Fantasy Sports Recommendation Engine Weight Adjustment
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Solution Overview
Problem
Conventional fantasy sports draft systems lack an adaptive and automated method to synthesize weighted ranking sources into player recommendations, requiring manual effort and relying on external sources for decision-making.
Innovation Solution
A recommendation engine that receives and adjusts weights for multiple ranking values from various sources, generating tailored recommendations based on user selections and preferences, integrating these weights to optimize draft decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual survey and arbitrary determination of player differences are used, then external sources can be consulted for draft decisions, but the process requires significant manual effort and time
Solution Approach 1:
The system performs automated player evaluation and recommendation generation without requiring manual survey or arbitrary determination. The computer automatically synthesizes ranking sources, adjusts weights, and generates draft recommendations, eliminating the need for manual effort while providing comprehensive draft decision support
Solution Approach 2:
The patent replaces manual mechanical processes (surveying players, determining differences, consulting external sources) with automated computational processes. The system uses algorithms to evaluate players, synthesize multiple ranking sources, and generate recommendations, substituting human manual work with automated mechanical computation
2Productivity
If conventional recommendation engines are used that focus on scoring points without reference to opponent teams, then maximum points can be targeted, but the system lacks adaptability to specific matchup conditions
Solution Approach 1:
The system dynamically adjusts player rankings and recommendations based on matchup conditions. Instead of static point-based evaluations, the system modifies player values according to specific opponent team characteristics, making the recommendation engine adaptive and flexible to different draft scenarios and matchup situations
Solution Approach 2:
The patent changes the parameters used for player evaluation based on matchup context. The system adjusts player rankings by considering opponent team strength, defensive capabilities, and other matchup-specific factors, transforming the evaluation parameters from generic point projections to context-specific performance expectations
3Measurement precision
If multiple ranking sources are synthesized with fixed weights, then comprehensive player evaluation can be achieved, but the system lacks adaptability to individual user preferences and behaviors
Solution Approach 1:
The system incorporates feedback mechanisms that allow it to learn from user selections and behavior patterns. By analyzing how users interact with recommendations and which players they select, the system adjusts weightings of different ranking sources to better match individual user preferences, creating a personalized evaluation system that improves over time
Solution Approach 2:
The system performs preliminary weight adjustment based on user profile data and historical behavior before generating recommendations. By pre-configuring weightings according to known user preferences and drafting patterns, the system tailors the synthesis of multiple ranking sources to individual users from the outset, enhancing both accuracy and adaptability
Data Source
AI summary
A method and device generates a fantasy sports recommendation. The method includes receiving a plurality of ranking values associated with a sport player, each of the ranking values being generated from a respective source. The method includes assigning a weight value to each of the ranking values, the weight value being associated with the respective source. The method includes generating a recommendation value for the sport player as a function of the ranking values and the corresponding weight values. The method includes receiving a selection value for the sport player. The method includes determining a further weight value for each of the sources as a function of the selection value, the recommendation value, and the weight value for the corresponding source.