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Clustering Professional Basketball Players by Performance


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are quickly becoming outdated.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.

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