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 · 1,528 fixtures
Random guess
33%
Home, draw or away: 3 outcomes
Always the home team
45%
Same 1,464 matches
AI council majority
50%
The result most models picked
When all AIs agree
62%
568 matches with a unanimous council
| # | Model | Hit rate | Brier | Last 10 matches |
|---|---|---|---|---|
| 01 |
Claude Sonnet 5
783/1528 decided
|
51% | 0.61 |
|
| 02 |
Gemini 3.8 Flash
754/1527 decided
|
49% | 0.61 |
|
| 03 |
DeepSeek V4 Pro
748/1528 decided
|
49% | 0.63 |
|
| 04 |
Grok 4.6
738/1528 decided
|
48% | 0.62 |
|
| 05 |
Qwen 3.8 Max
726/1528 decided
|
48% | 0.64 |
|
| 06 |
GPT-5.6 Luna
720/1528 decided
|
47% | 0.63 |
|
| 07 |
Mistral Medium 3.5
719/1528 decided
|
47% | 0.63 |
|
| 08 |
MiMo V2.6 Pro
688/1528 decided
|
45% | 0.64 |
|
| 09 |
Kimi K3
459/965 decided
|
48% | 0.62 |
|
| 10 |
GLM 5.3
458/965 decided
|
48% | 0.63 |
|
Push alerts when predictions refresh. Confidence bands and model disagreement — without clutter.
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