Dynamic Decision Making and the Market for NFL Draft Picks By Michael Band, Carlos Moya, and Chris Yacu University of Chicago Nick Kadochnikov Supervisor Lander Analytics Sponsor A Capstone Project Submitted to the University of Chicago in partial fulfillment of the requirements for the degree of Master of Science in Analytics Graham School of Continuing Liberal and Professional Studies March 2017 Abstract In an environment driven by unique compensation constraints, the National Football League Draft provides teams with the best opportunity to gain advantages in roster composition. This research explores the market value of draft picks, estimates the expected value of player performance as a function of draft order, and proposes a dynamic strategy to support trade negotiations in real-time. The researchers find significant differences between the market value and the expected performance value of draft picks. This discrepancy is critical to the evaluation of potential trades—an NFL team can maximize its expected return from trades by selling (trading down) at or above market price, and buying (trading up) at a price reflective of the targeted player. Keywords: dynamic decision making, instance-based learning theory, efficient market hypothesis, competitive bidding, player personnel, professional football, NFL Draft i Executive Summary For an individual team in the dynamic environment of the NFL Draft, a series of decisions can often determine the fate of the franchise. To effectively navigate trade opportunities in real-time, decision makers must have a support system in place to evaluate options instantaneously. The purpose of this research is to transform analytical insights into an actionable strategy that can adapt to a team’s specific intention when making trade decisions during the draft. Several models are developed to assign a value to each draft pick represented by the trade market, historical performance, and expected surplus. The results of the models power the memory of an application that can support the trade negotiation process and improve decision utility. The research finds the market no longer abides by the market convention known as the chart. The trade market from 2009-2016 appears to behave more efficiently than the market from 1983-2008 (Massey & Thaler, 2012), though the discount rate for future draft picks remained consistent across periods—135% annually. As the market becomes more efficient, accumulating future picks becomes the more practical arbitrage-seeking strategy. Through analysis of historical player performance metrics, the researchers build position- specific models to assign a value in salary cap dollars to the single-season performance of NFL players. The models are trained by veteran performance metrics and compensation data to estimate the value of rookie performance on the unrestricted market. Regression is applied to the results aggregated by draft pick. The model finds performance declines monotonically as a function of draft order, but surplus does not. In fact, surplus increases as a function of draft order until its apex, the 19th overall pick, followed by a gradual decline. Despite decreases in salaries for rookies following the 2011 collective bargaining agreement, top picks are still paid at a disproportional figure relative to rest of the draft based on performance ii expectations. However, since all draft picks are expected to yield positive surplus, the draft is the most efficient method to acquire new players since the cost of draft picks is cheaper than the cost of veterans with equivalent performance. By comparing the performance model results to the trade market, the research finds the slope of the market declines faster than performance. However, the difference in values for the top 10 picks is minimal, which indicates the market effectively values top picks relative to the first overall pick. This validates the hypothesis that the trade market is becoming more efficient. The values of the performance model are used as the basis for a new pick valuation mechanism, the DC Chart. Performance value varies by player position. The research finds the quarterback, edge defender, and offensive tackle positions are the premium positions in the draft—they are the only position groups expected to yield positive surplus value for the first overall pick. Conversely, since performance value is significantly lower for the tight end, guard/center, and defensive safety positions, the research warns against using top picks on these positions. The research agrees with Massey & Thaler’s theory on draft-day trades, with an exception—never trade up for a top pick, unless it’s for a quarterback, and the price is reflective of the adjusted performance estimates. A quarterback is expected to outperform the average first pick by 115%, while all other positions yield less than 91%. The results of the valuation models are used to evaluate trade opportunities in real-time. The application proposed in this research can evaluate trade offers instantaneously, account for variations in value for the given situation, and identify optimal alternatives. A key feature of the application is an optimization algorithm that searches through all possible trade combinations between two trade partners to find terms that yield the most utility for the team within the limits of the market. iii Table of Contents 1. Introduction ............................................................................................................................ 1 2. Background ............................................................................................................................ 2 2.1. The Market Convention ................................................................................................... 2 2.2. Player Compensation ....................................................................................................... 4 2.3. The Value of Player ......................................................................................................... 6 2.4. Dynamic Decision Making and Instance-Based Learning Theory.................................. 8 3. Research Hypotheses ............................................................................................................. 9 4. The Market Value of Draft Picks ......................................................................................... 11 4.1. Data ............................................................................................................................... 11 4.2. Methodology ................................................................................................................. 11 4.3. Results ........................................................................................................................... 13 4.4. Discussion ..................................................................................................................... 15 5. The Value of Player Performance ........................................................................................ 16 5.1. Data ............................................................................................................................... 17 5.2. Methodology ................................................................................................................. 18 5.2.1. The Starter Index .................................................................................................... 19 5.2.2. Variable Selection ................................................................................................... 21 5.2.3. Regression Model ................................................................................................... 22 5.3. Results ........................................................................................................................... 24 5.3.1. The Value of Veteran Performance ........................................................................ 24 5.3.2. The Value of Rookie Performance ......................................................................... 27 6. The Value of Draft Picks...................................................................................................... 28 6.1. Data ............................................................................................................................... 29 6.2. Methodology ................................................................................................................. 29 6.3. Results ........................................................................................................................... 31 6.3.1 Surplus Value of Draft Picks ................................................................................... 32 6.3.2. Relative Value of Draft Picks ................................................................................. 34 6.3.3. Variance by Player Position .................................................................................... 36 6.4. Discussion ..................................................................................................................... 38 7. Dynamic Decision Making and the NFL Draft .................................................................... 40 7.1. Methodology ................................................................................................................. 40 7.1.1. Recognition ............................................................................................................