Game Skill Factor Determination Using Variance Measures
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Solution Overview
Problem
Existing digital game evaluation methods are subjective and fail to objectively determine the relative influence of skill and chance in mixed skill-and-chance games, leading to inconsistent classifications.
Innovation Solution
A system that computes variance measures from game trial data to objectively assess the skill factor and dominant factor between skill and chance in digital games, using percentile scores, normal distributions, and weighted sample variances to classify games as predominantly skill or chance, and provide a confidence level in the classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective assessments are used to determine skill factor, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces subjective human assessment with an automated computational system that calculates variance measures from game trial data. The system objectively determines skill factor by comparing between-player variance to within-player variance across multiple trials, eliminating subjective bias while maintaining ease of operation through automatic computation.
Solution Approach 2:
The patent introduces variance measures as an intermediary metric between raw game outcomes and skill factor determination. By computing between-player variance and within-player variance as intermediate steps, the system provides an objective bridge that translates game trial data into reliable skill assessments without requiring subjective judgment.
2Measurement precision
If multiple variance measures and computations are performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the skill factor determination into distinct computational components: between-player variance calculation, within-player variance calculation, and skill factor computation. This segmentation allows each variance measure to be computed independently using standard statistical formulas, making the complex overall process more manageable and implementable.
Solution Approach 2:
The patent transforms raw game outcome data into variance measures by changing the parameter representation from individual trial results to statistical summaries (variance and standard deviation). This parameter transformation simplifies the complexity by aggregating multiple trials into meaningful statistical metrics that directly inform skill factor determination.
Data Source
AI summary
Data characterizing a quantifiable outcome for each of a plurality of game trials can be accessed. Each game trial can be associated with one of a plurality of players and a game. The game can be a digital game played on a computing platform. Using the accessed data, a first variance measure of the quantifiable outcome for all game trials associated with the plurality of players can be computed. For one or more of the plurality of players and using the accessed data, a second variance measure of the quantifiable outcome for the game trials associated with the one or more of the plurality of players can be computed. Based on at least the first variance measure and the second variance measure, a skill factor for the game can be computed. Data characterizing the skill factor can be provided. Related apparatus, systems, techniques, and articles are also described.