Live Event Prediction Interface with Real-Time Outcome Updates
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Solution Overview
Problem
Conventional box pools for live event predictions are static, requiring participants to wait until the end of the event to determine the winning outcome, providing little real-time interaction or understanding of how event actions affect predictions.
Innovation Solution
A computer-implemented system that generates and manages cells for live event outcomes, using historical data to predict results and provide real-time updates to participants through a user interface, allowing for dynamic participation and immediate feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional box pools are used for live event predictions, then participants can make predictions, but they must wait until the end of the event to know if their prediction was correct
Solution Approach 1:
The system transforms static end-of-event prediction into dynamic real-time prediction updating. Participants can modify their predictions at multiple time intervals during the event based on changing conditions, and the system continuously updates predicted outcomes as new information becomes available, allowing participants to see results evolve rather than wait for a final static result.
Solution Approach 2:
The system implements continuous feedback loops where participants receive immediate updates on how current event conditions affect their predictions. The system provides real-time information about how actions during the event impact predicted outcomes, allowing participants to understand the relationship between event developments and prediction results without waiting until the end.
2Loss of information
If conventional box pools are used for live event predictions, then participants can make predictions, but they have little understanding of how actions during the event affect their predictions
Solution Approach 1:
The system provides continuous feedback to participants about how specific event actions and conditions affect their predictions. Participants receive updates explaining the relationship between event developments and predicted outcomes, enhancing their understanding of prediction mechanics through transparent, real-time information about the prediction model's response to changing conditions.
Solution Approach 2:
The system acts as an intermediary that translates complex prediction algorithms and event data into understandable information for participants. It mediates between the complex underlying prediction model and the user, providing clear explanations of how event actions influence predictions without requiring participants to understand the full complexity of the prediction system.
3Productivity
If real-time prediction updates are implemented, then participants receive immediate feedback, but the system complexity increases
Solution Approach 1:
The system performs preliminary processing and preparation of prediction models and data structures before events begin. Prediction algorithms are pre-configured and data pipelines are established in advance, allowing the system to quickly process and update predictions in real-time during events without having to build complex processing infrastructure from scratch during the event itself.
Solution Approach 2:
The system uses dynamic architecture that can scale and adapt processing resources based on event demands. Rather than maintaining constant high-level complexity, the system dynamically adjusts its processing capacity and computational resources to match real-time requirements, optimizing the balance between update speed and system complexity.
Data Source
AI summary
A method for providing an interactive interface for live event outcome selection and prediction may include generating a set of cells for an event. The set of cells may be provided to a client device to present in a user interface. A selection of a cell may be received from the client device. The selected cell may be assigned to a user account. The method may generate a coordinate for each cell in the set, wherein each cell coordinate includes a plurality of dimensions, each dimension corresponding to a different entity of the event, and wherein each cell coordinate is unique for the set of cells. The method may comprise calculating, prior to the start of the event, a probability that an event result represented by a cell coordinate will occur; and presenting the probability in association with the cell on the user interface. Other embodiments are described and claimed.