Gaming Analytics Platform Player Performance Variance
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Solution Overview
Problem
Conventional fantasy sports analytics platforms rely heavily on unreliable conventional metrics for player performance projections, particularly for rookies or less experienced players, leading to suboptimal team selections in fantasy sports games.
Innovation Solution
An advanced analytics platform that ingests data from multiple sources, generates user interfaces for detailed player analysis, and applies modeling algorithms to provide more accurate predictions and insights, including user-directed performance levels and outcome calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional metrics are used for player performance projections, then the analytics platform is simple and easy to operate, but the reliability and measurement precision of player performance projections deteriorate
Solution Approach 1:
The patent segments the analytics platform into distinct functional modules: data ingestion module, data storage module, analysis module, and user interface module. Each module handles specific tasks independently, making the complex system manageable while improving projection reliability through specialized processing of different data types (player stats, team data, weather, injuries).
Solution Approach 2:
The patent introduces an intermediary data processing layer that converts raw data from multiple sources into structured analytics. The system uses intermediate data tables and temporary datasets to bridge the gap between raw data ingestion and final projections, enabling complex analyses without directly exposing system complexity to users.
2Measurement precision
If multiple data sources and comprehensive analysis are implemented, then the measurement precision and reliability improve, but the ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically ingesting data from multiple sources, storing it in structured formats, and generating projections without requiring manual user intervention. The analytics platform autonomously processes data, applies analysis algorithms, and presents results through user-friendly interfaces, maintaining precision while simplifying operation.
Solution Approach 2:
The analytics platform is designed as a universal system that handles multiple functions: data collection from various sources, data cleaning, statistical analysis, machine learning projections, and visual presentation. This multi-functionality consolidates what would otherwise be separate complex systems into one unified platform that maintains precision while improving ease of use.
3Reliability
If conventional fantasy sports tools are used, then the device complexity is low, but the reliability of team selection advice deteriorates
Solution Approach 1:
The patent applies composite materials metaphorically by combining multiple data types and analysis methods into a unified analytics system. The system integrates player statistics, team data, weather conditions, injury information, and machine learning models to create comprehensive projection advice, improving reliability while managing complexity through structured integration.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing data from multiple sources before generating projections. Historical data is collected, cleaned, and organized in advance, allowing the analytics platform to quickly generate reliable team selection advice without requiring complex real-time processing during decision-making moments.
Data Source
AI summary
Techniques for an analytics platform associated with gaming are described. According to certain aspects, systems and methods include ingesting data from a plurality of data sources into a database to support one or more tools. The tools may be configured to cause the analytics platform generate a temporary data table used to populate user interfaces associated with the tools. Additionally, systems and methods may include obtaining user-directed performance levels for one or more players. In response, the analytics platform executes a modeling algorithm based on the user-directed performances levels to generate a user-adjusted event outcome.