Hockey analytics, drawn simply.
Every explainer is built around one clear diagram, then broken down step by step. Skim the picture, then go as deep as you like.
- Foundations5 min read
Corsi vs Fenwick: which possession metric should you use?
Two shot-attempt metrics, one small difference — and when each one tells the truer story.
Read explainer - Data Stack6 min read
Getting started with DuckDB for hockey data
Why a single-file analytical database is the fastest way to start your hockey data stack.
Read explainer - AI & Claude5 min read
What is MCP, and why it matters for hockey analytics
The open protocol that lets an AI safely query your data — explained in one diagram.
Read explainer - Data Stack6 min read
How a hockey analytics data stack works
The end-to-end path from raw game data to an answer you can act on — drawn as one diagram.
Read explainer - AI & Claude5 min read
Using Claude to analyze your hockey data
How an LLM turns your data stack into a question-and-answer machine — safely.
Read explainer - Foundations5 min read
What is expected goals (xG) in hockey?
The single most useful hockey metric, explained from shot to season total.
Read explainer - Workflows & Craft4 min read
How to frame a hockey analytics question
The step great analysts never skip: turning a vague hunch into an answerable question.
Read explainer - Skills5 min read
Turn a prompt into a reusable AI skill
How to package a one-off AI request into a repeatable procedure your whole workflow can call.
Read explainer - Architectures7 min read
A reference architecture for hockey analytics
A full, opinionated blueprint tying data, models, AI and delivery into one system.
Read explainer