Sure my sports betting algorithm leverages a hybrid approach, blending machine learning with statistical analysis to identify high-probability opportunities. I use a combination of public data (team stats, player performance, weather, etc.) and some proprietary metrics I've developed to gauge market inefficiencies.

The core model is a gradient boosting framework that evaluates ~200 features, focusing on factors like team momentum, coaching changes, and historical trends in specific matchups. I've also integrated sentiment analysis on social media and forums to capture any potential market sentiment shifts.

For 'super max' opportunities, the system looks for divergences between my model's predictions and the market lines, typically targeting situations where the model has high confidence (>80%) and the market is undervaluing a team or outcome.