How to Use Python for Sports Analytics

Data manipulation: pandas or Polars. This is where most time goes: joining, reshaping, grouping, and handling missing values. pandas has the widest ecosystem and the most examples. Polars is faster on large tables such as multi-season play by play and has a stricter, more explicit API. Either is a sound choice; mixing them in one project usually is not.

Storage: Parquet files or a database. Parquet keeps types, compresses well, and loads quickly. A database such as DuckDB or PostgreSQL becomes worthwhile once you are joining several large tables or running the same queries repeatedly. DuckDB in particular can query Parquet files directly with SQL and requires no server.

Statistics: statsmodels and SciPy. For regression with interpretable output, confidence intervals, and classical tests. Most sports questions start here.

Machine learning: scikit-learn, plus a gradient boosting library. scikit-learn provides consistent interfaces, evaluation utilities, and calibration tools. Gradient boosting libraries are the usual next step for tabular data.

Visualisation: matplotlib, with seaborn or plotly on top. matplotlib gives full control for publication charts; the others speed up exploration.

Data access: community packages. Several sports have maintained Python packages that download cleaned public data. They save a great deal of work and come with their own update schedules and definitions, which are worth reading.

Separate collection from analysis. One script downloads raw data and saves it untouched with the date it was retrieved. Analysis reads only from saved files. This makes results reproducible, lets you rerun analysis without new requests, and keeps you from hammering sources that rate limit.

Keep a clean layer. A second step transforms raw data into tidy tables with consistent identifiers, types, and column names. Every analysis reads from this layer, so a fix applied once reaches everything.

Pin your environment. Record library versions in a lock file. Data packages and modelling libraries both change behaviour between versions, and an unpinned project can produce different numbers a year later.

Notebooks for exploration, scripts for anything repeated. Notebooks are ideal for looking at data. Once a step needs to run again, especially on a schedule, move it into a script or module with a clear entry point.

Version control everything except the data. Code and configuration go into git. Large data files go into storage with a documented path and retrieval date.

A minimal project layout

Random train and test splits. Sports data has strong time structure. Randomly splitting lets a model learn from games played after the ones it is predicting. Split by date or season, and for anything serious, evaluate across several cut points.

Rolling features that include the current game. A rolling average computed with default settings often includes the row being predicted. Shift features so each row only sees earlier games.

Joining on names. Player and team names differ across sources. Join on persistent identifiers and keep a mapping table for anything that lacks one.

Ignoring season boundaries. A rolling window that runs across the off-season mixes two different rosters. Decide explicitly whether windows reset at season boundaries.

Silent type problems. Dates parsed as strings, identifiers read as floats that drop leading zeros, and mixed time zones all produce joins that quietly lose rows. Check row counts before and after every join.

Treating downloaded model columns as ground truth. Many community datasets include derived columns such as expected points or win probability. They are estimates from another model, and they change between releases.

This page describes tooling and method and is not betting advice.

Check row counts after every join