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 - every printed block on the page is captured stdout from that finished script, which you can download and run yourself.
On most tutorials the Python blocks carry a Run button, powered by Pyodide, with the bundled real datasets pre-loaded - edit the code, break it, re-run it, nothing to install. Where code needs live APIs or specialist libraries the browser can't load, the page says so and hands you the finished script to run locally instead.
Every tutorial after the first declares its prerequisites and builds on the ones before it: 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 where its numbers came from - and, where they came from outside, when they were retrieved - and every finished script is downloadable - with the bundled sample data where the tutorial has one, and the live-API tutorials pull their own.
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, Simpson's paradox — closing with what actually predicts winning.
Monte Carlo seasons, bootstrap intervals, permutation tests, power, chi-square, Bayesian updating, regression to the mean — 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, the NHL and ESPN.
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 →
Thirty exact binomial tests on one NBA season flag 13 teams. Bonferroni keeps 9, Benjamini-Hochberg 12 - and on the next screen both keep nothing.
Turn an NFL point spread into a win probability three ways on 7,261 games with a closing line: raw bins, the 13.2-point normal model, and logistic regression.
Audit StatsBomb xG on 1,494 WC2022 shots: shoot-out rows unmasked, +14 goals at z=1.39, five of six reliability bins hold, Brier skill +19.8%.
Compute rest for 2,462 NBA team-games with one groupby diff: back-to-backs erase home court (54.3% to 47.3%), and rested-vs-tired runs 41.9%.
Pull all 380 Premier League results from ESPN's keyless scoreboard API: dodge the silent 100-event cap, tidy the JSON, and guard the saved season file.
Compute PDO for every NHL team across two seasons: why the league averages exactly 100, how much of the standings it explains, and which half of it persists.
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.
33 tutorials →Free event data from StatsBomb plus xG from Understat let you analyse matches like a pro - no paid feed required.
10 tutorials →nflverse opens up every NFL play. Compute EPA, compare quarterbacks, and learn the modern football-analytics toolkit.
7 tutorials →The NHL's public API needs no key. Pull standings and play-by-play, then plot shots on a rink you draw yourself.
5 tutorials →