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.
Pull the data yourself. Runnable Python and spreadsheet walkthroughs for college football and basketball analytics.
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.
No code required: build a simple opponent-adjusted rating in a spreadsheet, with the formulas and an iterative ranking explained.
Use sportsdataverse and public endpoints to pull men's and women's college basketball schedules and box scores in Python.
Wire a CSV into a clean, repeatable pandas-to-matplotlib pipeline you can rerun whenever the data updates.
A beginner-friendly walkthrough: get a free API key, store it safely, and pull schedules and ratings with Python and cfbd.
Strip out garbage-time plays so your efficiency numbers reflect competitive football, not blowout filler.
Build a fast, dependency-free stats dashboard that reads a JSON file: pure HTML, CSS, and vanilla JavaScript.
Give your sports charts a clean, consistent, publication-ready look with a reusable matplotlib style.
Stop hammering free APIs: build a polite, on-disk caching fetch helper you can reuse in every project.
Visualize a team's game-by-game scoring margin to see its real trajectory, hot streaks and collapses included.
Analyze team-level recruiting talent (aggregate only, no individual prospects) with the CollegeFootballData API.
Train a simple logistic-regression model on ratings and results to predict game outcomes, and measure its accuracy.
Use a full season of real results to estimate how many points home field is actually worth in college football.
Go beyond average opponent record: fold in opponents' opponents for a sharper strength-of-schedule estimate.
Aggregate a full season of play-by-play to compute a team's success rate by down, the right way to read efficiency.
Iterate raw efficiency into opponent-adjusted offensive and defensive ratings, the engine behind every modern hoops rating.
Turn ESPN's public win-probability data into a clean game-flow chart in a few lines of Python.
Build tempo-free team profiles: pace, three-point rate, and free-throw rate, from public box scores.
Adapt the Elo idea to college basketball, then back-test it against real results to see how well it predicts.
Code a from-scratch Elo system for college football using real game results, then rank every FBS team. No advanced math required.
Turn player box scores into usage rate and true-shooting efficiency to find who really drives an offense, men's or women's.
Effective FG%, turnover rate, offensive rebounding, and free-throw rate: compute the four factors that decide basketball games.
Sort a team's schedule into quadrants and build the kind of team sheet the selection committee actually reads.
No code required: use pivot tables to slice college stats by team, conference, and situation in any spreadsheet.