I’m starting to get a foothold oh leveraging PCA to understand the makeup of a team from their players’ PCA bi-plots. It’s really been a useful tool to see where teams were strong, where teams were weak, and the overall balance of a team. Like I left off last time, I’d now like to look […]
Read More All-NBA Predict #16 – Exploration of Historical NBA Players (Part VII, PCA on 2016 Teams)Last time, I explored 9 regions of our PCA bi-plot. I’ll review what I thought the 9 regions represented here: Region 1: All-Star Guards Region 2: All-Star Scorers Region 3: All-Star Big Men Region 4: Great Guards Region 5: Great All-Around / Two-Way Players Region 6: Great Big Men Region 7: Spot Up Shooters Region […]
Read More All-NBA Predict #15 – Exploration of Historical NBA Players (Part VI, PCA to Explore Historical Lineups)In our last post, we harnessed the power of PCA even further to look at different ways to break up the PCA bi-plot. With ggbiplot’s “ellipse” argument, we were able to check out where different players lie on the plot. To review, my 3 main questions were: How closely can I map different “types” of […]
Read More All-NBA Predict #14 – Exploration of Historical NBA Players (Part V, PCA to Cluster Playing Styles)
