TES's AI Model Architecture
Core Components
Predictive Modeling
TED’s AI uses supervised machine learning to model sports markets at depth.
The system analyzes thousands of variables per game, including:
team and matchup dynamics
player-level performance signals
historical outcomes
betting market behavior
These models generate probabilistic forecasts used directly for trading decisions.
Execution Engine
TED executes trades automatically through its betting engine.
Positions are placed programmatically
Capital allocation follows vault-specific risk parameters
No manual intervention or discretionary input
Execution is governed by model confidence, volatility, and bankroll constraints.
Post-Execution Feedback
Every executed trade feeds back into the system.
Outcomes are evaluated post-settlement
Model parameters and execution thresholds are adjusted
Performance improves through continuous statistical learning
This creates a closed learning loop between prediction and execution.
Real-Time Sports Data Integration
TED ingests both real-time and historical data from institutional-grade sports data providers, including:
player statistics and performance indicators
injury and lineup updates
weather and venue conditions
live market odds across multiple sportsbooks
This data is processed by TED’s proprietary neural network to convert raw inputs into actionable pricing signals.
Risk & Bankroll Optimization
TED applies dynamic risk management at the system level.
The engine:
Sizes positions based on volatility, correlation, and expected value
Adjusts exposure based on liquidity and bankroll availability
Prioritizes long-term capital growth over short-term variance
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