SportsDataTutorials
28 tutorials

Basketball

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

Begin with the ratings-table tutorial to learn the ORtg/DRtg/Net vocabulary the rest of the track speaks, then the home-court and shot-chart builds. The modeling arc — logistic regression, k-NN, cross-validation, ROC curves — uses NBA games as its running example and is the site's most complete from-scratch machine-learning sequence; take it in listed order.

Level:
A current-standings DataFrame from nba_api, with the proper headers baked in.
Basketball Beginner

Pull Your First NBA Data with nba_api

Pull NBA standings with nba_api, with the browser headers and retry logic stats.nba.com demands. Includes exactly what to do when the endpoint refuses to answer.

~9 min
A two-condition filter that finds the contenders, highlighted on a chart.
Basketball Beginner

Filtering and Querying a DataFrame

Use boolean masks and .query() to select the rows matching multiple conditions, then highlight that filtered set on a chart of every NBA team.

~5 min
A ranked net-rating table styled like a real dashboard, exported as an image.
Basketball Intermediate

Build a Team Net-Rating Dashboard Table

Combine offensive and defensive ratings into a ranked net-rating table, then style it into a dashboard-quality figure you can drop into a report.

~8 min
A half-court drawn in matplotlib with a player's makes and misses plotted on it.
Basketball Intermediate

Draw an NBA Shot Chart with matplotlib

Draw a regulation half-court from scratch in matplotlib, then plot a player's makes and misses in court coordinates for a real, shareable shot chart.

~10 min
A hexbin heatmap of where the entire league takes its shots.
Basketball Intermediate

Draw a League-Wide NBA Shot Heatmap

Plot thousands of NBA shots as a hexbin density map on a court you draw yourself, revealing the modern fingerprint: the rim and the three-point line.

~8 min
An Elo rating system built in ~20 lines over a real 2023-24 NBA season, with a rating-progression chart and the final-standings ranking it discovers on its own.
Basketball Intermediate

Build an Elo Rating System from Scratch

Code Elo in about 20 lines: run a real 2023-24 NBA season in date order and watch the ratings discover the standings on their own, plus a home-court edge.

~6 min
Two hexbin maps of 25,000 real NBA shots - a density map of where shots come from and an efficiency map of the make rate by location.
Basketball Intermediate

Make a Shot-Density Heatmap with hexbin

Turn 25,000 real NBA shots into two hexbin maps — where shots come from, and the make rate by location — exposing the cold mid-range the league abandoned.

~5 min
A permutation test on the NBA home-scoring edge: shuffle home/away thousands of times, build the null distribution, and read a p-value off it.
Basketball Intermediate

Is the Difference Real? A Permutation Test

Home teams scored 2.16 more points a game in 2023-24. Shuffle home/away thousands of times to build the null distribution — the edge is real (p < 0.0001).

~5 min
Replace tutorial 72's single train/test split with 5-fold cross-validation - train five times, test on every game exactly once - to get a stable accuracy estimate (67.8%) and, just as important, its spread across folds.
Basketball Advanced

K-Fold Cross-Validation: A Score You Can Trust

Replace one train/test split with 5-fold cross-validation on the NBA home-win classifier: folds range from 61.8% to 72.4% and average 67.8%. One split can lie.

~5 min
Build a ROC curve and its area (AUC) from scratch in pure numpy for the tutorial-71 home-win classifier: sweep the threshold to trace true-positive vs false-positive rate, then compute AUC two independent ways (trapezoid area and the rank identity) and watch them agree at 0.755.
Basketball Advanced

ROC Curves and AUC From Scratch

Sweep the threshold on the NBA home-win classifier to trace a ROC curve, then compute AUC two independent ways — trapezoid area and rank identity — both 0.755.

~6 min
Build k-nearest neighbors from scratch in pure numpy - no training, just store the games and let the k closest ones vote - on a 2-D feature space (home and away net rating) to predict the NBA home win, then sweep k and compare to the tutorial-71 logistic regression.
Basketball Advanced

k-Nearest Neighbors From Scratch

Build k-NN in pure numpy on both teams' net ratings to predict NBA home wins, then sweep k from 1 to 101: k=1 overfits at 55.8%, big k smooths to 66.2%.

~6 min
Grow a decision tree from scratch in pure numpy on the NBA home-win problem: measure purity with Gini impurity, brute-force the best (feature, threshold) split, recurse to depth 3, then read the flowchart it learned and compare its accuracy to logistic regression and k-NN.
Basketball Advanced

A Decision Tree From Scratch

Grow a depth-3 decision tree in pure numpy — Gini impurity, brute-force splits, recursion — on NBA home wins. It scores 64.6%, and every decision is readable.

~5 min