Ted AI Model Architecture

Core Components

TED’s AI operates through a multi-layered architecture:

• Predictive Modeling: Uses supervised learning and reinforcement learning to analyze thousands of variables per game, including team trends, player stats, and betting market behavior.

• Execution Engine: Bets are placed automatically using vault-specific risk parameters and capital allocation strategies.

• Feedback Loop: Each bet outcome is analyzed post-execution, and model weights are adjusted for improved accuracy in future predictions.

SPORTS RADAR Integration

TED sources real-time and historical data from SPORTS RADAR, which provides:

• Player-level statistics and performance indicators

• Injury and lineup updates

• Weather and venue conditions

• Market odds across multiple sportsbooks

This institutional-grade data is processed by TED’s proprietary black-box neural network, which transforms raw data into actionable predictions.

Risk & Bankroll Optimization

TED uses dynamic risk assessment algorithms to:

• Diversify bet sizing based on volatility, correlation, and expected value

• Adjust for bankroll constraints and liquidity availability

• Ensure optimal long-term capital growth

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