LINHAC 2026 — Conference Report

Conference Report · June 2–4, 2026

5th International Ice Hockey Analytics Conference · Linköping · 36 sessions

LINHAC 2026 made one thing clear: hockey analytics is moving from specialist craft to organizational infrastructure. Across 36 sessions, the same pattern appeared repeatedly — clubs that treat data as a side project will lose speed, while clubs that build shared data foundations can improve decisions across coaching, development, and commercial operations.

This report is a structured readout of that shift. It combines key research signals, practical examples from clubs and federations, and my own enterprise lens from large-scale data and AI transformation. The goal is simple: capture what matters now, what is changing next, and where hockey organizations can act immediately.

6 main areas I want to highlight from the conference

01 — Data is no longer an edge — it is infrastructure One of the clearest parallels to business. Analytics used to be a competitive edge. Today it is basic infrastructure. I see the same shift in enterprise — and hockey is heading the same way. Clubs still treating data as a side project risk falling behind.

02 — Own the data, not the dashboard Many companies now invest in their own data platforms instead of depending on vendors. Hockey is on the same path. The long-term edge is owning data, models, and processes — not buying another tool. “Own your data. Control your AI.”

03 — Insights are cheap — behavior change is hard AI makes reports and analysis easier than ever. That is already everyday in business. The real challenge is getting people to change behavior and make better decisions. Hockey is facing the exact same journey.

04 — Build, test, learn There is no finished product that solves everything. The most successful organizations work iteratively — test ideas, learn fast, adjust along the way. That is how business already runs. Hockey is catching up.

05 — From data to stories Hockey already has enormous amounts of data. What matters next is not more data points — it is how they are used to create relevant stories for players, fans, and partners, at scale. Business learned this years ago with personalization. Hockey is next.

06 — Build the organization, not just the team Maybe the biggest reflection from the conference. Hockey still often recruits from the same circles, while other sports and companies look for talent more broadly. As AI takes over more of the analysis work, the question gets sharper: who does the organization actually need going forward? When AI makes analysis available to everyone, the question is not who played hockey — but who can solve the problems.

Key numbers from research

OR 5.05 — Pull the goalie earlier Down one with more than 2:30 left (vs 2:00 or less): 5x better odds to score, and 0.5–3.2 extra standings points per season.

+4.3% — Home ice matters most on the power play +4.27% Corsi on the PP vs +2.02% at even strength, and about 10% better odds to score. The PP does not help road teams catch up.

r = .437 — EPV passing tracks with coach ratings Best match with how coaches rate passing smarts (n=120). Raw shot volume barely matters (r=0.08).

Δ +0.001 — Keep career models simple Age, size, games played, draft spot, and region explain almost everything. Fancy social inputs add almost nothing.

From data to behavior on the ice

MODO U20 (Felix Dahlin): data sets the bar; players choose the behaviors to hit it. Five win factors run the season — slot shots, blocked shots %, shift length, penalties — talked through in small groups, not handed down by coaches.

Färjestad BK (Erik Wilderoth): weekly reports beat game-by-game noise. SIF (Veronica Eriksson): own the full pipeline — video → events → metrics — in-house. Spiideo: most clubs track games but not practice. The same chain — capture → data → insight → decision — needs to cover both.

Beyond the ice and the analytics room

Data Talks (Stefan Lavén): stop running one-off campaigns. Build a fan OS loop — collect, unify, segment, activate, measure. One fan profile across tickets, merch, digital, and sponsors, with churn and lifetime value driving what you send next.

49ing (Andreas Hänni): hockey already has the data. The question is whether it becomes league highlights or personal stories. AI on match data means “one model, millions of stories.” With Claude, approved users can build their own views.

Ray Lalonde: analytics is moving from edge to core infrastructure. The Tampa Bay Rays compete without a top payroll because data is how the org runs — not a side project. Timo Seppa: garbage in, garbage out. Fix your data before you add more dashboards.

What hockey can learn from AI

David Radke (Chicago Blackhawks): use game sims (FC 24, RoboCup) to test tactics. Game Plan (DeepMind × Liverpool FC) points to three building blocks — stats, game theory, and computer vision — as a hockey AI roadmap. His ranking paper with Troy Mulholland shows that Elo, social choice, and basic stats can rank the same teams differently. How you rank is a choice, not just math.

Personal reflection: Data for the whole organization

My read on LINHAC — shaped by the sessions and by what I see daily in a large enterprise organization. Hockey is not there yet, but it is on the same change journey business is running through now, only faster. One argument in four steps.

1 · The shift — less dashboards, more leadership

LLMs have made analysis a commodity. Anyone can prompt their way to a dashboard. In business, this is already everyday reality — dashboards everywhere, insight everywhere, change nowhere. The bottleneck is no longer getting dashboards out; it is finding people who can drive data-driven change. Hockey is a few steps behind, but heading for the same wall. Next year’s conference will have more graphs, data, and AI than anyone can absorb. Dashboards are not the bottleneck anymore.

2 · The response — data engineering as foundation

The bottleneck is the data platform — how it is built and continuously improved. That shapes not just game and opponent analysis, but how efficiently the organization runs on the business side too. Enterprise is investing heavily in this now across the whole operation: marketing, sales, finance. One shared platform, structured data, engineers who build and maintain it — the base for efficient AI flows and lasting efficiency, with data that stays when people move on. Hockey is still on the old model: each analyst arrives with their own tools and takes them to the next club. Clubs need to build that capability in the organization.

