Sports Predictor

Challenge

The client wanted to offer data-driven predictions alongside live odds but had no in-house ML capability. Predictions needed to be fast, explainable, and accurate enough to be useful before events.

Solution

We built a Python pipeline for feature engineering and model training on historical data, a Node API for real-time inference and odds integration, and a dashboard to monitor accuracy and drift so the team could iterate on models confidently.

Results

• 78% accuracy on held-out test sets for target leagues
• Sub-second prediction latency for live odds integration
• Dashboard and metrics in place for ongoing model improvement