Dynamic Team Rating Model for Cross-League Sports Comparison
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Solution Overview
Problem
Existing systems fail to provide accurate and scalable team ability ratings across different leagues, countries, and continents, particularly in women's soccer, due to a lack of matches and varying team abilities.
Innovation Solution
A dynamic rating system using a deterministic algorithm and machine-learning model that adjusts team ability scores based on multiple data sources, incorporating a hierarchical structure and match importance factors to generate and refine ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If team ability ratings are calculated using traditional methods within domestic leagues, then rating accuracy within the league is improved, but the ability to compare team quality across different leagues, countries, and continents deteriorates
Solution Approach 1:
The patent implements a universal rating system that functions across multiple leagues, countries, and continents simultaneously. The Elo rating algorithm is adapted to handle international competitions like the Champions League and World Cup, allowing the same rating metric to be applied universally across different football contexts while maintaining accuracy within each domestic league and enabling cross-league comparisons.
Solution Approach 2:
The patent introduces intermediary rating adjustments for international competitions that act as mediators between domestic league ratings. These intermediaries facilitate the transfer and comparison of team abilities across different leagues and countries by providing a standardized framework for evaluating teams in international contexts based on their domestic performance.
2Reliability
If ratings are updated dynamically with new match data, then rating currentness is improved, but computational complexity and data processing requirements worsen
Solution Approach 1:
The patent implements dynamic rating updates that automatically adjust team ratings based on new match results. The system continuously processes incoming match data and recalculates Elo ratings in real-time, ensuring ratings remain current and reflective of the latest team performances without requiring manual intervention or complex batch processing.
Solution Approach 2:
The patent incorporates feedback mechanisms where match results immediately feed into rating calculations. The system uses the outcome of each match as feedback to adjust team ratings, creating a closed-loop system that automatically learns from new data and updates ratings accordingly, reducing the need for complex external analysis.
3Loss of information
If the system processes data from multiple data sources including social media, then rating comprehensiveness is improved, but data processing time and system complexity worsen
Solution Approach 1:
The patent segments data processing into distinct modules: core match result processing, team performance analysis, and optional social media sentiment analysis. This segmentation allows the system to process essential rating data quickly while optionally incorporating additional data sources like social media without significantly impacting overall processing time or requiring complete reprocessing of all data.
4Measurement precision
If team icons are rearranged on GUI based on updated ratings, then visual accuracy is improved, but user interface update frequency and system responsiveness requirements worsen
Solution Approach 1:
The patent implements a self-updating graphical interface that automatically rearranges team icons based on updated ratings without requiring manual intervention. The GUI monitors rating changes and autonomously repositions team icons to reflect current standings, ensuring visual accuracy is maintained while reducing the burden on users and minimizing the need for forced system updates.
Data Source
AI summary
A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first rating.