Plots depicting the principal component scores and variable loadings for the first 3 principal components in our WNBA data set.

Each year, the WNBA Most Valuable Player is determined by a committee of sportswriters and broadcasters. Each individual in the committee is asked to select their top 5 players from that season with their top choice receiving 10 points, 2nd receiving 7 points, 3rd receiving 5 points, 4th receiving 3 points and 5th receiving 1 point. So How does the panel decide who they are voting for? That is, Are certain statistics more valuable than others in the eyes of the committee? Can we predict who will be the next MVP based on their statistics? This analysis uses an ordinal regression to examine the relationship between advanced statistical metrics and where a player ends up on the WNBA MVP ladder.