Dynamic Rake Allocation for Balanced Player Liquidity
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Solution Overview
Problem
Existing online poker systems face challenges in maintaining player liquidity due to uneven rake allocation, favoring skilled players over lesser-skilled ones, leading to an unbalanced player ecology and increased attrition among recreational players.
Innovation Solution
A method and system for allocating rake based on player value contributions, calculated from wagering activity and a player value indicator, which considers net loss, break-even ratio, and newness, ensuring a healthier player pool by rewarding both winning and losing players, thereby promoting player retention and attracting new players.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rake is allocated based on traditional methods (proportional to wagers or fixed), then operator revenue is simplified to collect, but skilled players dominate the player pool leading to increased attrition among recreational players
Solution Approach 1:
The patent changes the parameters used for rake allocation from simple wager-based proportions to a multi-factor player value contribution model that includes win rate, average bet, session duration, and player skill level. This allows the system to adjust rake allocation dynamically based on player characteristics, maintaining player pool balance while ensuring operator revenue.
Solution Approach 2:
The patent segments the player pool into different categories (recreational players, skilled players, high-volume players) and applies differentiated rake allocation strategies to each segment. This segmentation allows the system to protect recreational players from excessive rake while still generating adequate revenue from skilled and high-volume players.
2Productivity
If rake allocation favors skilled players, then operator revenue is maximized from high-value players, but player liquidity decreases due to recreational player attrition
Solution Approach 1:
The system introduces player value contribution as a new parameter that combines multiple factors (wager amount, win rate, session duration, player skill) to determine rake allocation. This allows the system to identify and reward high-value players while providing protection to recreational players, thereby maintaining player liquidity while maximizing operator revenue from the most valuable segments.
Solution Approach 2:
The patent applies different rake allocation qualities to different player segments. Recreational players receive preferential treatment with lower effective rake rates, while skilled and high-volume players contribute more to the rake pool. This local differentiation maintains overall player liquidity while ensuring adequate revenue from high-value players.
3Ease of operation
If a centralized topology is used, then player pool homogeneity simplifies rake collection, but player liquidity is limited compared to distributed topologies
Solution Approach 1:
The patent introduces an intermediary clearing account system that sits between multiple operator poker rooms and the central platform. This intermediary layer handles the complexity of cross-operator rake allocation and fund transfers, allowing the system to support a distributed topology with multiple operators while maintaining simplified rake collection through automated clearing processes.
4Quantity of substance
If distributed topology pools players from multiple operators, then player liquidity increases, but rake allocation complexity increases due to cross-operator fund transfers
Solution Approach 1:
The clearing account system acts as an intermediary that automates the complex rake allocation process across multiple operators. The system calculates player value contributions, determines appropriate rake allocations, and handles fund transfers between operators automatically, reducing the operational complexity despite the distributed topology.
Solution Approach 2:
The system implements automated feedback loops that monitor player activity, calculate value contributions in real-time, and adjust rake allocations dynamically. This automated feedback mechanism handles the complexity of cross-operator rake allocation without requiring manual intervention, enabling the system to support distributed topology with multiple operators.
Data Source
AI summary
A gaming server hosts a turn of a zero-sum game played by a plurality of players via a plurality of websites, each of the websites having a respective clearing account. The application server determines for each player a respective player value contribution based on the wagering activity of the player during the turn and a respective player value indicator associated with the player. The application server determines a total player value contribution based on the player value contributions of all of the players who played during the turn. The application server determines for each player a respective rake allocation based on the player value contribution of the player and the total player contribution. The application server credits each website's clearing account based on the respective rake allocation of each player who used the website to play the game during the turn.