Normalizing data: And the ones to the left, like JJ Redick, are arguably those least known for their rebounding in our selected subset. Antetokounmpo and Anthony Davis, the two young players considered by NBA General Managers to have the highest potential, are most similar to each other. Note how players who usually only score from the low post have their separate cluster in the top. This makes sense as most of the statistics that we are looking at are offense statistics and therefore these dominate our overall analysis. But in general, the small forwards are noticeably closer to the power forwards than the shooting guards are. For good old basketball fans who could care less, allow me to explain PCA in an over-simplified way. Note that by normalizing the data, we are affecting the outcomes with our own biases. In one of the more bizarre stat-lines last season, he averaged more offensive rebounds than defensive rebounds. Iterate steps 3 and 4 until the cluster assignments stop changing or the maximum number of iterations is reached R uses 10 as the default value for max number of iterations. Specify number of clusters K to be created Randomly select k players from the data set as our initial cluster centroid Assign each player to their closest centroid, based on Euclidean distance For each of the k clusters, update the cluster centroid by calculating the new mean values of all the player features in the cluster. Despite leading the league at boxing out for rebounds, Adams routinely puts up low defensive rebounds. Distance measurement: Point guards PG seem to mostly group together in the top, with high-scoring ones like Curry, Lillard and Kyrie Irving close together. Red indicates high dissimilarity and teal indicates high similarity between players.
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Again, our goal is to get the best clusters while minimizing k. We use the following 2 methods: Point guards PG seem to mostly group together in the top, with high-scoring ones like Curry, Lillard and Kyrie Irving close together.