Learn sports analytics by actually doing it.

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.

Foundations and method

1. Python Foundations for Sports Data

   Zero to competent: install Python, learn pandas on real standings, and finish by building a league table from raw results.

   Beginner

   10 parts ~66 min total

2. Sports Data Visualization

   Ten chart types, each built on real sports data, ending with the styling pass that makes a figure publishable.

   Beginner to Intermediate

   10 parts ~60 min total

3. Statistics for Sports Data

   The measurement toolkit: distributions, z-scores, percentiles, outliers, time series, Simpson's paradox — closing with what actually predicts winning.

   Intermediate

   13 parts ~88 min total

4. Randomness, Inference & Simulation

   Monte Carlo seasons, bootstrap intervals, permutation tests, power, chi-square, Bayesian updating, regression to the mean — how to tell signal from luck.

   Intermediate

   11 parts ~109 min total

5. Machine Learning from Scratch

   Twelve models and methods — gradient descent to random forests — every one implemented in pure numpy on real NBA data.

   Advanced

   12 parts ~139 min total

6. Working with Real Sports APIs

   Read API docs like a developer, then pull your first real data from MLB, the NBA, StatsBomb, nflverse, the NHL and ESPN.

   Beginner

   7 parts ~65 min total

By sport

1. Baseball Analytics with Statcast

   Exit velocity, pitch maps, spray charts, luck-hunting and Pythagorean wins — the richest free data in sports, put to work.

   Intermediate

   7 parts ~49 min total

2. Basketball Analytics with the NBA API

   Ratings tables, shot charts, zone efficiency, the three-point revolution, rest and back-to-backs — and an Elo system you build yourself.

   Intermediate

   9 parts ~69 min total

3. Soccer Analytics with StatsBomb & xG

   Pass maps, shot maps, heatmaps and pass networks from free pro event data — then a complete World Cup: scoring by stage and a Poisson scoreline model.

   Intermediate

   8 parts ~66 min total

4. NFL Analytics with nflverse

   EPA built by hand, quarterback efficiency, play-calling tendencies, receiver value, the fourth-down decision, spreads as probabilities and coaching tenure.

   Intermediate

   9 parts ~105 min total

5. Hockey & Cross-League Projects

   Four NHL builds — shots, leaders, form, and the PDO luck flag — then the capstone: measuring home advantage across five leagues at once.

   Intermediate

   5 parts ~52 min total

All 11 courses

1. 14 Oct

   Random Forests: A Column of Noise Ranks Third

   A random forest in numpy on 923 NBA games: 500 trees vote, and the built-in importance ranks a column of random numbers third of six.

   Machine Learning from Scratch, part 12 of 12
   BasketballAdvanced~30 min

2. 11 Oct

   Gaussian Mixtures: EM Finds the Court's Zones

   EM by hand on 24,947 NBA shot locations: six Gaussians find the court's zones, and 704 shots logged at the rim break the likelihood.

   Machine Learning from Scratch, part 11 of 12
   BasketballAdvanced~31 min

3. 7 Oct

   Principal Component Analysis: Where a Box Score Hides Its Shooting

   PCA by hand on 279 NBA players: volume and inside-outside carry 63% of ten box-score rates, and the shooting signal hides in the smallest component.

   Machine Learning from Scratch, part 10 of 12
   BasketballAdvanced~28 min

4. 4 Oct

   Regression Discontinuity: What a Close Win Is Worth

   Regression discontinuity by hand on 13,904 NFL team-games: a close win buys nothing next week, and the balance check shows close games are not coin flips.

   Randomness, Inference & Simulation, part 10 of 11
   FootballAdvanced~23 min

5. 23 Sep

   Bradley-Terry Ratings: Strength From Who You Beat

   Fit Bradley-Terry ratings by hand with the MM algorithm: schedule-adjusted strength for every NFL team, and an honest out-of-sample test against win %.

   NFL Analytics with nflverse, part 9 of 9
   FootballIntermediate~11 min

6. 20 Sep

   Rank Correlation: How Much of an NFL Season Carries Over

   Kendall's tau and Spearman's rho by hand on 26 NFL season pairs: order persists at a median tau-b of 0.205, and vanishes inside the playoff field.

   Statistics for Sports Data, part 13 of 13
   FootballIntermediate~18 min

• Foundations

  23 tutorials

  Sport-agnostic skills - Python, pandas, plotting and data cleaning - that every other tutorial builds on.

• Baseball

  15 tutorials

  MLB Statcast is the richest free data in sports. Pull it with pybaseball and build leaderboards, heatmaps and more.

• Basketball

  36 tutorials

  The NBA's stats API is powerful but picky. Learn to pull it politely, then build standings, ratings and shot charts.

• Soccer

  10 tutorials

  Free event data from StatsBomb plus xG from Understat let you analyse matches like a pro - no paid feed required.

• Football

  12 tutorials

  nflverse opens up every NFL play. Compute EPA, compare quarterbacks, and learn the modern football-analytics toolkit.

• Hockey

  5 tutorials

  The NHL's public API needs no key. Pull standings and play-by-play, then plot shots on a rink you draw yourself.