Machine Learning Sports Betting Predictor

I need a complete sports-betting prediction system powered by machine-learning techniques. The model must ingest and crunch every layer of performance data—individual player metrics, overall team statistics, and full historical game results—then output win-probability or spread forecasts that can be called programmatically.

Here’s how I picture the workflow:

• Data pipeline: automated collection and cleaning of player, team, and historical results from reliable public or paid feeds.
• Feature engineering: transform raw stats into meaningful inputs (form trends, injury flags, home/away effects, etc.).
• Model training & testing: build, tune, and validate one or more ML algorithms (e.g., gradient-boosted trees, neural networks). Solid cross-validation and back-testing are essential.
• Prediction service: expose forecasts through a lightweight REST or GraphQL API so I can plug them straight into my staking tool.
• Documentation: explain data sources, model logic, and usage instructions so I can retrain or extend the system later.

Acceptance criteria
– Forecast accuracy beats baseline bookmaker odds or a naïve historical average on a held-out test set.
– Reproducible environment (Dockerfile or requirements.txt).
– Clean, well-commented code in Python (preferred) or an equivalent ML-friendly language.

If you have existing frameworks, feature libraries, or proprietary approaches that accelerate delivery, feel free to integrate them as long as the final solution remains transparent and maintainable.