See that picture up there? It’s a study in contrast — it’s fifth overall pick Kevin Love, one of the most gifted offensive power forwards in the NBA, and second overall pick Darko Miličić, one of the most notoriously underachieving high lottery picks of the past decade and change. The fact that the freethrows are occurring on the Washington Wizards home floor, a franchise that could be the poster for poor drafting decisions, is just the icing on the cake. In case you missed out, Darko was second to LeBron James and was drafted ahead of Carmelo Anthony, Chris Bosh, and Dwyane Wade. It was not the best choice — although Detroit would win a championship before Cleveland, Denver, Toronto, and Miami, Darko spent the majority of that season riding the pine.
But what’s the true cost of a botched draft pick? It’s very easy to ride the hindsight bias bandwagon and point out that, say, the Portland Trail Blazers really should’ve taken Michael Jordan over Sam Bowie, but in drafts where the talent all comes out about the same in the wash, like 2013 — which really is looking as awful as people said it would — how do you quantify that? What methods would you use? And who would attempt such a seemingly impossible task?
The answer to that last question: the intrepid sailors of the statistical sea over at Wages Of Wins. The website, the mission of which is to shoot down “the lies and damned lies in sports with stories written by the numbers,” recently hosted a post from Phillip Maymin, a professor of Finance and Risk Engineering at NYU, who broke down “a new way to project college performance to the pros using machine learning techniques, and how much money teams have effectively lost by failing to do so over the past decade.” Now we’re taking a look at his ideas and findings.