Ten minds.
One consensus.
Zero guessing.
We run every football match through multiple frontier AI models, then publish the consensus, the dissent, and the confidence behind it.
Every model gets the same match and context. We log their answers, score them after the final buzzer, and publish the leaderboard.
Form, schedule, expected goals, and match context are loaded into a structured prompt for every model.
Each active model forecasts scoreline, outcome, and confidence independently before kickoff.
We surface the majority pick, confidence bands, and where models disagree.
After full-time, picks are marked against the result and the public leaderboard updates.
To date · 1,916 fixtures
Random guess
33%
Home, draw or away: 3 outcomes
Always the home team
44%
Same 1,870 matches
AI council majority
51%
The result most models picked
When all AIs agree
62%
800 matches with a unanimous council
| # | Model | Hit rate | Brier | Last 10 matches |
|---|---|---|---|---|
| 01 |
Claude Sonnet 5.5 1006/1916 decided
|
53% | 0.60 |
|
| 02 |
Gemini 3.8 Flash 967/1915 decided
|
51% | 0.60 |
|
| 03 |
DeepSeek V4 Pro 956/1916 decided
|
50% | 0.63 |
|
| 04 |
Grok 4.7 955/1916 decided
|
50% | 0.62 |
|
| 05 |
GPT-6 Luna 936/1916 decided
|
49% | 0.62 |
|
| 06 |
Qwen 3.8 Max 933/1916 decided
|
49% | 0.63 |
|
| 07 |
MiMo V2.6 Pro 894/1916 decided
|
47% | 0.63 |
|
| 08 |
Kimi K3 674/1353 decided
|
50% | 0.61 |
|
| 09 |
GLM 5.3 674/1353 decided
|
50% | 0.61 |
|
| 10 |
Meta Muse Spark 1.3New 43/84 decided
|
51% | 0.59 |
|
Push alerts when predictions refresh. Confidence bands and model disagreement — without clutter.
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