Emil Ingemar Karlsson · The Hockey Analytics

Hockey analytics

Data craft and the game — not one without the other.

I build hockey intelligence systems: NHL and SHL analysis, sports data pipelines, and the publishing platform at thehockeyanalytics.com. The edge is pairing platform engineering with hockey-specific judgment — so insights stay reproducible, current, and worth acting on.

Visit The Hockey Analytics Stories in the data Contact

The edge

Most sports analytics teams are strong on one side.
I work both.

Pure data engineers often miss tactical context. Pure hockey people often lack durable pipelines. I sit in the overlap: data platforms that survive production and analysis grounded in how hockey actually works — from NHL tracking questions to Swedish league and player paths.

Data craft
Pipelines you can rerun. Models you can audit. Dashboards tied to sources — not screenshots of someone else's API.
Hockey context
League structures, roster rules, special teams, and Swedish player paths — the domain knowledge that keeps analysis honest.
Publishing loop
From raw feeds to readable reports on thehockeyanalytics.com — automated where it should be, reviewed where it matters.

Live platform

The Hockey Analytics

A dedicated hockey analytics and sports intelligence site — sibling brand to The Unnamed Roads. Built with Astro, wired into automated content and reporting pipelines documented in my case studies.

What lives there today

NHL-focused analysis and SEO-ready publishing
Sports intelligence presentation — not generic BI exports
Automated reporting hooks (Slack, webhooks) from the platform project
Part of a multi-site autonomous content pipeline — see project write-up

thehockeyanalytics.com ↗

See the pattern

Stories in the data

Advanced hockey analysis, told the Gapminder way: start with the question, show how to read the chart, then the one insight that flips intuition. Full methods live in each linked piece.

The question

What actually separates Cup teams from the rest — stars, or a pattern?

Insight. Across 30 Stanley Cup seasons, a 12-metric “Championship DNA” index lined up with winners about 89% of the time in backtesting — far above media consensus picks.

The Stanley Cup pattern

The question

If referees are fair, what does a chart of penalty calls look like late in a one-goal game?

Insight. In a study of 50,000+ NHL penalty calls, visiting teams drew about 23% more penalties in the final five minutes when the home team trailed by one.

Referee bias under pressure

The question

Can you see a scoring “map” the way Rosling showed countries — body, stick, and shot type together?

Insight. Computer-vision work on 25,000+ goals found four scoring archetypes; taller players using a stick near 80% of height scored substantially more snap-shot goals in the modeled sample.

The scoring map (equipment × body)

The question

Is hockey analytics still a clever side project — or the plumbing of the club?

Insight. LINHAC 2026’s through-line: analytics is becoming core infrastructure. Clubs that own data, models, and process outpace those stuck buying another dashboard.

LINHAC 2026 — from edge to infrastructure

The question

Do SHL clubs treat the timeout the same way — or do benches keep different habits?

Insight. Across 1,303 timeouts in 1,854 SHL games (2020/21–2024/25), Färjestad used it in 47.3% of games while Växjö used it in 22.9%. HV71 led on rate at 50.2%. Same league, same one-timeout rule — very different club ledgers.

SHL timeouts by club

The question

Is it the coach’s name that moves the table — or the returning core?

Insight. In 52 SHL team-seasons (2021/22–2024/25), returning-core share correlated with points% (r = 0.32); mean age did not (r = −0.05). Coaching changes barely moved Δ pts%. The club’s level mostly stays.

SHL cores beat coaches

From this site

FAQ

Common questions

What makes your hockey analytics different from a generic data consultant?

Most sports analytics work stops at charts or one-off models. I combine data platform engineering — pipelines, warehouses, automation, and reproducible methodology — with hockey-specific context across NHL and SHL. The goal is analysis you can trust, refresh, and act on, not a slide deck that ages in a week.

Which leagues and data sources do you work with?

Primary focus is NHL and Swedish hockey (SHL, HockeyAllsvenskan, and Swedish NHL players). Work draws on official league APIs, public tracking data, computer-vision workflows where relevant, and modern data stacks (Python, SQL, MotherDuck, Mage, and Astro-powered publishing). Live platform work lives at thehockeyanalytics.com.

Is this the same as The Hockey Analytics brand?

Yes. The Hockey Analytics (thehockeyanalytics.com) is the public platform and content brand. This page is my personal positioning as the engineer and analyst behind that work — sibling to The Unnamed Roads venture studio and this portfolio site.

Do you build custom pipelines or only publish analysis?

Both. I design sports data pipelines, automated reporting, and SEO-ready publishing systems — see the hockey analytics project and case studies on this site. Engagements can start with a specific analysis question or with infrastructure to make recurring hockey intelligence reliable.

How do I get in touch about hockey analytics work?

Use the contact form on this site. Share the league, question, and whether you need a one-off study or ongoing data infrastructure — I reply by email.