Parallel Event and Market Simulation for Prediction Accuracy
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Solution Overview
Problem
Cumulative series event prediction in simulations is computationally intensive and lacks efficient methods to calculate the likelihood of single downstream events, especially in sports and financial markets, where existing tools fail to provide context on expected performance compared to odds.
Innovation Solution
A system that receives sports data, generates simulated events data using a predictive event outcomes model, executes a simulated market participant data model to create a simulated markets model, and performs parallel single event analysis and betting market simulations to provide predicted outcomes and betting markets, allowing for informed predictions and arbitrage opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple simulations are executed to calculate the likelihood of downstream events, then prediction accuracy is improved, but computational intensity increases
Solution Approach 1:
The simulation process is divided into separate stages: first executing simulations to generate event outcomes, then separately executing market participant models to simulate betting markets. This segmentation allows each component to be optimized independently and reduces overall computational burden while maintaining prediction accuracy.
Solution Approach 2:
The system performs preliminary simulations of sporting events to generate event outcome data before executing the market participant model. This preliminary action provides the necessary input data for subsequent market simulation, improving efficiency by preparing data in advance rather than processing everything simultaneously.
2Device complexity
If sequential execution of single event analysis and market simulation is used, then model complexity is reduced, but processing time increases
Solution Approach 1:
The patent combines the single event analysis and market simulation processes into a unified integrated model that executes both functions simultaneously. This merging allows the system to process event outcomes and market reactions in parallel, reducing total processing time while maintaining manageable complexity through structured data flow.
3Ease of operation
If existing prediction tools are used, then ease of operation is maintained, but lack of context on expected performance versus odds
Solution Approach 1:
The system incorporates feedback loops that compare simulated event outcomes with actual betting market data and odds. This feedback mechanism provides users with contextual information about expected performance versus actual odds, enhancing decision-making while maintaining ease of use through automated processing.
Data Source
AI summary
Systems, methods, and computer-readable storage media for cumulative series event prediction, and more specifically to calculating the likelihood of a single downstream event by using multiple simulations. A system can receive sports data and generate, using the sports data, simulated events data. The system can also execute a simulated market participant data model, resulting in a simulated markets model. The system can then execute, in parallel, the simulated markets model and an event simulation/analysis, and output both the event prediction and predicted betting markets to a user.