Machine Learning Odds Generation for Custom Wager Settlement
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Solution Overview
Problem
Betting operators face challenges in creating custom bets without human intervention, calculating accurate odds, and settling wagers efficiently, especially when historical data for unique events is scarce, leading to potential losses and uncompetitiveness.
Innovation Solution
A bet facilitation system that uses machine learning models to predict mean values and generate odds for custom bets based on historical and synthetic data, adjusts odds through backpropagation, and settles wagers in real-time using live data, reducing risk through risk aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If betting operators manually create custom bets and calculate odds, then bet accuracy and reliability improve, but operational complexity and time consumption increase
Solution Approach 1:
The system enables automated self-service for bet creation and odds calculation through machine learning models. The processor automatically receives expressions, generates odds values using trained models, and settles wagers without human intervention, eliminating manual operational complexity while maintaining reliability through algorithmic consistency
Solution Approach 2:
Manual mechanical processes of bet creation and odds calculation are replaced with automated electronic systems. Machine learning models process expressions and historical data computationally to generate odds values, substituting human-operated mechanical systems with automated electronic processing that reduces complexity and increases speed
2Measurement precision
If historical data is used to train machine learning models, then odds prediction accuracy improves, but data availability worsens for unique events
Solution Approach 1:
The system performs preliminary training of machine learning models using available historical data before actual betting events occur. By pre-training models with existing data and then applying them to new events, the system maximizes the utility of limited historical data while maintaining prediction accuracy for unique events where data is scarce
Solution Approach 2:
The machine learning model serves multiple functions: it processes various types of expressions (proposition bets, parlay bets, same-game parlay bets), handles different data availability scenarios, and generates odds values across diverse betting contexts. This universal application allows the single model to effectively utilize limited historical data across multiple event types
3Productivity
If real-time data processing is implemented for wager settlement, then settlement speed improves, but system complexity increases
Solution Approach 1:
The system implements continuous real-time processing of live data during events for automated wager settlement. The processor continuously receives live data feeds, evaluates expressions against current event state, and settles wagers immediately when conditions are met, maintaining continuous useful action without interruption while managing complexity through streamlined automated logic
Data Source
AI summary
A method includes receiving a first proposition bet represented as a first expression and associated with a sporting event that has yet to commence or that is in progress. Using a statistical model and based on historical data associated with the sporting event, a first mean value is generated. Wager data associated with the first proposition bet is received, and using a backpropagation technique, wager data is aggregated on the parameters of the statistical model. If at least one risk threshold is met, at least one parameter included in the statistical model is modified to result in a modified statistical model. The method also includes receiving a second proposition bet different from the first proposition bet, represented as a second expression, and associated with the sporting event that has yet to commence or that is in progress. Using the modified statistical model, a second mean value is generated.