Multiplayer Game Session Duration Prediction for Break-Aware Matchmaking
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Solution Overview
Problem
Users in multiplayer online gaming sessions lack information about the gameplay history of their peers, leading to unsatisfying experiences due to unexpected short game sessions or frustrating gameplay interruptions.
Innovation Solution
A method and system that utilize historical gameplay metrics and machine learning to predict the duration of gaming sessions and schedule breaks, providing players with estimated gameplay times and recommendations through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the gaming service assembles a group of users interested in playing the game and displays them for selection, then the user can select players to play with, but the user lacks information about the gameplay history of the users in the group
Solution Approach 1:
The system performs preliminary actions by collecting and storing gameplay history data (connection time, disconnection time, total duration, breaks taken) in a database before the user needs to make selections. This historical data is prepared in advance and can be quickly retrieved when users are assembling gaming sessions, eliminating the need for real-time data collection during the selection process.
Solution Approach 2:
A gaming session database is introduced as an intermediary component between the user selection interface and the historical gameplay data. This database stores and manages gameplay history information, acting as a mediator that provides users with relevant historical data about potential playing partners without requiring direct access to raw data sources or complex queries.
2Reliability
If the user selects players without duration information, then the selection process is simple, but the gaming experience becomes unsatisfying due to unexpected short sessions or frequent breaks
Solution Approach 1:
The system collects and stores gameplay duration metrics (total duration, number of breaks, break durations) in advance in the gaming session database during previous gaming sessions. When users need to select playing partners, this historical duration information is already available for immediate display, eliminating the need for time-consuming real-time data collection and enabling informed selections about expected session lengths.
Solution Approach 2:
The system provides feedback to users by displaying historical gameplay duration information and predicted session lengths for potential playing partners. This feedback loop allows users to see how long previous sessions lasted with specific players, enabling them to make more reliable decisions about future gaming sessions based on actual historical performance data.
3Ease of operation
If the system provides detailed gameplay history information, then users can make informed selections, but the user interface becomes more complex
Solution Approach 1:
The system applies local quality by selectively displaying specific gameplay history information (total duration, number of breaks) at relevant locations in the user interface where users are evaluating potential playing partners. Rather than presenting all possible data uniformly throughout the interface, the most relevant duration metrics are displayed locally near player selection options, making the interface cleaner while still providing essential information.
Solution Approach 2:
The system extracts only the most relevant gameplay duration information (total session duration, number of breaks taken) from the complete gameplay history database and presents it in the user interface. By taking out and displaying only the critical duration metrics that help users make selection decisions, rather than showing all raw data, the interface remains simple while providing sufficient information for informed player selection.
Data Source
AI summary
A method for generating game duration metrics includes assembling a gaming session for a game, with the gaming session identifying a first player and a second player, and accessing a database that includes an historical duration metric for playing the game by each of the first player and the second player. The method also includes accessing contextual data related to the gaming session to be played by the first player and the second player, and generating a prediction of time metrics for the first player and the second player. The predicted time metrics provide an estimated duration time for play of the gaming session. The method further includes executing the gaming session for the first player and the second player. As the gaming session is progressed by the first player and the second player, the duration time for the gaming session is updated periodically.