Simulate March Madness with a Monte Carlo Bracket (Python)
Turn team ratings into thousands of simulated tournaments with a few lines of Python, and estimate every team's odds to reach the Final Four or win it all.
Tutorials
Pull the data yourself. Runnable Python and spreadsheet walkthroughs for college football and basketball analytics.
Do it yourself. Every tutorial here builds one working thing — an Elo rating, a logistic win-probability model, an adjusted-efficiency engine, a Monte Carlo bracket, a chart that looks publishable — from a free, public data source, with the complete code printed on the page rather than hidden behind a download. They assume you can write a loop and nothing more: the API key, the caching layer, the cleaning steps, and the pitfalls that quietly corrupt a result are all spelled out. Start with the CFBD API walkthrough if you have never pulled sports data before, then the polite caching piece, which every other tutorial assumes you have read.
Build a Strength-of-Schedule-Adjusted Ranking in a Spreadsheet
No code required: build a simple opponent-adjusted rating in a spreadsheet, with the formulas and an iterative ranking explained.
Pull College Basketball Data with sportsdataverse: Men's and Women's Hoops
Use sportsdataverse and public endpoints to pull men's and women's college basketball schedules and box scores in Python.
From CSV to Chart: a Repeatable Analysis Pipeline with pandas
Wire a CSV into a clean, repeatable pandas-to-matplotlib pipeline you can rerun whenever the data updates.
Pull Your First College Football Data with the CollegeFootballData API (Python)
A beginner-friendly walkthrough: get a free API key, store it safely, and pull schedules and ratings with Python and cfbd.
Filter Garbage Time from Play-by-Play Data (Python)
Strip out garbage-time plays so your efficiency numbers reflect competitive football, not blowout filler.
Build a No-Framework Stats Dashboard with HTML, CSS, and JS
Build a fast, dependency-free stats dashboard that reads a JSON file: pure HTML, CSS, and vanilla JavaScript.
Make Publication-Quality Sports Charts with matplotlib
Give your sports charts a clean, consistent, publication-ready look with a reusable matplotlib style.
Cache API Calls Politely: a Reusable Fetch Helper (Python)
Stop hammering free APIs: build a polite, on-disk caching fetch helper you can reuse in every project.
Chart a Team's Scoring-Margin Trend Over a Season (Python)
Visualize a team's game-by-game scoring margin to see its real trajectory, hot streaks and collapses included.
Analyze Recruiting Class Talent in Aggregate with the CFBD API
Analyze team-level recruiting talent (aggregate only, no individual prospects) with the CollegeFootballData API.
Predict College Games with Logistic Regression (Python)
Train a simple logistic-regression model on ratings and results to predict game outcomes, and measure its accuracy.
Measure Home-Field Advantage with Real Results (Python)
Use a full season of real results to estimate how many points home field is actually worth in college football.
Two-Deep Strength of Schedule: Opponents' Opponents in Python
Go beyond average opponent record: fold in opponents' opponents for a sharper strength-of-schedule estimate.
Calculate Season-Long Success Rate from Play-by-Play (Python)
Aggregate a full season of play-by-play to compute a team's success rate by down, the right way to read efficiency.
Build Adjusted Efficiency Ratings for College Basketball (Python)
Iterate raw efficiency into opponent-adjusted offensive and defensive ratings, the engine behind every modern hoops rating.
Chart a Game's Win Probability with ESPN's Public API
Turn ESPN's public win-probability data into a clean game-flow chart in a few lines of Python.
Profile a Team's Tempo and Shot Selection in Python
Build tempo-free team profiles: pace, three-point rate, and free-throw rate, from public box scores.
Build a College Basketball Elo Rating and Test It (Python)
Adapt the Elo idea to college basketball, then back-test it against real results to see how well it predicts.
Build a College Football Elo Rating from Scratch in Python
Code a from-scratch Elo system for college football using real game results, then rank every FBS team. No advanced math required.
Measure Player Usage and Efficiency from Box Scores (Python)
Turn player box scores into usage rate and true-shooting efficiency to find who really drives an offense, men's or women's.
Compute Basketball's Four Factors in Python
Effective FG%, turnover rate, offensive rebounding, and free-throw rate: compute the four factors that decide basketball games.
Build a Quadrant and Team-Sheet Analyzer in Python
Sort a team's schedule into quadrants and build the kind of team sheet the selection committee actually reads.
Analyze College Stats with Spreadsheet Pivot Tables
No code required: pivot a real 877-game 2024 college football season by conference, month, and margin band in Excel or Google Sheets.