Pull real MLB, NBA, soccer, NFL and NHL data, analyse it in Python, and build charts you made yourself - one focused tutorial at a time.
The build system only publishes a chart or a printed result if the tutorial's script actually produced it when it was run. Nothing is mocked up, and no number is typed in from memory - if the code on the page can't generate it, it doesn't appear.
Every Python block has a Run button, powered by Pyodide. Edit it, break it, re-run it - the real datasets each tutorial uses are bundled and pre-loaded, so there's nothing to install before you start learning.
All 83 tutorials declare their prerequisites and build on each other: foundations first, then the sport track you care about, then statistics and machine learning implemented from scratch. The learning path shows the whole spine.
Statcast, StatsBomb Open Data, nflverse, and the NBA and NHL public APIs. Every figure captions its source and retrieval date, and every finished script is downloadable with the sample data it needs to run offline.
Every tutorial belongs to exactly one course, in a deliberate order. Each course page carries a syllabus, a one-ZIP bundle of all its code and data, and progress you can tick off as you go.
Zero to competent: install Python, learn pandas on real standings, and finish by building a league table from raw results.
Ten chart types, each built on real sports data, ending with the styling pass that makes a figure publishable.
The measurement toolkit: distributions, z-scores, percentiles, outliers, time series — closing with what actually predicts winning.
Monte Carlo seasons, bootstrap intervals, permutation tests, chi-square, Bayesian updating and best-of-seven series math — how to tell signal from luck.
Nine models and methods — gradient descent to decision trees — every one implemented in pure numpy on real NBA data.
Read API docs like a developer, then pull your first real data from MLB, the NBA, StatsBomb, nflverse and the NHL.
A capstone: ask one question - how big is home advantage? - across five leagues, reusing everything you've learned, and put the answers on a single comparison chart.
Read the capstone →
Turn a single-game win probability into exact best-of-seven odds in pure numpy, verify it by Monte Carlo, and read the 2024 Finals off the amplification curve.
Build a 2x2 table from 25,000 real NBA shots and compute the chi-square test by hand in numpy — expected counts, the statistic, and a clean null verdict.
Test 'the knockouts tighten up' on a real 96-match 2026 World Cup snapshot with one pandas groupby — goals per game, both-scored and goalless rates by stage.
Flag blowouts in 1,231 real NBA margins three ways — IQR fences, the median/MAD modified z-score, and the fragile plain z-score — and see why robust wins.
Smooth one NBA team's margin series two ways — a 10-game rolling mean versus pandas .ewm() — and watch the EWMA react faster to streaks without a hard window.
Start from a flat Beta(1,1) prior, feed it 1,231 real NBA games, and watch the posterior sharpen into a tight home-win estimate with a 95% credible interval.
Sport-agnostic skills - Python, pandas, plotting and data cleaning - that every other tutorial builds on.
23 tutorials →MLB Statcast is the richest free data in sports. Pull it with pybaseball and build leaderboards, heatmaps and more.
15 tutorials →The NBA's stats API is powerful but picky. Learn to pull it politely, then build standings, ratings and shot charts.
28 tutorials →Free event data from StatsBomb plus xG from Understat let you analyse matches like a pro - no paid feed required.
7 tutorials →nflverse opens up every NFL play. Compute EPA, compare quarterbacks, and learn the modern football-analytics toolkit.
6 tutorials →The NHL's public API needs no key. Pull standings and play-by-play, then plot shots on a rink you draw yourself.
4 tutorials →