Player Odds API for Online Casino
Dynamic computation of player odds for numerous casino games using Monte Carlo simulation
Problem Statement
An Indian casino product development company was seeking an analytics solution for dynamic computation of player odds for various casino games on their platform. A product-grade API capable of delivering real-time odds after each action/event in a game was required.
Challenges
- 8 casino games in total requiring in-depth knowledge of gameplay and nuances
- Latency in estimation had to be low as the games were real-time and players had to be kept engaged
- Complexity of certain games made deterministic mathematical approaches almost impossible
Solution
Summary
I designed a Monte-Carlo simulation based approach to estimate odds. The game play was replicated in efficient python code that was designed to run a large number of simulations in each call to estimate odds. The simulator was wrapped in an API that received requests from the frontend and delivered the odds to be implemented for that scenario.
Approach
- Codifying the rules and points system of each game
- Creating virtual card decks and assigning probabilities to the next card drawn, etc.
- Simulating the game 25000 times
- Computing probabilities and odds based on the aggregated outcomes of simulations
Games Developed
Baccarat, Blackjack, Card Race, Joker, Poker, Super Over, Teen Patti, Worli Matka
Deliverable
- Production-ready codes for Python Flask based APIs for each game
Modular Code for Each Game - Poker Example
Sample Request and API Response - Blackjack Example
Results & Impact
Latency in Odds Generation
Accuracy of Odds
Number of games implemented
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