Become the best hockey brain.
Clear, visual guides to hockey analytics — how to read the game with data, build your own data stack, and use AI like Claude. For anyone who wants to actually get good, not just read theory.
Free to read · no account needed.
New to hockey analytics? Start with the foundations or follow the roadmap.
Four steps that turn raw game data into a better decision — the path every analyst learns.
Game data
- APIs
- Play-by-play
- Scrapers
Your data stack
- DuckDB / MotherDuck
- Modeled tables
Metrics & AI
- xG, Corsi
- Claude + skills
Better calls
- Scouting
- Game prep
Six pillars. One hockey brain.
Each pillar is a topic cluster of visual explainers — from first principles to the architectures and AI workflows pros actually use.
Foundations
What hockey analytics actually is, how to read the game with data, and the metrics that matter.
ExploreData Stack
How to build a hockey data pipeline: sources, ingestion, storage, transforms and serving.
ExploreAI & Claude
Use LLMs and agents to analyze hockey: prompting, tool use, MCP and retrieval over your own data.
ExploreSkills
Reusable AI skills and playbooks: scouting reports, opponent prep, xG, dashboards — as repeatable procedures.
ExploreArchitectures
Reference architectures and data flows drawn as diagrams — the signature visual explainers.
ExploreWorkflows & Craft
How great analysts actually work: framing questions, validating, storytelling and shipping decisions.
ExploreFrom fan to AI-augmented analyst.
A clear progression so you always know your next step.
- 1Beginner
Read the game with data
- Metric literacy: xG, Corsi, Fenwick
- Where hockey data comes from
- Ask the right questions
- 2Builder
Build your own stack
- Ingest a real data source
- Model shots into xG
- Serve answers from a notebook
- 3AI-augmented
Analyze like a department
- Point Claude at your data
- Turn playbooks into reusable skills
- Ship decisions, not just charts
Start with a visual explainer.
Connect your own AI assistant to your club data
How to plug Claude, ChatGPT or Copilot into your owned data — and what it unlocks.
Read explainerHow a club can own its hockey data
A reference architecture where the club owns the data and models — and any AI is just a swappable consumer.
Read explainerCorsi vs Fenwick: which possession metric should you use?
Two shot-attempt metrics, one small difference — and when each one tells the truer story.
Read explainerNewsletter
Weekly hockey analytics
Data-driven takes on performance, scouting, and team strategy. No fluff — just the numbers that matter.
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Running a club or program?
Beyond the free explainers, we help organizations build their analytics stack and AI workflows hands-on.