How Luzmo is Powering AI Football Simulations for World Cup

AI is fuelling almost every facet of modern sports analytics, transforming how teams and fans interact with data.

In line with this technological shift, the team behind the Luzmo AI Octopus Euro 2024 predictor has released an updated tournament simulator for the 2026 FIFA World Cup.

Football fans can now input freeform, natural-language scenarios to test how unpredictable match variables, such as sudden red cards or extreme heat waves, might alter the entire tournament outcome.

The interactive simulation engines are moving away from traditional dashboards dominated by sliders and settings, letting users type out tournament-altering variables. 

This development follows a successful rollout during the European championships, where data insights provided automated tournament predictions.

Simulating match variables 

Luzmo, which is behind the tournament simulator, specialises in embedded analytics and data visualisation software. 

The updated simulator is designed to ingest a wide variety of tournament-altering prompts.

Haroen Vermylen, CTO at Luzmo, explains that the system can process realistic tactical shifts as well as more unusual concepts.

He tells The Register: “Sensible questions work – a red card, a key injury, a heat wave, a squad switching base camp – but so do the daft ones like ‘What if the tournament were played with rugby rules?’”

To turn these user inputs into realistic outcomes, the backend platform combines squad-quality data based on player information, injury reports and environmental factors unique to the 2026 host nations, such as heat and altitude.

The simulator uses a Monte Carlo approach over thousands of simulated matches to generate win, lose or draw probabilities. Scorelines are derived from 5,000 match runs for every processed scenario.

An early baseline simulation shows Spain has an 18% chance to win the trophy and a 26.8% chance of reaching the final.

Slashing the simulation time

The original engine behind the Euro 2024 predictor was written in TypeScript, which could not support real-time scenario calculation.

To improve responsiveness for the new conversational interface, the team rewrote the entire simulation calculation engine in Rust.

Haroen notes that the language migration was essential to handle the immediate computing demands of public users.

He says: “We moved to Rust to also be able to run things more quickly, as now there is a real-time component to this. Before it could run for five minutes or so. Now we want the predictions to actually come out within two to three seconds of actual simulation time.”

OpenAI models to orchestrate data pipeline

The system relies on external large language models to bridge the gap between human language and raw data calculations.

OpenAI models parse user requests and an AI agent handles the end-to-end orchestration. The agent transforms the natural-language prompt into structured parameters, calls the Rust calculation engine and generates readable text summaries of the results.

Built-in filtering blocks are active within the pipeline to prevent misuse. Haroen explains that content safety controls are in place to ignore profanities and to avoid scenarios that would just be harmful to certain groups.

While using chat features makes it easier for regular people to ask ‘what if’ questions without needing to be tech experts, it is a tricky balancing act for the developers building these systems.

They have to make sure the math stays accurate, block inappropriate language and keep the system fast enough to reply in just a few seconds.

While natural language offers an accessible alternative to complex menus, clarity remains vital because an AI parser can still misunderstand ambiguous prompts.