nba

NBA Team Twitter Analysis Flexdashboard

I just wrapped up a mini-project that allowed me to do a handful of things I’ve been meaning to do: Try out the {flexdashboard} package, which is supposed to be good for prototypying larger dashboards (perhaps created with {shinydashboard}. Test out my (mostly completed) personal {tetext} package for quick and tidy text analysis. (It implements a handful of the techniques shown by David Robinson and Julia Silge, in their blogs and in their Tidy Text Mining with R book.

Conversion of Old Posts to Bookdown

I’m happy to announce that I’ve finished converting the bulk of my old posts to an e-book, using the Yihui Xie’s wonderful {bookdown} package. The e-book is live on the docs branch of a GitHub repo. The posts (now chapters) apply concepts in the field of decision analysis to evaluate “value” in the NBA Draft. Although analysis of the NBA draft itself is certainly not novel and , I think my approach is fairly original.

NBA Decision Analysis

Apply Decision Analysis (DA) concepts to real-world context. Expand upon existing research related to monetary value of NBA draft picks.

NBA Twitter Sentiment Analysis

Exploration of Twitter sentiment for each NBA team.

Visualizing an NBA Team's Schedule Using R

If you’re not completely new to the data science community (specifically, the #rstats community), then you’ve probably seen a version of the “famous” data science workflow diagram. 1 If one is fairly familiar with a certain topic, then one might not spend much time with the initial “visualize” step of the workflow. Such is the case with me and NBA data–as a relatively knowledgeable NBA follower, I don’t necessarily need to spend much of my time exploring raw NBA data prior to modeling.