# 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.&#x20;

### 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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