OracleBTTS

Quantitative pricing engine for football markets.

Role: Founder & developer
Timeline: Ongoing
Stack: Stack TBD, Stack TBD, Stack TBD
Live site: oraclebtts.com

The problem

Pricing football markets well requires a modelling pipeline, clean historical data and a fast feedback loop between model and market. Most people attempting it are stitching together notebooks that nobody else can run.

Built for Quantitative bettors and analysts working with market data

The approach

I treated the model and the product as one system: the same pipeline that backtests is the one that prices live. Everything the engine outputs is traceable back to the inputs that produced it.

What I built

Automated data ingestion and cleaning across fixtures and market feeds
Probability model with versioned parameters and reproducible runs
Live pricing surface with divergence highlighting against the market
Backtesting harness with performance reporting per model version

Screenshots

Home: the positioning — priced probabilities rather than tips, with ROI and hit rate up front

Signal card: model probability, minimum value odds, expected value and the rationale behind it

FAQ and closing statement: a quantitative model, not a tipster

Outcome

A pricing engine that runs unattended and produces auditable numbers, rather than a research project that only works on one machine.

Next project

Nutrilayer →

Personalised plant-based nutrition, built for practitioners.

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