All workCase study

Probabilistic forecasting product

Presaira

A full-tournament forecasting system that forecast all 104 matches of the 2026 World Cup, with reproducible runs, model evaluation and 50,000-iteration Monte Carlo simulation.

Evidence domainData science · forecasting · calibration & evaluation · Monte Carlo · reproducibility

Open public evidence
Plot / evolvePublic code · reproducible
Presaira 2026 home-win reliability evidenceCommitted calibration points compare mean predicted home-win probability with observed frequency. The diagonal is ideal calibration.20%40%60%80%26% predicted → 22% observed; n=2335% predicted → 35% observed; n=2043% predicted → 65% observed; n=2055% predicted → 77% observed; n=2264% predicted → 80% observed; n=10Mean predicted probability
104 matches forecast50,000-iteration Monte Carlopost-event evaluation
Committed 2026 calibration evidenceForecast confidence compared with observed outcomes after the completed 104-match tournament.

Evidence

What can actually be checked.

Tournament coverage

104 / 104

Every 2026 World Cup match is represented in the committed evaluation coverage; none is silently imputed away.

Simulation

50,000×

Full-tournament Monte Carlo iterations per production forecast run, using a fixed seed and versioned provenance.

Evaluation posture

Post-event

Calibration, backtests, incident notes and a tournament postmortem remain inspectable after the event finished.

The project

The interesting part was not producing probabilities. It was building a forecast that could still be inspected after every one of the 104 outcomes was known.

Tournament forecasting is easy to overstate when future results leak into evaluation, missing matches quietly disappear, or simulation choices cannot be reproduced. Presaira was structured so those failure modes stay visible rather than being polished away.

Role & scope

What this case study covers.

The public project spans the Dixon-Coles and Elo model blend, full-tournament simulation, reproducible prediction artifacts, backtesting, calibration, tests and the product surface that presents the forecast.

Approach

How the system earns the result.

01 — Model

Blend scoring dynamics with team strength.

A Dixon-Coles goal model is combined with Elo ratings so the forecast has both match-score structure and an independent strength signal.

02 — Simulate

Run the tournament, not just isolated fixtures.

Each published run simulates the full competition 50,000 times at a fixed seed, preserving group rules, knockout progression and tournament-level uncertainty.

03 — Guard

Make leakage and ordering errors fail loudly.

Pre-cutoff assertions, non-vacuous negative tests and tie-break ordering tests are part of the evidence. A failed convergence target is committed rather than hidden, with production held at the higher simulation count.

04 — Evaluate

Score the forecast after reality happens.

All 104 World Cup matches are covered and scored, with calibration artifacts, historical backtests and a postmortem that discusses the direction of the model's misses.

Limits & boundaries

What the case study does not pretend.

  • The public repository is a curated extract; the live ingestion, database and publication pipeline are deliberately not published.
  • A committed convergence check records a failed 0.5 percentage-point target between 10k and 50k simulations instead of rewriting the result as a pass.
  • The portfolio treatment avoids live-odds and sportsbook language because this is model-evaluation proof, not betting theater.

Public record

What is publishable, and where the evidence lives.

Public case-study claims are limited to the repository, committed evaluation artifacts and the live Presaira product. Private operating code and planning material remain outside the website narrative.

Open PresairaPublic repositoryMethodology

From proof to useful work

Relevant service pathways.