Rewarding Non-Dangerous Betting Behavior With Dynamic Odds
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Solution Overview
Problem
Existing wagering networks face challenges in maximizing profitability, as high frequency low wager amount bettors are less profitable, while high frequency high wager amount bettors are harder to engage in increasing their wager frequency, despite being a significant source of profits.
Innovation Solution
Implementing a system that classifies users as high frequency or high wager amount bettors and provides personalized incentives through a wagering network connected to a mobile device, offering improved odds or frequency adjustments to encourage increased wagering activity during live sporting events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the wagering network encourages high frequency low wager amount bettors to increase their wager amount, then the network's profitability increases, but the bettors' risk exposure increases
Solution Approach 1:
The system dynamically adjusts the odds parameters for specific plays based on the bettor's historical behavior patterns and current game state, offering favorable odds that incentivize larger wagers while maintaining the network's profit margin through calculated risk assessment
Solution Approach 2:
The system continuously monitors bettor behavior, wager outcomes, and game state to provide real-time feedback through personalized odds adjustments and notifications, creating a closed-loop system that adapts to both bettor preferences and network profitability requirements
2Productivity
If the wagering network provides personalized incentives to high frequency bettors, then user engagement increases, but the system complexity increases
Solution Approach 1:
The system segments bettors into different categories (high frequency, high wager amount, etc.) and applies different incentive strategies to each segment, simplifying the overall system by handling different user groups through standardized protocols rather than fully personalized complex algorithms
Solution Approach 2:
The system applies personalized incentives only to specific plays rather than all wagers, and only to certain bettor segments, concentrating computational resources on high-value interactions while using simpler approaches for other scenarios
3Loss of energy
If the wagering network offers improved odds for single plays, then bettor profitability increases, but the network's risk increases
Solution Approach 1:
The odds are dynamically adjusted based on real-time factors including game state, bettor behavior patterns, and risk assessment, allowing the network to offer favorable odds when risk is low while protecting profitability when risk increases
Solution Approach 2:
The system pre-calculates risk exposure for different odds scenarios and sets predetermined limits and conditions that automatically prevent situations where the network's risk exposure would become unacceptable
Data Source
AI summary
Improving the profitability of an in-play betting system by identifying high frequency and high wager amount users and promoting an increase in wager amount or frequency by offering incentives to increase a user's wager amount, if identified to be a high frequency bettor or wager frequency, if identified to be a high wager amount bettor.


