Fantasy Sports Odds Calculation Using Historical Data
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Solution Overview
Problem
Current systems for fantasy sports and gaming lack an efficient method to determine and offer odds based on historical performance data, limiting user engagement and accuracy in predicting player achievements in real-time events.
Innovation Solution
A computing device receives user-selected participants and accomplishments, determines odds using historical performance data, and offers the game to users, allowing them to wager with the option to accept and risk money based on the outcome of the participants' achievements in live events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fantasy sports systems use traditional gaming methods without historical data integration, then system complexity remains low, but measurement precision and reliability of odds determination deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical performance data of participants before the actual gaming event occurs. This historical data is then integrated with real-time event data to determine accurate odds. The preliminary preparation of data structures and historical records enables precise odds determination without requiring complex real-time analysis during the event itself.
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between historical data storage and real-time odds calculation. This intermediary layer processes and integrates historical performance data with current event data, allowing accurate odds determination while maintaining system modularity and managing complexity through layered architecture.
2Reliability
If the system integrates historical performance data to determine odds, then reliability and measurement precision improve, but loss of time in data processing increases
Solution Approach 1:
Historical performance data is collected, stored, and pre-processed before it is needed for odds determination. By having this data readily available in structured formats beforehand, the system minimizes data processing time during actual gaming events while maintaining high reliability in odds calculations through comprehensive historical analysis.
Solution Approach 2:
The system replaces complex real-time mechanical data processing with pre-computed historical statistics and algorithms. Instead of processing raw historical data during events, the system uses pre-analyzed performance metrics and statistical models that quickly generate reliable odds without extensive real-time computation.
3Adaptability or versatility
If the system allows extensive customization of participants and accomplishments, then adaptability and user engagement improve, but device complexity increases
Solution Approach 1:
The system implements a universal framework with standardized data structures and modular components that can accommodate various types of participants, events, and accomplishments. This universal architecture allows extensive customization and adaptability while managing complexity through reusable templates, standardized interfaces, and configurable parameters that work across different gaming scenarios.
Solution Approach 2:
The system segments customization options into modular, independent components such as participant selection, event type, accomplishment criteria, and odds parameters. This segmentation allows users to customize each aspect independently without overwhelming complexity, while the system integrates these segments through standardized connection protocols and data structures.
Data Source
AI summary
Some embodiments include a fantasy sports game. In some embodiments, an expected performance value may be determined. In some embodiments, a set of players may be determined, one or more accomplishments may be determined, a parameter may be determined based on the participants and the accomplishments, and a game may be formed. Various other embodiments are described.