At the conclusion of his junior season at West Virginia, Joe Alexander led the Mountaineers to better-than-expected finishes in both the Big East and NCAA tournaments. Peaking at the right time, he went from relative obscurity to a lottery pick in a matter or weeks. As the 8th overall pick by the Milwaukee Bucks in the 2008 Draft, Alexander fulfilled his lifelong dream of playing in the NBA. Unfortunately, his dream was short-lived because he was out of the league after scoring fewer than 300 points in 67 games. In retrospect, he likely peaked too soon because his professional career might have been much different with another year to develop in college. Due to his inability to play in the NBA, Alexander has been selected as the #7 NBA Draft Bust.
Jack Thompson was a heralded quarterback from Washington State whose career will always be evaluated in the rear-view mirror of the greatest post-season quarterback in NFL history. As a foreshadowing, the previous sentence can be used to introduce a completely different Top 10 Bust simply by changing the highlighted word. If NFL draft busts were evaluated like NBA draft busts seem to be, Thompson (who was drafted ahead of Joe Montana) would be as well know as Sam Bowie (who was drafted ahead of Michael Jordan). Instead, Thompson hasn’t received his due credit as an all-time bust. With a Weighted Average Value (WAV) of 13 based on career totals of 5,300 passing yards and 33 touchdowns, he has the highest total of any Top 10 Bust. Then again, he had a record of 4-17 as a starter and a total of 45 interceptions so he gained a lot of bonus points. This post should convince you that Thompson, unlike Bowie, was completely unproductive as a professional so his bust status is well deserved.
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.