Ted AI Model Architecture

Core Components

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

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

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

  • Post Processing: Each bet outcome is analyzed post-execution, and betting engine parameters are adjusted for improved accuracy in future predictions.

Sportradar Integration

TED sources real-time and historical data from Sportradar, 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 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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