Sport-agnostic skills - Python, pandas, plotting and data cleaning - that every other tutorial builds on.
If you're new, run the Python setup tutorial first, then the twelve-operation pandas tour and the grouping-and-pivoting tutorial; every sport track assumes them. The statistics tutorials — summary stats and distributions, correlation and regression, z-scores — slot in whenever a sport tutorial leans on a concept you haven't met. Everything runs on bundled real data, in your browser if you like — no installs required to follow along.
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Install Python, a code editor, and the core data libraries the right way, then prove your setup works by running a tiny script that imports pandas and matplotlib.
Load, filter, sort, group, and join a real standings table. The twelve pandas operations here cover the vast majority of everyday sports-data work.
Go from a DataFrame to a clean, labeled bar chart with matplotlib. Learn the figure/axes model, titles, and how to save a chart you'd actually publish.
Endpoints, parameters, JSON, status codes - learn to read API docs by pulling live standings from the public NHL API and shaping the response into a DataFrame.
Meet your data before charting it: describe() summarizes a column in one line, and a histogram shows the distribution those numbers only hint at.
Combine a record table and a scoring table on a shared key with pandas merge, choose the right join type, and chart a column that lived in neither alone.
Season totals hide the rates that matter. Build runs per game, run differential per game, and win percentage with vectorized math, then chart them.
Use .apply() to run any function per row and .map() to translate codes to labels, turning raw win totals into the labeled tiers your analysis needs.
pd.cut() slices a continuous column into fixed-width bands you define — the companion to qcut() — turning raw run differential into labeled tiers, charted.
Mixed types, stray whitespace, duplicate rows, percent signs in numbers - fix the data problems that derail real sports projects, with before-and-after proof.
Go beyond groupby: build pivot tables, reshape between wide and long with melt and stack, and turn raw rows into the summary tables analysts actually present.
Pull a team's game log, sort by date, and use rolling windows and cumulative sums to measure form and momentum - the foundation of every 'hot streak' chart.
Turn a default matplotlib chart into a publishable figure: custom colors, direct labels, annotations that call out the story, and a high-res export.
Learn matplotlib's subplots to build small multiples - a grid of tiny charts that share axes so you can compare many teams or groups at a single glance.
Convert runs scored, runs allowed, and run differential to z-scores so one number means the same thing in every column, then read a diverging bar chart.
Where a histogram bins and smooths, the ECDF is exact: sort the values, assign each a running share, and read any percentile straight off the staircase.
Use ax.twinx() to give a chart a second y-axis so runs scored and win percentage fit on one plot — and learn when a dual axis quietly misleads.
Treat a team's winning percentage as true talent, then simulate thousands of 162-game seasons with weighted coin flips. The spread is the luck in a record.
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.
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.
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.
Quantify a relationship instead of eyeballing it: compute a correlation, fit a regression line with numpy, and report R-squared on NHL goal differential.
Reconstruct the standings week by week from a season of game results and draw a bump chart - the elegant ranked-line visual that shows every rise and fall.