for the scores, I could do:
Team A Score: ((Team A Points Per Game+ Team B Opp Pts/G)/2)
Team B Score: ((Team B Points Per Game+ Team A Opp Pts/G)/2)

With data with Georgia 2022 and Florida 2022 it would look like:

So with this we could say that Georgia would win 35- 22
Georgia would have:
Passing: 265.85
Rushing: 190
Total Yards: 455.95
Florida would have:
Passing: 221.75
Rushing: 138.65
Total Yards: 360.3
Which compares to their real life match up as:
Georgia wins 42 to 20.
Georgia had:
Passing: 316
Rushing: 239
Total Yards: 555
Florida had:
Passing: 271
Rushing: 100
Total Yards: 371

So close, but I think figuring in SOS or SRS somehow could make this model better.

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?

​

Here is what I believe the right way to do the playoffs is. First of all, all computer should always "rank" teams based on strength of record, if you're trying to do so descriptively. Once you have your power rating, it's a fairly trivial thing to calculate. For those who don't know, all you do is pick some arbitrary strength rating, simulate such a team's performance against a team's schedule, and then add up the odds that they get AT LEAST as many wins vs that schedule. Lowest odds is ranked highest. What that does is utilize legitimate predictive computer systems to more accurately describe how good a team actually is (and therefore how hard a given schedule is). Then you can calculate how hard is was to win the games they did. It's the best of both worlds.

So the NCAA should select maybe 3 or 4 computers that have a long demonstrated history of success in accurate prediction. They could even open up a multi-year submission process. They purchase the rights to use these formulas, and as a result, the formulas are made completely public. This way, the proprietors get their money and the fans get transparency. We need to be transparent. Using multiple computers will minimize allegations of being able to "gain the system".

From there, you average the computer rankings and seed accordingly. So easy. So painless. Everybody wins. Conspiracy loses. Games matter. Tough schedules matter. Winning matters. How hard your schedule was is accurately reflected (unlike in the Colley matrix which is just too simplistic to accurately capture the complexities of a 12 game college football season). Everything matters.

I know it's a pipe dream, but I just have to believe that in 2023, there's a better way to do this. As educated statisticians and fans of college football, what are your thoughts on such a system?

Hello!

I am attempting to make an app that compares CFB teams against eachother throughout the years. I have been trying to find a file or collection of files that would have stats and metrics of every team, across a time span of 10 to 30 years. I have been able to find files with stats for one team, in one year, but I haven't been able to find anything with all the teams, in all the years. I thought this might be the place to ask before I start doing needless, repetitive downloading and assembly.

I might be missing a download somewhere, but I couldn't find one for everything across multiple years. Any help would be appreciated, thank you!

WELCOME TO THE WEEK 13 RESULTS OF THE 2023 CFB RP POINTS STANDINGS!

My mathematical formula ranks teams based on how many points they earn over the course of the season (similar to the NHL and MLS), and the value of each win or loss is based on the Massey Composite Rating. These rankings will be posted weekly here on r/CFBAnalysis.

Click the links below to see past rankings and how the formula works.

Preseason Rankings/Formula

Week 1 Rankings

Week 2 Rankings

Week 3 Rankings

Week 4 Rankings

Week 5 Rankings

Week 6 Rankings

Week 7 Rankings

Week 8 Rankings

Week 9 Rankings

Week 10 Rankings

Week 11 Rankings

Week 12 Rankings

WEEK 14 MATCHUPS

RANKED MATCHUPS

• #1 Michigan vs #17 Iowa
• #2 Washington vs #7 Oregon
• #3 Georgia vs #8 Alabama
• #4 Florida State vs #15 Louisville
• #20 Tulane vs #24 SMU

KEY MATCHUPS

• #5 Texas vs Oklahoma State
• #19 Toledo vs Miami (OH)
• #11 Liberty vs New Mexico State
• #21 Troy vs Appalachian State
• UNLV vs Boise State

WEEK 13 RANKINGS

My flair will tell you that I am in pain, but this isn't about me, this is about the points standings. The 4 team playoff is still up for grabs and 7 teams are eligible (compared to the 8 teams eligible in the committee rankings).

