Learning from The Machine
I recently read an interesting paper, entitled “Profiting from Machine Learning in the NBA Draft (paper available here).” The author of the paper is Philip Maymin, Assistant Professor of Finance and Risk Engineering at the NYU School of Engineering. Maymin has written several articles applying machine learning techniques to NBA basketball.
Here’s the study’s abstract: I project historical NCAA college basketball performance to subsequent NBA performance for prospects using modern machine learning techniques without snooping bias. I find that the projections would have helped improve the drafting decisions of virtually every team: over the past ten years, teams forfeited an average of about $90,000,000 in lost productivity that could have been theirs had they followed the recommendations of the model. I provide team-by-team breakdowns of who should have been drafted instead, as well as team summaries of lost profit, and draft order comparison. Far from being just another input in making decisions, when used properly, advanced draft analytics can effectively be an additional revenue source in a team’s business model.
Based on The Machine’s* projections, we’re going to discuss some choice decisions the Wolves made in past drafts.
Punch-Drunk Wolves 