Dynamic UI Updates via Simulation Sampling
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Solution Overview
Problem
Existing systems face computational impracticality in calculating probability values for parlay wagers due to the increasing complexity and number of potential outcomes during live events, especially when serving multiple client devices concurrently, leading to resource exhaustion and impractical real-time updates.
Innovation Solution
The system employs simulations to estimate probability values by generating samples from a joint-probability distribution, caching selections, and dynamically updating user interfaces with margin values based on selection frequencies and thresholds, reducing computational load and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute-force approaches are used to calculate probability values for parlay wagers, then measurement precision is improved, but productivity deteriorates due to computational impracticality
Solution Approach 1:
The system pre-calculates and stores samples from the joint-probability distribution of multiple conditional events before live events occur. These pre-computed samples are cached and can be quickly retrieved and processed during live events, eliminating the need for brute-force calculations at runtime while maintaining accurate probability estimates
Solution Approach 2:
Instead of performing exhaustive brute-force calculations, the system creates a representative sample set (copy) of the joint-probability distribution through simulations. This sample set approximates the full probability space and can be efficiently queried to determine probability values for parlay wagers without examining all possible outcomes
2Reliability
If real-time updates are provided for multiple client devices, then service quality is improved, but loss of energy increases due to resource constraints
Solution Approach 1:
Probability calculations and sample generation are performed in advance before live events begin. During live events, the system only needs to retrieve pre-computed samples and filter based on actual event outcomes, dramatically reducing real-time computational energy consumption while maintaining the ability to serve multiple client devices concurrently
Solution Approach 2:
The system generates a large number of simulation samples in advance (excessive action) to ensure that sufficient data is available for accurate probability calculations. This upfront computational effort enables efficient real-time serving of multiple clients without exhausting resources during live events
Data Source
AI summary
Systems and methods for dynamically updating user interfaces are disclosed. A system can identify a set of samples of a joint-probability distribution of a plurality of conditional events to occur during a live event, and provide a user interface for presentation at a computing device with user interface elements each indicating a respective conditional event. The system can receive a first selection of a first conditional event from the computing device, and dynamically generate, based on the set of samples and responsive to the first selection, a respective probability value for a combined selection of conditional events including the first conditional event and each other conditional event. The system can provide instructions to update the user interface to include a respective indicator corresponding to the respective probability value for each combined selection.