This is where the formula falls short, as Ohio State is mathematically eliminated from the playoff regardless of championship week results, and the formula cannot possibly account for a Florida State team without their starting quarterback. This year has made me realize that I might need a new metric to base TeamValue off of, as I don't think this iteration of the formula is accurately valuing the quality of each teams resume, especially when it comes to game control and value points.

EXAMPLES:

• Washington's resume is propped up on early season blowout wins despite looking shaky for the past month.
• Michigan's most valuable win is at Penn State rather than their win over Ohio State, simply because it was away from home. Are away wins being valued too much, or is their not enough separation in TeamValue to truly value the gap between teams.
• Oklahoma State's resume is massively punished by the South Alabama loss, but they aren't getting enough credit for their wins over Kansas and Oklahoma. Their West Virginia win is actually the most valuable win on their resume, that is not right.

​

Tl;Dr at the bottom

This year there are 8 P5 teams with 1 or 0 losses heading into championship week. The previous record for this point in the season in the CFP era is 7. Despite that, there are only 3 teams that are good enough to win a championship.

I built a model that identifies "championship caliber" teams. The model reports how closely a team's offensive an defensive efficiencies match that of a championship-winning team. I cannot stress enough that this model is DESCRIPTIVE, not predictive, so it cannot with any certainty say how likely a team is to win the championship, nor is it designed to predict which team is most likely to win the championship. The model has a binary output; either a team is championship-caliber, or they aren't.

HOW TO INTERPRET THESE RESULTS: As stated above, the categorization of teams should be considered binary (either championship-caliber or not). Given the tuning of this model, the success threshold is 93%. So any teams with a match % above 93% should be considered championship caliber. This year, that means Michigan, Georgia, and Oregon. That being said, there's still 1 more week for the numbers to change, but they aren't likely to change significantly.

HOW THE MODEL WAS BUILT: The model uses data starting with 1998, the year the BCS was instituted, and therefore, an official championship game. I also tested the model with data going back to 1970, but got worse results as champions before 1998 were determined purely subjectively. This is a logistic model, with hyperparameters tuned such that the most important factor was correctly identifying teams that win championships as championship caliber (true positive). The secondary goal is to minimize the number of total identified teams while maintaining the first goal (so minimize false positives). I believe this model accomplishes this quite well. When back testing, the model correctly categorizes 25 of 26 championship teams (yes, there was a split champ 1 year before the PAC-12 integrated into the BCS.) The lone outlier was 1998 Tennessee. The model identifies 3.2% of all teams as championship-caliber, which with 133 teams would translate to 4.2 teams per season. Although the number of identified teams seems to be trending down in the CFP era. I believe the existence of the 4-team playoff somewhat validates this model, and vice versa. I think it's reasonable to say that there are about 4 teams in a given year that really are good enough to win a championship, even though, no matter how many good teams there are, there can only be one champion.

Now I know what you're saying "well we already have 4 teams in the playoff, aren't those just the four teams who are good enough to win?". NO, of course that's not the case. The CFP has always been about finding the right blend between best and most deserving teams so that everyone can feel like they had a fair shot at the championship. In reality, this model would not have identified 14 out of the 36 playoff teams as "championship-caliber", a classification I would call "Imposters". And hey, while we're at it, I'll tell you that by far the #1 imposter according to my model (and it wasn't even close) was the 2015 Michigan State team that got embarrassed by Alabama in the semifinal.

For a more in-depth explanation of the model, I will be posting a full description this offseason, along with my answer to the question "Does defense win championships?".

Tl;Dr: The model considers opponent-adjusted offensive and defensive efficiency and determines how closely a team's efficiency profile matches an average champ-winning team. 93% Match is considered championship caliber (the output is binary, so they either are or they aren't). Don't use it to rank teams.

WELCOME TO THE WEEK 12 RESULTS OF THE 2023 CFB RP POINTS STANDINGS!

My mathematical formula ranks teams based on how many points they earn over the course of the season (similar to the NHL and MLS), and the value of each win or loss is based on the Massey Composite Rating. These rankings will be posted weekly here on r/CFBAnalysis.

Click the links below to see past rankings and how the formula works.

