World Cup 2026 — AI Match Predictions
Every group-stage fixture, analysed by an Elo + Monte Carlo model. Open a match for team form, head-to-head context, AI prediction explanation, simulation summary, score forecast and model confidence.
Today’s World Cup AI Predictions
This hub highlights the latest available match predictions and full-time model reviews. Each match page includes unique analysis, team form, head-to-head stats, AI prediction explanation, simulation summary and internal links.
Latest AI Model Reviews
Finished matches with the pre-match forecast next to the final score. See the full model accuracy & trust page →
- 🇧🇪 Belgium 1–1 🇪🇬 EgyptAI 1-0 · Final 1-1 · Missed ✕
- 🇮🇷 Iran 2–2 🇳🇿 New ZealandAI 1-0 · Final 2-2 · Missed ✕
- 🇪🇸 Spain 0–0 🇨🇻 Cape VerdeAI 1-0 · Final 0-0 · Missed ✕
- 🇸🇦 Saudi Arabia 1–1 🇺🇾 UruguayAI 0-1 · Final 1-1 · Missed ✕
- 🇦🇺 Australia 2–0 🇹🇷 TurkeyAI 0-1 · Final 2-0 · Missed ✕
- 🇩🇪 Germany 7–1 🇨🇼 CuracaoAI 1-0 · Final 7-1 · Direction correct ✓
- 🇨🇮 Ivory Coast 1–0 🇪🇨 EcuadorAI 0-1 · Final 1-0 · Missed ✕
- 🇳🇱 Netherlands 2–2 🇯🇵 JapanAI 1-0 · Final 2-2 · Missed ✕
Group A
Group B
Group C
Group D
Group E
Group F
Group G
Group H
Group I
Group J
Group K
Group L
How the prediction model works
The World Cup 2026 model combines baseline team strength, recent match form, squad availability, coaching context, venue information, and a simulation layer. Team strength begins with an Elo-style rating so established performance is not ignored. Recent form is then used as a supporting signal, not as a single-match overreaction, because tournament football can swing on rotation, travel, or tactical choices.
For each fixture, the engine estimates attacking and defensive balance, converts those values into expected-goal ranges, and runs repeated simulated match states. Every match page shows the inputs and trend direction openly, alongside the score forecast, full probability table, model confidence, and trust score. This keeps the page useful for research while avoiding overstated certainty.
After completed matches, results can be compared with the original model output. That evaluation loop is used to measure direction accuracy, score error, confidence calibration, and data quality. The goal is not to claim perfect foresight, but to make the reasoning visible enough that readers can understand why the model leans one way or flags a volatile matchup.
Privacy and data use
The site uses public football references, generated model estimates, and live-data responses when available. It may store lightweight state in your browser with localStorage (such as your language choice). We do not require a payment or account for this launch.
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