Dynamic Skill Scoring with Bayesian Seeding for Gaming Matchmaking
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Solution Overview
Problem
Existing skill scoring systems in gaming environments face challenges in accurately determining and updating player skills over time, especially when players have varying levels of activity, and in matching players with comparable skills for competitive games.
Innovation Solution
A Bayesian scoring system that uses Gaussian distributions to model player skills, updates mean and variance based on game outcomes, and employs dynamic skill scoring to reflect changes in player abilities over time, while also seeding initial skill scores from compatible gaming environments based on compatibility factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional skill scoring systems are used, then initial skill scores can be set, but they fail to accurately reflect player skill changes over time and varying activity levels
Solution Approach 1:
The patent implements dynamic skill scoring where skill scores are not static but evolve over time based on player activity. The system continuously updates skill scores by incorporating time decay factors and recent performance data, allowing the scoring system to adapt to players who are inactive or highly active. This resolves the contradiction by making the measurement dynamic rather than static, so accuracy is maintained regardless of time elapsed since last game.
Solution Approach 2:
The system uses feedback loops where game outcomes are continuously fed back into the skill scoring algorithm. Each game result updates the player's skill score, which then influences future matchmaking. This continuous feedback mechanism ensures that skill scores remain accurate reflections of current player ability, counteracting the degradation that would occur over time without updates.
2Measurement precision
If skill scores are updated frequently based on game outcomes, then player skill changes are captured accurately, but matching players with comparable skills becomes more complex
Solution Approach 1:
The patent segments the skill scoring and matching process into distinct components: skill score calculation, time decay adjustment, and matchmaking. By separating these functions, the system can update skill scores frequently without proportionally increasing overall system complexity. The segmentation allows each component to be optimized independently, resolving the contradiction between frequent updates and manageable complexity.
Solution Approach 2:
The system changes parameters dynamically based on player characteristics and game context. Instead of using a fixed matching threshold, the system adjusts matching parameters based on skill score distributions, player activity levels, and game type. This flexibility allows accurate matching even with frequently updated scores, as the parameters adapt to the current state of the player population.
3Ease of manufacture
If initial skill scores are set uniformly for all players, then the system is simple to implement, but it cannot leverage historical performance data from compatible gaming environments
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing skill score data from multiple gaming environments before the player enters the new environment. The system identifies compatible games, extracts relevant skill metrics, and prepares seeded initial scores in advance. This preliminary preparation allows the system to leverage historical data without complicating the core implementation, as the complex data processing occurs before the player starts playing.
Solution Approach 2:
The system introduces an intermediary layer that translates skill scores from different gaming environments into a common framework. This intermediary process handles the complexity of data normalization and compatibility assessment, shielding the rest of the system from these complexities. The intermediary converts diverse historical data into standardized initial scores, preserving information while maintaining implementation simplicity.
Data Source
AI summary
Skill scores represent a ranking or other indication of the skill of the player based on the outcome of the game in a gaming environment. Skills scores can be used in matching compatible players on the same team and matching opposing players or teams to obtain an evenly-matched competition. An initial skill score of a player in a new gaming environment may be based in whole or in part on the skill score of that player in another game environment. The influence that the skill scores for these other game environments may have in the skill score seeding for the new game environment may be weighted based on a defined compatibility factor with the new game environment. The compatibility factor can be determined based on a game-to-game basis, compatible categories or features, game developer defined parameters, or any combination of considerations.