Preseason Rankings/Formula

Week 1 Rankings

Week 2 Rankings

Week 3 Rankings

Week 4 Rankings

Week 5 Rankings

Week 6 Rankings

Week 7 Rankings

Week 8 Rankings

Week 9 Rankings

Week 10 Rankings

Week 11 Rankings

WEEK 13 MATCHUPS

RANKED MATCHUPS

• #1 Michigan vs #3 Ohio State
• #7 Oregon vs #23 Oregon State

KEY MATCHUPS

• #2 Washington @ Washington State
• #5 Florida State @ Florida
• #9 Louisville vs Kentucky
• #13 James Madison @ Coastal Carolina
• #18 Iowa @ Nebraska
• #19 LSU vs Texas A&M
• #20 Tulane vs UTSA

WEEK 12 RANKINGS

Rivalry Week is here, and we continue to have the same 9 teams eligible for the 4 team playoff. One is likely to be eliminated in The Game up in Ann Arbor this weekend and odds are we dont see chalk across the rest of the board. Chaos could be coming as the conference championships loom.

Georgia continues their climb, finally reaching the top 4 for the first time since Week 7, and it is likely they will be in the top 2 with a win this weekend. That's how close everything is at the top! The winner between Ohio State and Michigan will have one foot in the playoff, and the inside track to the #1 seed in the points standings.

​

USC fan here still reeling from the trauma of having an all-time great college QB and all-time horror show college defense on the same team. As SC embarks on what I hope will be a program saving DC hire, I was curious what's the analytics™ approach to evaluating candidates for coordinator roles?

It feels like it's always just a who's hot pick based off of one or two recent good years. Team defense rankings are always just based on counting stats and I rarely see any SOS-adjusted rankings or ELO ratings or whatever. I admit my advanced stat knowledge is fairly limited when it comes to football so maybe I just don't know where to look. But I saw this chart that did a decent job demonstrating the quality of a team a new coach inherits and it made me think about DCs. I'd want to know which DC overperformed given his 'Inherited Team Talent Composite' on defense and adjusted for strength of offense schedule. Is that data out there? Or are there other ways to better quantify DC performance?

Thanks in advance for helpful answers!

Looking for things like Success Rate+, Explosiveness+, etc. I know things like SP+ adjust for strength of opponent, but I'm looking for more of the building blocks (eg; Opponent adjusted Success Rate, Opponent Adjusted Explosiveness, etc)

Is there a source for snap counts by player?

WELCOME TO THE WEEK 11 RESULTS OF THE 2023 CFB RP POINTS STANDINGS!

My mathematical formula ranks teams based on how many points they earn over the course of the season (similar to the NHL and MLS), and the value of each win or loss is based on the Massey Composite Rating. These rankings will be posted weekly here on r/CFBAnalysis.

Click the links below to see past rankings and how the formula works.

Preseason Rankings/Formula

Week 1 Rankings

Week 2 Rankings

Week 3 Rankings

Week 4 Rankings

Week 5 Rankings

Week 6 Rankings

Week 7 Rankings

Week 8 Rankings

Week 9 Rankings

Week 10 Rankings

WEEK 12 MATCHUPS

RANKED MATCHUPS

• #4 Washington @ Oregon State

KEY MATCHUPS

• #5 Georgia @ Tennessee
• #8 Texas @ Iowa State
• #11 Louisville @ Miami
• #18 Kansas State @ Kansas
• #20 North Carolina @ Clemson
• #25 Utah @ Arizona

WEEK 11 RANKINGS

The 11 teams eligible for the 4-team playoff is down to 9, as Ole Miss and Penn State both suffered their 2nd losses of the season at the hands of top 5 teams. Again, I suspect the top 8 in the committees rankings will feature the same 8 teams seen here, however in a different order.

Georgia is once again looking like the best team in the country, however they still remain in 5th place in the standings for the third week straight. That is a deceiving statistic though, as for the fourth straight week, they have reduced the gap between themselves and the top 4.

After Week 8, Georgia sat in 9th place following their BYE, and 14.183 points adrift from the top 4. Since then that deficit has reduced to 9.752, 8.865, and 5.387 points in respective weeks. A win this week would at minimum put them above Florida State, and potentially above Ohio State. Regardless, this will likely be the Bulldogs last week outside the top 4 for quite some time.

