The Automated General Manager: An Unbiased, Backtested Algorithmic System for Drafts, Trades, and Free Agency that Outperforms Human Front Offices
NYU Poly - Department of Finance and Risk Engineering
December 12, 2014
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 both as an expert-system replacement or 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 $165,000,000 worth of on-court productivity relative to what they could have had with the automated GM. 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, and offers a variety of performance metrics; it is thus robust, comprehensive, and does not overfit information from the future. I provide virtually all of the interactive tools supporting this paper, including backtesting results, projections, scenario analysis, and more, online, for free, at nbagm(dot)pm.
Number of Pages in PDF File: 8working papers series
Date posted: April 24, 2014 ; Last revised: December 13, 2014
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