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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