Fantasy Sports Group Matching and Skill-Based Payouts
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Solution Overview
Problem
Traditional fantasy sports games lack customization and flexibility, failing to accommodate diverse user preferences and skill levels, and there is a need for systems to ensure fair and competitive groupings and payouts in peer-to-peer (P2P) fantasy sports contests.
Innovation Solution
A computing infrastructure that forms player groups based on user selections, historical participation, and skill levels, using algorithms and data analytics to match users into competitive groups, and determines payouts based on difficulty levels and entry fees, integrating real-time data from external resources to ensure fairness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If peer-to-peer fantasy sports contests are implemented to allow user autonomy in defining competition parameters, then customization and flexibility are improved, but technical complexity of matching users into fair groups and calculating payouts increases
Solution Approach 1:
The system segments users into different skill levels (novice, intermediate, expert) and creates separate contest groups for each level. This segmentation allows the complex P2P matching problem to be divided into smaller, more manageable sub-problems, reducing the overall technical complexity while maintaining customization and flexibility.
Solution Approach 2:
The patent introduces an intermediary matching system that automatically assigns users to appropriate contest groups based on their skill levels and preferences. This intermediary layer handles the complex matching and payout calculation logic, shielding users from the technical complexity while enabling customized P2P contests.
2Ease of operation
If users are given autonomy to define competition parameters in P2P contests, then user engagement is improved, but the difficulty of ensuring fair and competitive groupings increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing skill level assessments and contest group definitions before users create their contests. User skill levels are determined in advance through performance metrics and self-assessment, and contest groups are pre-configured with specific skill level ranges. This preliminary preparation simplifies the user experience while ensuring fair groupings.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor contest performance and adjust user skill level classifications accordingly. The system tracks user performance across multiple contests and uses this feedback to refine skill level assessments, ensuring that future groupings remain fair and competitive as users improve their skills.
3Reliability
If dynamic payout structures are implemented based on skill level and contest difficulty, then fairness of payouts is improved, but computational complexity increases
Solution Approach 1:
The patent uses parameter changes to simplify payout calculations by establishing predetermined payout multipliers for different skill level combinations and contest types. Instead of performing complex real-time calculations, the system assigns payout parameters based on pre-defined tables that consider factors like skill level differences, contest difficulty, and entry fees, reducing computational complexity while maintaining fairness.
Solution Approach 2:
The system performs preliminary calculations to establish base payout structures before contests begin. Payout parameters are pre-calculated based on expected skill level distributions and contest configurations, allowing the system to quickly determine final payouts by applying simple adjustments rather than performing complex computations in real-time.
Data Source
AI summary
The disclosed system discussed herein may include systems and methods for forming player groups for a peer-to-peer (P2P) fantasy sports contest based on one or more of selections by the user (e.g., selected projections or selected entry fees) and historical participation (e.g., skill, experience, etc.), and determining group payouts based on payout selection criteria, experience, and skill level of individual group members.