​

Where would be the best to find this per CFB Team for the 2023 Season?

Thanks in advance,

WELCOME TO THE WEEK 10 RESULTS OF THE 2023 CFB RP POINTS STANDINGS!

My mathematical formula ranks teams based on how many points they earn over the course of the season (similar to the NHL and MLS), and the value of each win or loss is based on the Massey Composite Rating. These rankings will be posted weekly here on r/CFBAnalysis.

Click the links below to see past rankings and how the formula works.

Preseason Rankings/Formula

Week 1 Rankings

Week 2 Rankings

Week 3 Rankings

Week 4 Rankings

Week 5 Rankings

Week 6 Rankings

Week 7 Rankings

Week 8 Rankings

Week 9 Rankings

WEEK 11 MATCHUPS

RANKED MATCHUPS

• #2 Michigan @ #7 Penn State
• #4 Washington vs #23 Utah
• #5 Georgia vs #11 Ole Miss
• #17 Tennessee @ #22 Missouri

KEY MATCHUPS

• #9 Oregon vs USC
• #3 Florida State vs Miami
• Rutgers @ Iowa
• Duke @ North Carolina
• Florida @ LSU

WEEK 10 RANKINGS

There are now 11 teams in the running for the 4-team college football playoff, and all 11 sit inside the top 12 of the points standings this week. JMU is the only outlier of the group and their forecasted slide down the standings has already begun.

Georgia is the only undefeated team that the formula has a slightly different opinion about than the committee, and only becuase they are currently in the most difficult part of their schedule and haven't earned those points yet.

The 1 loss teams are also ordered slightly different, with Texas leading the way with now 3 ranked wins on their resume compared to Penn State, Oregon, and Alabama's three combined.

This is another eliminator week, as 4 playoff eligible teams are playing ranked opponents, and at least 1 is guaranteed to lose.

​

Looking at the run game, I find that Line Yards, secondary yards, and open field yards tell a really good story of the run game. I haven't found any think equivalent for the pass game.

Do y'all have any good passing stats you like? I'm thinking some combination of Average Depth of Target, Average Depth of Completion, Average Yards after Catch would paint a good picture of the passing game? But I don't know where I could find this data...

Any ideas for useful passing play data and where to find it?

WELCOME TO THE WEEK 9 RESULTS OF THE 2023 CFB RP POINTS STANDINGS!

My mathematical formula ranks teams based on how many points they earn over the course of the season (similar to the NHL and MLS), and the value of each win or loss is based on the Massey Composite Rating. These rankings will be posted weekly here on r/CFBAnalysis.

Click the links below to see past rankings and how the formula works.

Preseason Rankings/Formula

Week 1 Rankings

Week 2 Rankings

Week 3 Rankings

Week 4 Rankings

Week 5 Rankings

Week 6 Rankings

Week 7 Rankings

Week 8 Rankings

WEEK 10 MATCHUPS

RANKED MATCHUPS

• #4 Washington @ #17 USC
• #5 Georgia vs #18 Missouri
• #6 Texas vs #19 Kansas State
• #10 Alabama vs #20 LSU

KEY MATCHUPS

• #7 James Madison @ Georgia State
• #11 Oklahoma @ Oklahoma State
• #12 Ole Miss vs Texas A&M
• #13 Notre Dame @ Clemson

WEEK 9 RANKINGS

The formula is finally nearing its full intended strength. I suspect that the top 6 teams in this weeks standings will be the top 6 teams in the committees initial rankings tonight, although likely in a slightly different order.

#1 Michigan and #2 Ohio State are separated by only 0.184 points which is essentially a dead tie. If both teams enter the final week of the season undefeated, expect them to be #1 and #2 here and in the committee rankings.

There is a huge drop-off after Washington at #4, mostly due to Georgia not having played their toughest games yet (which will be the next three weeks). I fully expect them the climb up into the top 3 if they continue to win.

JMU and Air Force are still up there as a result of their valuable early season schedules, but if you look at the points in terms of avg value of a win for each team, JMU and Air Force sit at #21 and #24 respectively, which very close to their actual AP Poll rankings. I expect them to slowly drop back down the standings as the clear playoff contenders emerge.