In-Play Proposition Odds Adjustment Using Real-Time Audience Feedback
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Solution Overview
Problem
Live sports betting systems face challenges in setting accurate and optimal odds for In-Play propositions due to the lack of real-time feedback from the betting audience, limiting the frequency, flexibility, and creativity of betting options, and relying heavily on historical data and subjective human judgment.
Innovation Solution
A system that utilizes real-time input from a Skill Game Operator's audience response to adjust In-Play betting odds, incorporating data from a panel of experts and the betting universe's collective wisdom to optimize the odds for In-Play propositions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional historical data and subjective human judgment are used to set odds, then the system is simple to operate, but the accuracy and optimality of the odds are insufficient
Solution Approach 1:
The system implements real-time feedback loops where betting audience responses are continuously collected, analyzed, and used to dynamically adjust odds. This feedback mechanism transforms static historical data into dynamic, responsive odds setting that adapts to current betting patterns and audience sentiment, significantly improving odds accuracy.
Solution Approach 2:
The system performs preliminary analysis of betting audience responses and historical data before finalizing odds. By pre-processing and analyzing multiple data sources in advance, the system prepares optimized odds recommendations that are then quickly deployed, maintaining both accuracy and operational efficiency.
2Measurement precision
If real-time audience response data is collected and analyzed, then the odds accuracy improves, but the data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the most relevant features and signals from the overwhelming volume of real-time betting data. By filtering and extracting key patterns from audience responses rather than processing every raw data point, the system maintains high odds accuracy while reducing computational burden and data processing requirements.
Solution Approach 2:
The system transforms raw betting data into meaningful parameters and metrics that capture audience sentiment and betting patterns. By changing the representation of data from raw volumes to condensed parameters, the system achieves accurate odds setting with reduced data processing complexity.
3Productivity
If more frequent and varied betting propositions are offered, then audience engagement increases, but the financial risk to the bookmaker increases
Solution Approach 1:
The system uses real-time feedback from betting responses to dynamically adjust odds and manage exposure. By continuously monitoring betting patterns and adjusting odds in response to audience behavior, the system can offer frequent betting propositions while maintaining financial risk within acceptable parameters through adaptive risk management.
Solution Approach 2:
The system implements dynamic odds adjustment that responds to real-time betting conditions. This allows the bookmaker to offer frequent and varied propositions with odds that automatically adapt to current risk levels and betting patterns, transforming static risk exposure into dynamic risk management that can handle high betting frequencies safely.
Data Source
AI summary
A skill game operator provides real time propositions to a viewing audience, and based on the input received from those propositions, comparable In-Play wagering propositions are able to be generated, and the odds of the In-Play propositions are able to be accurately adjusted based on the actual input received from the same participating audience the skill game operator's responses to the same propositions.