Animating Expected Possession Value in the NBA with Player Tracking Data

I'd like to present on a spatiotemporal project I completed last year on Expected Possession Value (EPV) in the NBA. Based on a paper, "POINTWISE: Predicting Points and Valuing Decisions in Real Time with NBA Optical Tracking Data", EPV is calculated using optical player tracking data and denotes the number of points the offense is expected to score by the end of the possession in real time, given everything we know now. In other words, EPV of a possession is the weighted average of the outcomes of all future paths that the possession could take The model breaks down a possession into discrete (macrotransitions) and continuous actions (microtransitions). Macrotransitions include passes, shots, and turnovers whereas microtransitions are defined as every other movement that players make with the ball. I've used the R package gganimate to create an animated graph that convey a strong, clear narrative on player tracking data. I wrote a series of blog posts you can find here: http://insidethetv.rbind.io/post/epv-the-epilogue/