Cross-Market Peer Competitive Gaming Scoring System
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Solution Overview
Problem
Current fantasy sports and gaming systems lack the ability to allow users with diverse interests to compete against each other, as they do not provide mechanisms for user customization and combination of players or statistics from different segments and markets.
Innovation Solution
A computerized system and method for peer competitive gaming that enables users to select and customize non-uniform data sets from various sources, including sports, entertainment, finance, and politics, using a processor to calculate and rank manipulated scoring values based on user-defined parameters and weighting factors, allowing cross-market and intra-market competitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fantasy sports systems use fixed, uniform data sets from single markets, then system complexity is reduced and ease of operation is improved, but adaptability and versatility are limited
Solution Approach 1:
The system dynamically adapts to user needs by allowing customization of data sets, markets, and scoring parameters. Users can modify league rules, select different data sources, and adjust weighting factors, transforming a static system into a dynamic one that evolves based on participant requirements while maintaining operational simplicity through guided interfaces.
Solution Approach 2:
The system enables parameter changes by allowing users to modify scoring weights, select different statistical categories, and adjust data collection parameters. This flexibility in changing parameters allows the same base system to serve multiple fantasy sports markets and customization needs without requiring separate systems for each scenario.
2Adaptability or versatility
If fantasy sports systems allow user customization and combination of players from different segments, then adaptability and versatility are improved, but device complexity and system complexity increase
Solution Approach 1:
The system achieves universality by creating a single platform that handles multiple fantasy sports markets (football, baseball, basketball, hockey, soccer) and custom leagues through common core functionality. The same infrastructure processes player selections, collects statistics, and calculates scores across different sports and customization levels, eliminating the need for separate systems for each sport or league type.
Solution Approach 2:
The system segments functionality into modular components: user interface layer, data collection layer, statistical processing layer, and scoring calculation layer. This segmentation allows complex customization features to be built from simpler, independent modules that can be configured through software parameters rather than requiring complex hardware or system architecture changes.
3Measurement precision
If fantasy sports systems incorporate broader statistical analyses from multiple sources, then measurement precision and information quality are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system introduces intermediary components including data aggregation services, statistical processing engines, and normalization layers that sit between raw data sources and the fantasy scoring system. These intermediaries clean, standardize, and process data from multiple sources before feeding it to the scoring calculation, improving measurement precision while containing complexity within dedicated processing modules rather than distributing it throughout the entire system.
Data Source
AI summary
A computerized method of peer competitive gaming comprising receiving by a processor a league duration timeframe from a computerized commissioner device and selections of first and second pluralities of non-uniform data sets from first and second computerized user devices in addition to data corresponding to the pluralities of user-selected non-uniform data sets from one or more data sources. The method further comprises calculating, one or more statistical values for each user-selected non-uniform data set received from the one or more data sources and a manipulated scoring component value using the one or more statistical values and the data received from the one or more data sources for each user-selected non-uniform data set. The calculated manipulated scoring component values for each user are summed to form a total manipulated scoring value for each user and ranked among the users.