Fundamentals Library · 2026

Sport Data Analysis
Fundamentals

Two professional, self-contained handbooks that teach how modern sport is measured, modelled and explained — from raw data to a report a coach can act on.

For analysts · scouts · coaches ~90-minute reads Python & visualization included Interactive charts

Both volumes follow the same arc and the same belief: data is only worth the decisions it improves. Each one moves from foundations and data types, through advanced metrics and models, into the tools and workflow of the job, and finishes with worked case studies that end in a real tactical or scouting report. Read either cover-to-cover, or jump in via its sidebar. Pick a sport to begin.

Football · Soccer

Football Data Analysis

Understand football through data — xG, expected threat, possession value, pressing metrics and player clustering, all the way to a full tactical report.

xG & shot maps xT & VAEP PPDA & pressing Python · mplsoccer
Open the handbook →
Basketball

Basketball Data Analysis

Understand basketball through data — efficiency and the Four Factors, shot value, impact metrics and lineups, all the way to a full scouting report.

TS% & shot charts Four Factors RAPM & EPM Python · nba_api
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What's in every volume

A shared four-part structure, twenty chapters, the same standard of rigour.

📊

Foundations & data

Data types, collection systems and a scientific approach to evidence.

🎯

Metrics & models

The advanced metrics that turn raw data into meaning and value.

🛠️

Tools & workflow

Hands-on Python, visualization and the data-to-video pipeline.

📝

Interpretation

Worked case studies ending in a real tactical or scouting report.