13 Pages Posted: 24 Apr 2014 Last revised: 3 Jul 2015

Date Written: July 2, 2015

Abstract

I introduce an automated system and interactive tools for NBA teams to better decide who to draft, who to trade for, and who to sign as free agents. This automated general manager can serve either as an expert-system replacement or a complement to a team's front office, and also as a calibrating benchmark to compare against actual team building performance. Backtested over the past ten years, the automated GM outperforms every single team, and by substantial margins that often represent a major portion of the team's market value. From draft decisions alone, the average team lost about $130,000,000 worth of on-court productivity relative to what they could have had with the automated GM; this shortfall represents about a quarter of the average franchise value. Historically the automated GM's choices would have produced about twice as much as the human choices actually did: approximately one extra win per year per draft pick. The system is calibrated using an innovative extension of traditional machine learning methods, applied to a uniquely broad historical database that incorporates both quantitative and qualitative evaluations, in a way that avoids possible survivorship bias, and for a variety of performance metrics; it is thus robust, comprehensive, realistic, and does not overfit information from the future.