Dynamic Match-Play Game System for Live Sports Prediction Markets
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Solution Overview
Problem
Current game design methods fail to effectively integrate real-time prediction and engagement for individual and crowd participation in live sports events, limiting the interactive experience and competitive elements in multiplayer games.
Innovation Solution
A system and method for developing match-play games that allow users to predict outcomes of live sports events, with features such as determining possible outcomes, creating game designs, and associating predictions with point values and probabilities, enabling user participation and competition based on accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional fantasy league formats are used, then users can participate in sports competitions, but real-time interaction and prediction engagement are limited
Solution Approach 1:
The system transforms static fantasy league rosters into dynamic prediction markets where users continuously update their predictions based on real-time game events. The game state evolves dynamically as users place bets on various outcomes (next play, next score, player statistics) rather than simply drafting fixed teams, creating ongoing real-time engagement throughout the sporting event.
Solution Approach 2:
The system implements immediate feedback loops where prediction outcomes are instantly resolved based on actual game events. Users receive real-time updates on their prediction accuracy, win/loss status, and updated balances, creating a continuous feedback cycle that drives sustained engagement during live sports events rather than delayed season-long feedback.
2Productivity
If simple prediction mechanisms are used, then real-time interaction is enabled, but competitive depth and engagement are reduced
Solution Approach 1:
The prediction market is segmented into multiple independent betting markets (next play, next score, player statistics, game milestones) rather than a single complex fantasy roster. Each segment can be played independently with simple binary or multi-choice predictions, reducing individual decision complexity while providing diverse engagement options that collectively create deep competitive opportunities.
Solution Approach 2:
The system uses a universal prediction interface and settlement mechanism that works across all sport types and prediction categories. The same core technology handles diverse prediction types (yes/no outcomes, numerical predictions, player-specific events), simplifying the user experience while enabling rich competitive scenarios across different sports and game situations.
3Ease of operation
If individual fantasy leagues are used, then user participation is simplified, but crowd engagement and social competition are limited
Solution Approach 1:
The system merges individual prediction accounts into crowd-based teams where multiple users pool their predictions and resources. Users can form or join crowds that collectively compete in prediction markets, combining individual analytical skills with collective decision-making. This creates social competition dynamics while maintaining simple individual account management, as the system handles crowd formation, resource pooling, and collective settlement automatically.
Data Source
AI summary
Developing a match-play game in which an outcome of a live event determines an outcome within the match-play game comprises several steps including determining a set of outcomes that may occur in the live event, presenting the set of outcomes to a game designer, presenting a design interface in which a game designer may create a match-play game, enabling a user to determine a set of outcomes that may occur in the match-play game, and enabling the user to associate at least one outcome in the match-play game to an outcome in the live event.