1. Quick Studies? An Investigation Into “Learning” Based on the NFL Draft By Christopher Ferraro and Jacob Leibenluft

2. Introduction A recent analysis of NFL draft behavior (Massey and Thaler, 2005) finds that teams tend to exhibit “overconfidence” in their ability to assess unproven talent In particular, what Massey and Thaler find is that the NFL market tends to “overvalue” high draft picks relative to lower picks or players with known values

3. Teams value early picks far more than later picks…

4. …but some later picks have a greater surplus value

5. Does Learning Happen? We would expect teams to learn over time and exploit market inefficiencies Some have: the example of the New England Patriots –Only two top 10 picks in the last 10 drafts –Won three of the past four Super Bowls Loser’s curse: Bad teams get higher picks, but those picks do not produce as much surplus as lower choices

6. Experimental Setup List of players with estimated “PowerPoints” An “opt-out” value that declines with each successive pick Group 1: No information given after each round Group 2: Draft pick’s actual value, plus the names of the top 2 players revealed to each participant

7. Assumptions For participants 1-6, the optimal strategy will usually be to pick the player with the highest PowerPoints For participants 7-9, the optimal strategy will usually be to opt out Our hypothesis: Over time, participants will recognize their optimal strategy

8. Assigned Abilities Players 1-3: PowerPoints distributed around actual value, small spread (sd=5) Players 4-6: PowerPoints distributed around actual value, large spread (sd=20) Players 7-9: PowerPoints randomly assigned

9. Surplus Value – Normal Evaluation We looked at the surplus from each pick — what a participant earned over the comparable opt-out value. For subjects with PowerPoints distributed around the actual value, we expected to see steady oscillation above zero, meaning these subjects were generally making the correct picks. * Data from Group 1, subject ID 6 Expected Results by Player

10. For a subject with a random distribution (IDs 6-9), we expected to see some early oscillation around zero and then a convergence to zero as they choose to opt out more and more. * Data from Group 2, subject ID 7 Surplus Value – Random Valuation

11. Normal SubjectsRandom Subjects Overall Results, Group 1

12. Normal SubjectsRandom Subjects Overall Results, Group 2

13. In general, Group 1 tended to opt out more frequently. With respect to the top 5 picks, 57 percent of our random subjects in Group 1 chose to opt out relative to 14 percent in Group 2 Summary of Opt-Out Strategies Opt-Out Analysis

14. Gain for Top 5 Picks Surplus from Top Five Picks Note: In this graph, surplus for participants who opted out is calculated as the opt-out value minus what the participant would have earned if he had selected the highest rated player on his sheet

15. Regression Analysis Learning appears to happen around Round 4 in the second group, but not in the first group - offering weak support for our hypothesis!

16. Analysis Why should we be cautious in interpreting these results? –Sample size (number of rounds and number of players) –Some players chose alternate strategies we didn’t expect (Group 2, Player 9) –Questionable design in some cases — three groups instead of two

17. Applications The job market –Does a boss rely on his/her own assessment or on proven performance? –How do different employers react to the behavior of other employers? Auctions for antiques/art –Do buyers learn to follow their own assessments or the “herd”? –“Bargain hunting” vs. masterpieces