This project’s data set is the overall results from the 2022 Canadian Football League’s (CFL) Combine. The purpose of this project is to faithfully represent the high-dimensional (eight) data set in two-dimensions while preserving the variance (that is, differences in athletic performance) and keeping the process easy to understand by non-statisticians. This is achieved via principal component analysis (PCA), and results in interesting patterns emerging in player position with respect to athletic performance as measured by their Combine results.

This project’s data set is overall results from the 2022 Canadian Football League’s (CFL) Combine. The purpose of this project is to faithfully represent the high-dimensional (eight) data set in two-dimensions while preserving variance (that is, differences in athletic performance) for easy interpretation by non-statisticians. This is achieved via principal component analysis (PCA) and results in interesting patterns emerging in player position with respect to athletic performance as measured by their Combine results.