Generic Skill Rating Algorithm for Multiplayer Game Matching
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Solution Overview
Problem
New video game titles lack effective skill rating algorithms and historical participant performance data, leading to suboptimal player matching and a poor gaming experience, as they cannot accurately rate players based on skill levels.
Innovation Solution
A generic skill rating algorithm is generated using an aggregate of parameters and weights from established skill rating algorithms of other titles within the same game category, allowing new titles to rate players and match them fairly without needing to develop their own algorithms or store extensive performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a new title uses conventional skill rating algorithms based solely on wins and losses, then the algorithm can be implemented with simple data requirements, but the skill rating accuracy is insufficient for new titles without historical performance data
Solution Approach 1:
The system performs preliminary actions by generating a generic skill rating algorithm in advance that aggregates parameters and weights from multiple established algorithms. This pre-computed framework allows new titles to immediately rate players without requiring extensive historical data, effectively preparing the rating infrastructure before actual gameplay data accumulates.
Solution Approach 2:
The generic skill rating algorithm serves multiple functions by being applicable across different game titles within the same category. It aggregates parameters from various established algorithms (TRUESKILL, ELO, etc.) to create a universal rating system that works for new titles lacking their own historical data, while still providing accurate skill ratings.
2Adaptability or versatility
If a new title develops its own skill rating algorithm from scratch, then the algorithm can be tailored to the specific game, but the development time and complexity increase significantly
Solution Approach 1:
The system segments the skill rating algorithm into two distinct components: a generic framework that handles the complex aggregation of parameters and weights from multiple algorithms, and a game-specific layer that simply selects which established algorithms to aggregate. This segmentation reduces development complexity while maintaining adaptability to different game types.
Solution Approach 2:
The generic skill rating algorithm acts as an intermediary between established algorithms and new titles. Instead of requiring new titles to develop their own algorithms from scratch, the generic algorithm mediates by aggregating parameters from existing algorithms and applying them to new titles, thereby reducing development complexity while maintaining game-specific adaptability through parameter selection.
3Measurement precision
If the system aggregates participant performance data from multiple titles to generate skill ratings, then the skill rating accuracy improves, but the network bandwidth and processing resources increase
Solution Approach 1:
The system extracts only the essential parameters and weights from established algorithms rather than transferring entire datasets. By extracting just the parameter structures and aggregation logic, the system achieves accurate skill ratings without requiring extensive data transfer, thereby reducing network bandwidth and processing resource consumption.
4Ease of manufacture
If the system shares actual participant performance data with new titles, then the new titles can improve their own algorithms, but privacy concerns arise
Solution Approach 1:
Instead of sharing actual participant performance data, the system creates and shares a copied representation in the form of a generic algorithm framework with aggregated parameters and weights. This copying approach allows new titles to benefit from historical data patterns without exposing actual player information, thereby maintaining privacy while enabling algorithm improvement.
Data Source
AI summary
The systems and techniques described herein generate a skill rating of a participant with respect to a new title. The system is configured to generate a generic skill rating algorithm based on an aggregate of parameters and parameter weights that already exist in established skill rating algorithms of other titles of a particular game category. Once the generic skill rating algorithm is generated, the system is configured to aggregate, organize, and plug participant performance data into the generic skill rating algorithm so that a representative skill rating for the participant can be calculated. The representative skill rating is provided to the new title thereby enabling the new title to effectively match participants in a multiplayer session of the new title such that a fair and a competitive gaming experience is realized.