About estimador.pt
Independent data analysis for Portugal — sports, elections, and more
Our Mission
estimador.pt is a data analysis platform that provides transparent, probabilistic forecasts for Portugal. We believe the public benefits when it has access to rigorous, unbiased analysis — not just punditry and speculation.
Our goal is to bring the standards of modern statistical modelling to the Portuguese context, combining Bayesian methods with real data to answer relevant questions.
What We Do
Liga Portugal A Bayesian model that simulates thousands of possible seasons to forecast standings, championship and relegation probabilities, and the impact of each matchday on the league's destiny.
Presidential Elections 2026 Probabilistic forecasts for the presidential race, combining polling data with historical patterns and electoral uncertainty.
Parliamentary Elections 2025 Archive of the forecast model for the parliamentary elections, with district-level seat projections using the D'Hondt method.
Polling Analysis Systematic estimation of pollster house effects — the consistent biases each polling firm shows toward certain parties or candidates.
Our Approach
We use Bayesian statistical methods that explicitly quantify uncertainty. Our forecasts produce probability distributions that reflect what we actually know — and don't know — about outcomes.
Key principles:
- Transparency: Open methodology with detailed documentation of our models and assumptions
- Independence: No external funding or editorial pressure — the models are transparent and the data speaks for itself
- Accuracy: Continuous validation against actual results, with honest assessment of errors
- Accessibility: Presenting probabilistic forecasts in ways that general audiences can understand
About the Project
estimador.pt was created by Bernardo Caldas as an independent project combining Bayesian statistics, computational methods, and analysis of Portuguese data.
For questions, suggestions, or media inquiries, please contact us at info@estimador.pt.
Limitations & Disclaimers
Probabilistic forecasting is inherently uncertain — that's why we present probabilities, not certainties. Our models account for several sources of uncertainty, but there are always unpredictable factors.
A forecast showing a 70% probability also means a 30% chance of the opposite. Our forecasts should be interpreted as informed probabilistic assessments, not facts.