Skill-Based Matchmaking Using Interoperable Metrics for Low Liquidity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing skill-based matchmaking (SBMM) systems in online competitive gaming face challenges with low player liquidity, leading to extended match times and vulnerabilities to player exploitation, particularly in real-money tournaments, lacking flexibility across different games and configurations.
Innovation Solution
A dynamic skill-based matchmaking system that includes a matchmaking engine capable of cross-buy-in matching, using multiple algorithms and real-time configuration to ensure fair and fast matches, while preventing exploitation, and integrating monitoring and observability techniques to adapt to various game configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional skill-based matchmaking systems are used, then skill matching accuracy is maintained, but match times extend to days and player liquidity becomes insufficient
Solution Approach 1:
The system dynamically adjusts matchmaking parameters including skill thresholds, buy-in tier configurations, and matching criteria based on real-time player availability and game state. This allows the system to maintain skill matching accuracy while adapting to varying player liquidity conditions, reducing match times from days to seconds.
Solution Approach 2:
The system implements multiple interoperable skill metrics (Elo, Bayesian, Glicko-2, MMR, performance rating, win-loss ratio, K-D ratio) that can be selected and adjusted based on game configuration. By changing the parameters and thresholds of these metrics dynamically, the system maintains matching precision while accommodating different player pools and time constraints.
2Reliability
If rigid skill-based matchmaking rules are applied, then fairness is maintained, but system flexibility across different games and configurations is reduced
Solution Approach 1:
The system provides a universal matchmaking framework that supports multiple game types, buy-in configurations, and skill metrics through a common architecture. The configurable parameters and interoperable metrics allow the same system to maintain fairness across different games while adapting to specific configuration requirements, eliminating the need for bespoke solutions for each implementation.
Solution Approach 2:
The system dynamically configures matchmaking rules and parameters based on game-specific requirements and real-time conditions. This allows rigid fairness principles to be maintained while the specific implementation details adapt flexibly to different games and configurations, resolving the contradiction between reliability and versatility.
3Device complexity
If skill-based matchmaking is implemented without dynamic configuration, then system simplicity is maintained, but observability and adaptability to different games are insufficient
Solution Approach 1:
The system segments matchmaking configuration into modular, independent parameters (skill metrics, buy-in tiers, matching thresholds, game configurations) that can be individually observed, measured, and adjusted. This segmentation maintains overall system simplicity while enabling detailed observability and adaptability to different games through configurable parameters.
Data Source
AI summary
The subject technology receives an online request from a first player to enter a game via a network. The subject technology determines a set of interoperable skill metrics of the first player, each interoperable skill metric corresponding to a different skill metric. The subject technology selects a particular interoperable metric from the set of interoperable skill metrics based on an analysis of prior game data of the game and an indication of a predictive performance of each interoperable skill metric. The subject technology performs a skill-based matchmaking process to match the first player to at least a second player based at least in part on the selected particular interoperable metric. The subject technology initiates an instance of the game for the first player and the second player.