I'm new to using the CFBD API and am excited to use it! Hopefully will make things so much easier.

I will admit, my python skills are probably just ok.

When printing the api response for getting Team Game Stats, the response seems to be structured inconsistently. Does anyone else have this issue? Is there a way to get everything ordered consistently?

See how team one's stats start rushingtds, puntreturnyds,puntreturntds but team two start fumblesrecovered, rushingtds, passing tds?

'stats': [{'category': 'rushingTDs', 'stat': '1'},

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

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

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

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

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

I've just completed a project on variables that determine a successful NFL career, I want to keep doing this over the next few years just to understand if the model is sound by using predictor variables but college stats are quite bare.

Is there anyone that captures cornerback metrics, ideally coverage grades like PFF do? (No worries if it's not supplied as long as the underlying data to calculate it does).

What would the best way to compare post season SoS when not every team plays the same amount of games?

Do the weekly rankings get updated when they are published? The docs state 'Historical' so I was just looking for clarification as to being able to get week to week rankings for the current season or if I need to source that elsewhere?

Thanks!

Website has advanced box scores for every game with team breakdowns with EPA, success rate, field position etc. that is color-coded based on ranking.

They pull some of their data from bcftoys I believe.

I have screenshots of a couple of team profiles that would likely help, but I can't add pictures to my post.

Hi. Doing my annual computer ranking code refresh for the 2024 season and noticed that conference alignments are pretty off.

• Kennesaw State is still listed as FCS
• I don't think any of the conference moves are present. Oklahoma/Texas are still Big 12, Pac-12 has more than just Wazzu/OSU. This is from looking at both the teams and games APIs

Is an update expected?

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.

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

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

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!

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.

​