The Scenario
You are a sports analytics student. Your professor assigned a semester project: build a custom power ranking model for every FBS team's 2024 season. You need rushing yards, passing yards, and points per game for all 133 FBS programs in a single Google Sheet before you can write a single line of Python.
It is Sunday afternoon. The model is due Thursday.
The bad version of this weekend:
- You find the CollegeFootballData.com API docs and figure out the right endpoint
- You write a Python script to loop across all 133 teams
- The API returns nested JSON — you spend an hour flattening it into a table
- You paste the CSV into Google Sheets and the column headers are all wrong
- You fix the headers manually, realize points per game is not in the response, go back to the API docs, find a different endpoint, and repeat
- It is Monday morning and you have not started the model.
The fast version is one prompt.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your spreadsheet that knows the CollegeFootballData.com API structure, so you do not have to.
Open the SheetXAI sidebar and type:
Pull all FBS team season stats for 2024 and paste them into a new sheet called "Team Stats 2024" with columns for team, conference, rushing yards, passing yards, and points scored, sorted by total points descending.
SheetXAI calls the CollegeFootballData.com API, parses the response, flattens it, and writes the full table into the sheet. All 133 teams, clean columns, sorted. You are building the model by Sunday evening.
What You Get
A clean table in "Team Stats 2024" with 133 rows and sorted columns:
- Team and conference — every FBS program identified
- Rushing yards — total season rushing yards per team
- Passing yards — total season passing yards per team
- Points scored — total season points, used as the sort key
The table is model-ready. No header cleanup, no JSON parsing, no format conversion. Open a new sheet, write your formula, reference the table.
Want advanced metrics alongside the basic stats? Ask SheetXAI to pull PPA, success rate, and havoc rate into the same sheet and it extends the table in the same session.
What If the Data Is Not Quite Ready
Power ranking models usually need more than one data type. SheetXAI handles multi-endpoint pulls and cleanup in the same prompt.
When you want advanced metrics in the same table
The basic stats endpoint does not include PPA or success rate. Those live on a different endpoint.
Fetch advanced season stats for all FBS teams in 2024 — including PPA, success rate, and havoc rate — and write them into my "Advanced Metrics" sheet with one row per team. Then add a column that ranks teams by offensive PPA from highest to lowest.
When conference names are inconsistent across endpoints
You pulled stats from two different endpoints and the SEC shows up as "SEC" in one and "Southeastern Conference" in the other.
Look at the conference column in the "Team Stats 2024" tab and the "Advanced Metrics" tab. Normalize conference names so they match across both tabs — use the short form (SEC, Big Ten, Big 12, ACC, Pac-12). Then add a conference column to the Advanced Metrics tab if it is missing.
When you only want Power 5 and Group of 5 conferences
Your model focuses on the major conferences, not FCS programs that occasionally appear in the data.
Filter the "Team Stats 2024" tab to Power 5 and Group of 5 conferences only — SEC, Big Ten, Big 12, ACC, Pac-12, American, Conference USA, MAC, Mountain West, Sun Belt. Remove any other rows. Then re-sort by total points descending.
When you want the full model-ready table in one shot
You want basic stats, advanced metrics, and a composite ranking score all in a single prompt before you open your Python notebook.
Pull all 2024 FBS team season stats — rushing yards, passing yards, and points scored — into column A through E of the "Power Rankings" tab. Then fetch advanced stats for the same teams and add PPA, success rate, and havoc rate in columns F through H. For each team, calculate a composite score using points scored (40%), offensive PPA (30%), and success rate (30%), put it in column I, and sort the full table by composite score descending.
The pattern: you describe the analytical question, not the API calls. SheetXAI figures out which endpoints to hit and how to combine them.
Try It
Get the 7-day free trial of SheetXAI and open a blank sheet, then ask it to pull all FBS team season stats for any year. The CollegeFootballData.com integration is included in every SheetXAI plan. For more, see how to export game-level PPA metrics or the CollegeFootballData.com in Google Sheets overview.