Customized Event Media Simulation for On-Demand Sports Betting
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Solution Overview
Problem
The inflexible scheduling of sporting events and limited availability of events of interest to users hinder sports media consumption and betting experiences, particularly during off-seasons or scheduling conflicts.
Innovation Solution
A system generates personalized sports media content using historical sports data, simulating games to match historical outcomes, identifying betting scenarios, and creating media content for on-demand sports betting through a simulation engine and generative AI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sporting events are scheduled according to traditional seasonal calendars, then the structure and organization of sports leagues is maintained, but users cannot access sports content during off-seasons or when events conflict with user availability
Solution Approach 1:
The system performs preliminary actions by simulating sports events in advance using historical data and team attributes before actual events occur. This allows users to access simulated game content during off-seasons or when real events are not available, effectively preparing content ahead of time to fill temporal gaps in the sports calendar.
Solution Approach 2:
The system creates copies of sports events through simulation, generating virtual game scenarios that replicate the structure and outcomes of actual sports events. These simulated event copies provide users with access to sports content when original events are unavailable, maintaining engagement without requiring physical presence at actual games.
2Productivity
If the number of simulated games is increased to provide more betting opportunities, then user engagement and betting options are improved, but the computational resources and processing time required increase significantly
Solution Approach 1:
The system applies partial action by generating a focused set of betting scenarios based on selected teams and events rather than simulating every possible game outcome. This selective approach produces sufficient betting content for user engagement while avoiding the excessive computational burden of exhaustive simulation of all potential scenarios.
3Measurement precision
If historical sports data is extensively modified to match historical outcomes precisely, then the accuracy of simulated results is improved, but the complexity of the simulation process and data adjustment increases
Solution Approach 1:
The system uses feedback mechanisms by comparing simulated outcomes against historical results and iteratively adjusting team attributes and simulation parameters. This closed-loop process continuously refines the accuracy of simulated outcomes based on performance feedback, achieving high precision through systematic adjustment rather than complex one-time modifications.
Data Source
AI summary
Systems and methods are provided herein for providing on-demand sports betting by generating sports media content using historical sport outcomes. This may be accomplished by a simulation engine simulating a plurality of games using historical sports data. The simulation engine modifies the historical sports data based on the simulated results of the plurality of games to generate balanced sports data. A scenario engine may then simulate a plurality of events using the balanced sports data. The scenario engine selects a subset of the plurality of events based on the simulated outcomes of the plurality of events. The scenario engine also generates betting information related to the selected subset of the plurality of events. The betting information is used to generate a user interface comprising personalized betting options and simulated sports media content related to the personalized betting options.