Fixture-Specific Betting Models for Real-Time Odds Accuracy
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Solution Overview
Problem
Current gaming systems lack the capability to generate accurate real-time betting lines for sporting events due to the inability to account for specific fixtures, leading to inaccuracies and the need for time-consuming manual adjustments.
Innovation Solution
Implementing a system that utilizes fixture-specific models and artificial intelligence/machine learning to adapt generic models in real-time, incorporating event-activity-fixture data to generate precise betting lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic models are used for betting simulations, then processing speed is maintained, but measurement precision of betting lines deteriorates due to inability to account for specific fixtures
Solution Approach 1:
The patent segments the betting simulation system into multiple components: a simulation server that executes simulations, a pricing server that determines prices, and a database that stores fixture-specific models. This segmentation allows the system to maintain processing speed while improving precision by using specialized fixture-specific models only when needed, rather than requiring all models to be complex by default.
Solution Approach 2:
The system dynamically changes the model parameters based on the specific fixture being simulated. Instead of using a single generic model with fixed parameters, the system selects and adjusts parameters from fixture-specific models stored in the database, allowing high precision for specific fixtures while maintaining overall system efficiency through parameter optimization.
2Measurement precision
If fixture-specific models are implemented, then measurement precision of betting lines improves, but productivity decreases due to increased processing time for model selection and adaptation
Solution Approach 1:
The system performs preliminary actions by pre-storing fixture-specific models in the database before the actual betting simulations occur. This allows the simulation server to quickly retrieve and apply the appropriate models without time-consuming on-the-fly creation or adaptation, maintaining high simulation speed while using precise fixture-specific data.
Solution Approach 2:
The patent uses copying by creating simplified representations of fixture-specific models that can be rapidly loaded and applied during simulations. Instead of working with complex full models throughout the entire process, the system copies essential fixture-specific parameters into a format optimized for quick simulation execution, balancing precision with speed.
3Measurement precision
If manual adjustments are made to correct simulation errors, then measurement precision improves, but loss of time increases due to intervention requirements
Solution Approach 1:
The system implements self-service by using fixture-specific models that automatically account for fixture details without requiring manual intervention. The models self-adjust to the specific characteristics of each fixture based on pre-stored data, eliminating the need for manual corrections and reducing time loss while maintaining high precision.
Solution Approach 2:
The patent incorporates feedback mechanisms where the simulation results are continuously monitored and compared against actual fixture data. This feedback loop allows the system to automatically detect and correct any discrepancies using the fixture-specific models, eliminating manual adjustment needs and reducing time loss while maintaining accuracy.
Data Source
AI summary
Devices, systems, and process for generating fixture specific models for use in adjusting pricing of real-time betting lines are described. A system may include a front-end system including an event-activity-fixture (EAF) server that adjusts betting lines based on results of simulations generated by an EAF simulation server (EAFSS). The EAFSS generates the simulations using fixture specific models that have been generated based upon adaptations of generic models, where the adaptations occurring using historic and real-time EAF data and fixture specific modeling data. The fixture specific models adapted from one or more generic models are leveled and stored in a database for use by the EAFSS on a real-time basis as the EAF occurs. A server for adapting the generic models instantiate one or more computer engines including a model adaptation engine, a generic modeling engine, an EAF data engine, and an FSM leveling engine.