## Introduction

As outlined in the first post, this installment of the series will be about the model’s estimates and predictions. I will also touch on how to compare predictions between models. For all that to work, I will have to introduce a few mathematical definitions. In a similar way to the first post, I will give both an *intuitive* explanation of every concept and a *mathematical* explanation.

The **Estimates** section will begin with an explanation of how the model fits the data (with a few graphs to show the process of finding the estimates for last season’s NBA), followed by a brief introduction on Bayesian inference. Next, the **Model Comparisons **section will define the predictive likelihood of a model, and how to calculate it. I will also discuss a few different ways to compare predictions. Finally, in the **Discussion** section, I will offer critiques of the work presented in this post.