Athlete Stock Trading Platform Using Real-Time Performance Pricing
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Solution Overview
Problem
Sports fans and enthusiasts lack an online platform to invest in and monetize their knowledge about athletes, with existing betting platforms being limited to short-lived gambling bets rather than long-term investments in athletes' profiles as stocks.
Innovation Solution
An online trading platform where professional and collegiate athletes' profiles are listed as shares, with stock prices calculated in real-time during games using machine learning and blockchain technology, allowing users to trade based on athletes' performance and market sentiment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demand and supply model is used for setting stock prices, then the pricing mechanism is simple and easy to implement, but it does not reflect real-time athlete performance and market sentiment accurately
Solution Approach 1:
The patent implements dynamic stock pricing by continuously updating prices based on real-time athlete performance data, game statistics, and market sentiment. The pricing model transitions from static to dynamic, allowing stock prices to fluctuate during games and seasons based on actual performance metrics and user trading activity.
Solution Approach 2:
The system incorporates multiple feedback loops including real-time performance data from games, user trading behavior, and market sentiment analysis. These feedback mechanisms continuously inform the pricing algorithm to adjust stock prices dynamically, ensuring prices reflect current athlete value and market conditions.
2Productivity
If real-time stock pricing based on athlete performance is implemented, then the system provides accurate and dynamic pricing, but it requires complex machine learning models and blockchain technology
Solution Approach 1:
The patent segments the pricing system into distinct functional modules: performance data collection from games, machine learning model processing, blockchain transaction recording, and user interface presentation. This segmentation allows each component to be developed and optimized independently while working together to provide real-time trading capabilities.
Solution Approach 2:
The patent introduces an intermediary layer between raw performance data and stock pricing that uses machine learning models to process and interpret athlete performance metrics. This intermediary translates complex performance data into meaningful pricing signals, bridging the gap between sports data and financial markets.
3Loss of time
If users can trade athlete stocks during games in real-time, then the platform enables immediate investment opportunities, but it requires continuous data processing and real-time transaction handling
Solution Approach 1:
The patent implements periodic updates to stock prices during games rather than continuous real-time updates. The system processes performance data at key moments such as after plays, quarters, or halftime, providing timely pricing updates without requiring constant computational processing throughout the entire game duration.
Solution Approach 2:
The system performs preliminary processing of performance data and pre-calculates pricing adjustments before they are needed for trading. By anticipating when price updates will be required and preparing calculations in advance, the system reduces computational burden during actual trading moments and enables faster response times.
Data Source
AI summary
An embodiment relates to a system comprising: a processor, a memory, and a database; wherein the processor configured to: receive a real-time data along with a real-time performance of the athlete; determine a recommendation, using a machine learning model comprising artificial intelligence of a recommendation engine, to a user of a stock of the athlete for a future period; determine, a stock price associated with the stock of the athlete in real-time based on a computation metric comprising the real-time performance of the athlete in the game and the historical performance of the athlete, wherein the stock price of the athlete is computed using a blockchain technology; wherein the blockchain technology is configured to facilitate at least one of a creation of transaction of the stock; and wherein a cyber security module is configured for providing security to the system.