Monte Carlo Simulation and Multi-objective optimization
Budget: $30 – $250 USD
I need help building scripts for Monte Carlo Simulation for sports games. Need to simulate the outcome events and calculate fantasy points for each player in a sample space. Based on the simulations, I want to be able to run a multi-objective optimization for choosing the best roster combinations.
Python is the preferred language. Open to frameworks such as Pymoo or whatever the top frameworks are today. I have access to all of the data needed.
Deliverables
First Python script that pulls in data, runs Monte Carlo simulations and generates projected values. Second Python script that pulls in the simulation results and runs multi-objective optimization to generate lineups that meet certain criteria. Second script must accept various constraints and rules that can be applied. Final deliverable is a Jupyter Notebook with comments/documentation demonstrating the workflow with appropriate visualizations (where applicable) and where I can make adjustments or add new rules. Scripts and notebook must be reproducible. Need it fast, so willing to pay for speed.
Python is the preferred language. Open to frameworks such as Pymoo or whatever the top frameworks are today. I have access to all of the data needed.
Deliverables
First Python script that pulls in data, runs Monte Carlo simulations and generates projected values. Second Python script that pulls in the simulation results and runs multi-objective optimization to generate lineups that meet certain criteria. Second script must accept various constraints and rules that can be applied. Final deliverable is a Jupyter Notebook with comments/documentation demonstrating the workflow with appropriate visualizations (where applicable) and where I can make adjustments or add new rules. Scripts and notebook must be reproducible. Need it fast, so willing to pay for speed.