Last week, we used an R optimizer to build a model for predicting game outcomes in the NHL. This week, Ellis Hughes and I continue on that work and build a Monte Carlo Simulation for forecasting NBA games. We use the model to obtain the probability that one team beats the other and then we extract the estimated margin of victory from our simulation and reflect the entire distribution of estimated values, rather than just a single point estimate.
To watch the screen cast, CLICK HERE.
To access our code, CLICK HERE.