Basketball players are traditionally grouped into five distinct positions, but these designations are quickly becoming outdated. We attempt to reclassify players into new groups based on personal performance in the 2016-2017 NBA regular season. Two dimensionality reduction techniques, t-Distributed Stochastic Neighbor Embedding (t-SNE) and principal component analysis (PCA), were employed to reduce 18 classic metrics down to two dimensions for visualization. k-means clustering discovered four groups of players with similar
playing styles. Player representation in each of the four clusters is similar across the 30 NBA teams, but better teams have players located further away from cluster centroids on the scatterplot. The results indicate that strong teams have players whose success cannot be attributed to fundamentals alone, meaning these players have advanced or intangible factors that supplement their performance.