Sports Outcome Forecasting

Train specialized models to predict sports outcomes from historical data

Use historical match data, player stats, and event records to train models that outperform generic LLMs on sports predictions. The same pipeline works across any sport with sufficient historical data.

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32%
better Brier Skill Score than GPT-5 on professional golf outcomes
↗ Model card
41%
better probability calibration than GPT-5
↗ Hacker News

Example prediction questions

The kinds of questions a model trained on your data can answer.

Key results

Benchmark comparisons against frontier models

ProphetArena Sports Leaderboard

Foresight V1 32B ranks #1 on ProphetArena Sports ahead of Grok 4, Gemini 2.5 Pro, and multiple GPT-5 variants. On 855 held-out golf questions, it achieved Brier Skill Score +17.0% vs. +12.8% for GPT-5, with 41% lower calibration error (ECE 0.062 vs. 0.106).

ProphetArena sports leaderboard: Foresight V1 32B at #1, Grok 4 at #2, Gemini 2.5 Pro at #3
↗ Model card

Explore

Primary write-ups and artifacts for this solution.

Case study We built a golf forecasting model that beats GPT-5 — r/LocalLLaMA → Dataset GolfForecasting → Model Golf-Forecaster → Notebook Golf Forecasting Fine-Tuning Notebook →

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