Today is the start of the 2026 FIFA World Cup, the largest sporting competition every four years. As a fun project, I decided to build a model to predict the tournament.
In cases like this, traditional machine learning models typically fail because the data doesn’t properly update the model in real time. So, I built a different kind of predictive engine. While the core math relies on a Monte Carlo simulation running 10,000 iterations, the real production challenge was state management: updating and reading a changing dataset every single day without manual intervention or expensive cloud compute.
I solved this by building an autonomous pipeline using GitHub Actions, flat CSV files, and Streamlit. This is exactly how the live state management and fault tolerance work.
Live State Management & Engineering Fault Tolerance
What makes this project stand out even more is its live state management and updates during the World Cup. Once the tournament begins (today), the system shifts from being just a predictive model to a tracker by handling two major risks: The Elimination Trap and Timezone Offsets.
