With only two months to go before the start of the 2017-18 NBA season, the biggest off-season story still involves Kyrie Irving (aka The Decision, Part II). Despite three consecutive trips to the NBA Finals and one title with the Cavs, Irving presumably wants to escape the long shadow cast by teammate LeBron James. Assuming they can no longer coexist, who would you take? The answer may not be as easy as it seems.
Years from now, most fans will refer to the 2016 NBA Draft as “the one with Ben Simmons.” Generally speaking, we tend to remember the success or failure of the #1 overall pick. For me, however, I’ll remember the 2016 Draft because of ESPN’s entertaining telecast. My son and I already have running jokes about Jay Bilas’ infatuation with wingspans and Lisa Salters’ desire to ask cringe-worthy questions. The leading contender for future recognition as a Top 10 Bust is #4 pick Dragan Bender. Additionally, #10 pick Thon Maker seems poised to become a T10B Honorable Mention.
To be fair, the following post is geared towards “quant jocks” (ok, nerds) who have a reasonable knowledge of statistical distributions. In particular, I have used Weibull distributions to model different subsets of 1st round picks from over 40 NBA drafts. With different shape and scale parameters for each subset, the expected value of a draft pick can be estimated with statistical probability. Based on my analysis, I developed a methodology to define a bust objectively in order to overcome the bias which seems to be apparent in existing lists of all-time busts. If you work for an NBA team and came across this site, you should read this post.