Sports analytics is a growing field with many avenues and entry points for students to engage in research projects. In this chapter, we demonstrate relevant topics and skills through the development of an expected goals model in hockey. Through this simple example, we discuss estimating the expected value of an action in sports by building a logistic regression model that is well-calibrated out-of-sample. This brings to attention unique aspects of sports data that must be taken into consideration for cross-validation procedures. Once the reader is comfortable with this model, we discuss how it can be used for evaluating team and player performance. This motivates the direction for introducing a hierarchical model to account for player effects, which is a fundamental method prevalent throughout sports analytics research. Finally, we emphasize the importance in measuring uncertainty of model estimates via resampling of sporting events to resemble simulating seasons of performance. While the example in this chapter is based on hockey data, we make connections to other sports throughout and provide research projects with information about available resources for students to begin their own sports analytics research portfolio.