1. Data Collection & Aggregation

We continuously collect and aggregate comprehensive game data including team statistics, player performance metrics, historical matchups, injury reports, and betting market information from multiple sources.

2. Machine Learning Analysis

Our machine learning models process this data through multiple layers of analysis, identifying patterns, trends, and statistical relationships that may not be immediately apparent. The models are continuously trained and refined using historical game outcomes to improve accuracy.

3. Value Identification

We compare our AI-generated predictions against current betting lines to identify discrepancies where the market may be undervaluing or overvaluing a team. Our confidence pick algorithm flags games where the predicted margin significantly differs from the betting line, indicating potential value opportunities.

4. Confidence Scoring

Each prediction is assigned a confidence percentage based on the strength of the AI's analysis, historical accuracy of similar predictions, and the quality of available data. Higher confidence scores indicate stronger statistical support for the prediction.

5. Continuous Learning

Our system learns from every game outcome, updating its models and improving future predictions. This feedback loop ensures that our AI becomes more accurate over time as it processes more data and learns from its successes and mistakes.