NIL Activity Pricing Valuation Model Using Real Market Data
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Solution Overview
Problem
The Name, Image, and Likeness (NIL) endorsement market lacks a fair market pricing tool, making it difficult to determine transparent and accurate pricing for various NIL activities due to the uniqueness of athletes' characteristics and the rapid growth of athletes in the United States.
Innovation Solution
A system and method that uses real market data, including completed and disclosed deals, to calculate suggested activity pricing through a valuation model, considering user attributes such as gender, sport, position, institution, and follower count, to provide a fair market value for NIL activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pricing methods are used for NIL activities, then the pricing process is simple, but the pricing accuracy and fairness deteriorate due to lack of real market data and unique athlete characteristics
Solution Approach 1:
A valuation model acts as an intermediary between real market data and pricing determination. The model processes completed deal data, disclosure data, and user characteristics through a structured algorithm to generate suggested activity pricing, resolving the contradiction by introducing a mediating system that transforms complex data into accurate pricing without requiring direct complex calculations
Solution Approach 2:
The patent replaces manual pricing negotiation with an automated computational system. The valuation model uses programmed algorithms to process data and determine pricing, substituting the mechanical complexity of manual analysis with electronic computation that achieves higher precision while managing system complexity through standardized processing steps
2Measurement precision
If personalized pricing is provided for each athlete, then pricing fairness improves, but the difficulty of determining pricing increases due to unique athlete characteristics
Solution Approach 1:
The pricing determination process is segmented into distinct stages: data collection (real market data, user characteristics), data processing (filtering and adjusting datasets), and pricing generation (calculating suggested activity pricing). This segmentation reduces the difficulty of measuring unique athlete characteristics by breaking down the complex evaluation into manageable, standardized segments that can be systematically processed
Solution Approach 2:
The system changes parameters by transforming raw user characteristics (gender, sport, position, institution, follower count) into adjusted pricing parameters through the valuation model. By converting unique athlete attributes into standardized pricing factors and using real market data to calibrate these parameters, the system achieves pricing fairness while reducing determination difficulty through parameter standardization
3Loss of information
If real-time market data is processed, then pricing transparency improves, but the processing time and data volume increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing real market data (completed deal data and disclosure data) in a database before pricing determination is needed. This advance data preparation ensures transparency by maintaining comprehensive market information while reducing processing time during actual pricing calculations, as the data is already organized and ready for quick retrieval and analysis
Data Source
AI summary
A method is disclosed. The method may include receiving real market data from a database; receiving user input data from a user device; retrieving a real-time current follower count for a user; determining at least one of an activity price per follower or an adjusted price per follower based on the retrieved real-time current follower count; generating an adjusted dataset by adjusting the filtered received real market data based on the determined at least one the price per follower or the adjusted price per follower; generating one or more match level tables by reducing the adjusted dataset based on one or more predetermined thresholds; generating a final dataset based on the generated one or more match level tables; and determining a suggested activity price for the user based on the generated final dataset.