3 · From generalist to specialist

Once the shared platform is in place — with today’s tools, that is 3–6 months of work — the analyst role changes. The generalist who collected data, built dashboards, and ran every report does not scale anymore. AI handles the basic analytics. What the org needs instead are deep specialists: pass data, shot data, movement patterns — people extreme enough in one domain to actually move the needle. That will reshape who works in hockey analytics and open the door to subject matter experts who may not come from hockey at all.

4 · Small clubs can punch up

In business right now, small AI-native challengers are taking on giants that were never built to automate from scratch. Hockey has the same window — but only if a club invests in data foundation, runs the organization more efficiently, and finds new revenue streams — which is what data makes possible. Smaller clubs can challenge the big ones the way startups are challenging incumbents across every industry. That takes thinking differently and hiring educated people from different backgrounds — not the buddy or former teammate.

Appendix A — References

1. Kumagai, B. (Teamworks). New Frontiers in Hockey Analytics with Player Tracking Data. LINHAC 2026, session 1, 2 June 2026, Linköping.
2. Eriksson, V. (Swedish Ice Hockey Federation). Coach’s challenge: Own your data. Control your AI. LINHAC 2026, session 17, 3 June 2026, Linköping.
3. Lalonde, R. (Ray Lalonde Sports+). From Competitive Edge to Core Infrastructure: The Global Rise of Sports Analytics. LINHAC 2026, session 24, 3 June 2026, Linköping.
4. Seppa, T. (Hockey Data Show / NHL Network Radio). Garbage in, garbage out: What’s in your analytics? LINHAC 2026, session 23, 3 June 2026, Linköping.
5. Dahlin, F. (SIF & MODO Hockey). Behavior-driven development — Fueled by analytics. LINHAC 2026, session 19, 3 June 2026, Linköping.
6. Wilderoth, E. (Färjestad BK). Raise the floor — Learnings from Data-Driven Player Development. LINHAC 2026, session 18, 3 June 2026, Linköping.
7. Palvalin, M. Validating EPV Against Expert Coach Assessments of Passing Intelligence in Elite Youth Ice Hockey. LINHAC 2026, session 3, 2 June 2026. n=120. PDF
8. Davis, J., Bransen, L., Devos, L. et al. (2024). Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned. Machine Learning, 113, 6977–7010. doi:10.1007/s10994-024-06585-0
9. Radke, D. & Mulholland, T. Non-Transitivity in the NHL: Ranking Teams, Lines, and Players using Game Theory. LINHAC 2026, session 2, 2 June 2026. Paper
10. Goldstein, E. J. & Pearson, J. A. (ORRO AI GENIUS). Multi-Horizon Career Longevity Prediction for NHL Skaters. LINHAC 2026, session 6, 2 June 2026. Dataset: 5,754 skaters, 1979–2024. PDF
11. Ali, S. A. & Mohamed, H. (University of Waterloo). Closing the Gap: A Longitudinal, Covariate-Adjusted Analysis of NHL Goalie Pull Timing. LINHAC 2026, session 5, 2 June 2026. Dataset: 7,047 pulls across 10,250 games, 2015–16 to 2024–25. PDF
12. Riccardi, N., Campbell Jr., T. & Paul, R. J. (Syracuse University). NHL Home-Ice Offensive Advantage and the Power Play. LINHAC 2026, session 7, 2 June 2026. Dataset: 4 seasons, 2021–22 to 2024–25. PDF
13. Zou, Y. & Schuckers, M. A Bayesian Approach to Estimating an NHL Draft Value Pick Chart with Bounds. LINHAC 2026, session 4, 2 June 2026. Dataset: NHL drafts 2009–2018, tracked for 7 seasons each. PDF
14. Lavén, S. (Data Talks). From followers to revenue — what hockey orgs get wrong about fans. LINHAC 2026, session 22, 3 June 2026, Linköping.
15. Hänni, A. (49ing). Data use beyond analytics. LINHAC 2026, session 20, 3 June 2026, Linköping.
16. Radke, D. (Chicago Blackhawks). What Can Hockey Analytics Learn from AI? LINHAC 2026, session 25, 3 June 2026, Linköping.
17. Tuyls, K. et al. (DeepMind & Liverpool FC). (2021). Game Plan: What AI can do for Football, and What Football can do for AI. Journal of Artificial Intelligence Research, 71, 41–88. doi:10.1613/JAIR.1.12505
18. Paul, R. J., Riccardi, N., Pauline, G. & Weinbach, A. Climate and Income Effects on U.S. Hockey Participation. LINHAC 2026, session 8, 2 June 2026. Panel data 2009–2023, state level. PDF
19. Cook, D. & Zeba, M. (Spiideo). From Capture to Decision: Building the Modern Sports Intelligence Stack. LINHAC 2026, session 21, 3 June 2026, Linköping.
20. Boulet, L. (LB-Hockey). Defining Physicality and Skaters’ Ability to Play Through It. LINHAC 2026, session 12, 2 June 2026. PDF

Appendix B — Session index

All 36 sessions. Bold rows have detailed personal notes.