AI Team Selection System for Sports Auction Optimization
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Solution Overview
Problem
Conventional systems for player evaluation and team building in sports auctions require manual calculations, which are time-consuming and often lead to poor decision-making due to the complexity of weighing various player statistics and probabilities.
Innovation Solution
A system utilizing an AI-based architecture with a genetic algorithm to optimize team selection by determining a balanced fitness score between team quality and bid success probability, considering budget constraints and player attributes, to facilitate informed decision-making during auctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculations are used to evaluate players and compute auction values, then owners can assess player quality, but the process becomes time-consuming and leads to poor decisions
Solution Approach 1:
The patent replaces manual mechanical calculations with an automated computerized auction system that uses algorithms to evaluate player statistics, compute auction values, and generate draft recommendations. This substitution eliminates time-consuming manual computations while maintaining or improving evaluation accuracy through systematic data processing.
Solution Approach 2:
The system enables owners to input their team needs and constraints, then automatically generates optimized draft recommendations without requiring manual intervention for each calculation. The computerized system serves itself by autonomously processing player evaluations, computing bid strategies, and producing draft orders based on predefined objectives.
2Reliability
If owners compute all player auction values before the draft, then they can make informed decisions, but they must perform many manual calculations with regard to the entire pool of available players
Solution Approach 1:
The patent segments the player evaluation process into distinct computational modules: player statistic analysis, auction value computation, team need assessment, and draft recommendation generation. This segmentation allows the complex system to process information systematically through specialized sub-routines, making the overall complexity manageable while maintaining comprehensive evaluation.
Solution Approach 2:
The computerized auction system performs multiple functions within a single integrated platform: evaluating player quality, computing auction values, analyzing team needs, generating bid strategies, and producing draft recommendations. This multi-functionality reduces the need for separate manual processes while maintaining comprehensive decision-support capabilities.
3Ease of operation
If conventional recommendation engines are used to analyze players, then roster move recommendations can be delivered, but the system lacks optimization for auction-style drafts with multiple constraints
Solution Approach 1:
The patent incorporates auction-specific parameters such as budget constraints, bid increment rules, player availability, and team salary caps into the optimization algorithm. By adjusting these parameters, the system adapts conventional recommendation logic to the specific constraints of auction-style drafts, enabling versatile decision-making across different auction scenarios.
Solution Approach 2:
The system dynamically adjusts draft recommendations based on real-time auction conditions, including remaining budget, player availability, and team needs. The optimization algorithm continuously recalculates bid strategies and draft orders as auction conditions change, providing adaptive guidance throughout the drafting process rather than static pre-draft analysis.
Data Source
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AI summary
The present disclosure provides a system and method for optimizing a team selection process during an auction. The method includes receiving a first input associated with a quality of a sports team, receiving a second input associated with a success probability of a bid associated with the sports team, and determining an optimized list of players for the sports team based on the first and the second inputs.