Wagering Game Server Normalizing Skill-Based Bonus Games
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Solution Overview
Problem
Wagering game systems face challenges in maintaining consistent and engaging skill-based bonus games across multiple machines, as the rate of awarding secondary economy assets can vary significantly, affecting player engagement and profitability.
Innovation Solution
A method and system for normalizing the rate of awarding secondary economy assets in skill-based bonus games by collecting game result data, determining if the rate is within a predetermined range, and adjusting parameters such as difficulty levels to ensure consistent awarding rates across all machines, using a wagering game server with a bonus game controller and normalization unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If skill-based bonus games are deployed across multiple wagering game machines with fixed parameters, then device complexity is reduced and ease of operation is improved, but the rate of awarding secondary economy assets varies significantly across machines, harming reliability and player engagement
Solution Approach 1:
The system implements a normalization process that continuously monitors game result data from multiple wagering game machines and uses this feedback to identify and adjust machines with abnormal awarding rates. The controller compares each machine's awarding rate against the collective data and automatically adjusts parameters to bring deviating machines back into the acceptable range, ensuring consistent player experience across all locations.
Solution Approach 2:
The system dynamically modifies game parameters such as difficulty levels, target values, or resource availability in skill-based bonus games based on collected performance data. By changing these parameters selectively on specific machines, the system normalizes the rate of awarding secondary economy assets across the fleet while maintaining the skill-based nature of the games.
2Reliability
If game parameters are adjusted to normalize awarding rates, then reliability and player engagement are improved, but the ability to adapt to different player preferences and machine locations is reduced
Solution Approach 1:
The system applies different parameter adjustments to different wagering game machines based on their individual performance characteristics. Rather than uniform normalization, each machine receives tailored parameter modifications specific to its deviation from the target awarding rate, preserving local adaptations while achieving overall consistency.
Solution Approach 2:
The normalization system operates dynamically by continuously collecting game result data, identifying machines outside the acceptable awarding rate range, and adjusting parameters in real-time. This dynamic approach allows the system to adapt to changing conditions while maintaining reliability, balancing standardization with responsiveness to individual machine performance.
3Manufacturing precision
If extensive game result data is collected for normalization, then manufacturing precision of awarding rates is improved, but loss of time for data collection and processing increases
Solution Approach 1:
The system establishes predetermined acceptable ranges for awarding rates before deployment and uses these pre-defined thresholds to quickly identify machines requiring adjustment. By having normalization criteria prepared in advance, the system minimizes processing time when analyzing collected data and can rapidly implement corrections without extensive analysis delays.
Solution Approach 2:
The system replaces manual analysis and adjustment of game parameters with an automated electronic normalization process. The controller automatically collects game result data, compares it against target ranges, identifies deviating machines, and adjusts parameters without human intervention, dramatically reducing the time required for the normalization process while maintaining high precision.
Data Source
AI summary
In some embodiments, a method comprises initiating a plurality of skill-based bonus games across a plurality of wagering game machines; collecting game result data associated with the plurality of skill-based bonus games; during a normalization process for one of the plurality of skill-based bonus games, determining a rate of awarding a secondary economy asset for the skillbased bonus game is not within a range based on the game result data; adjusting parameters of the skill-based bonus game to normalize the rate of awarding the secondary economy asset across the plurality of skill-based bonus games; and deploying the skill-based bonus game with the adjusted parameters across one or more of the wagering game machines.