1. W HAT DOES IT MEAN TO FIND THE F ACE OF THE F RANCHISE ? P HYSICAL A TTRACTIVENESS AND THE E VALUATION OF A THLETIC P ERFORMANCE DAVE BERRI, ROB SIMMONS, JENNIFER VAN GILDER & LISLE O ’ NEILL WEAI Portland June 30 2010 Economics of the NFL

2. U NIVERSAL B EAUTY (F IRST D OWN ) “Beauty is in the eye of the beholder” Beauty affects our judgment from cradle to grave Sociological studies indicate proportion as a commonality Samuels (1994) says infants pay greater attention to symmetrical objects Honekopp (2006) finds human ratings of attractiveness confirm symmetry ratings

3. S YMMETRY : Q UANTITATIVE B EAUTY Measuring beauty in a quantitative manner Technological link between symmetry and human perception of attractiveness Gunes and Piccardi (2006) find high correlation between human ratings and digital ratings

4. B EAUTY IN THE L ABOR M ARKET Hamermesh and Biddle’s findings 1. Premium for beauty and penalty for ugliness 2. 3 reasons for premium or penalty Olson and Marshuetz (2005) suggest beauty has a hiring impact Our paper differs through use of symmetry analysis

5. D ATA : W HY Q UARTERBACKS ? (S ECOND D OWN ) Data Richness Acquired from NFL.com Subjects: 312 Quarterbacks from 1994-2006 QBs seen as ‘the face of the franchise’, have a leadership role on team, role models for fans & young players, attract media publicity Contributing factors of Productivity measurement included in the “passer” rating Creation of 2 data sets: primary and secondary quarterbacks- which can be merged into one set

6. M ETHOD AND T HEORY (T HIRD D OWN ) Images provided by NFL homepage and Yahoo sports Theory: why would a GM hire a better-looking quarterback? Marginal revenue product Utility maximization Null Hypothesis, given that B 2 is defined as the coefficient on the beauty variable: H 0 : B 2 = 0 [no impact of beauty on pay] H A : B 2 > 0 [beauty has a positive effect on pay, given performance & experience]

7. S YMMETRY A NALYSIS Software: symmeter.com Three Examples of Analysis and Results Symmetry Value: 98.87103438162 % Symmetry Value: 97.5382309740% Symmetry Value: 75.28242925108034 %

8. D ESCRIPTIVE S TATISTICS VariableMeanStd DevMinimumMaximum Symmetry97.731.81691.399.8 Cap Value3.402.728015.4 Plays446.8139.30757 Attempts386.5125.5160691 Pro Bowler.4625.499101 Primary Quarterbacks VariableMeanStd DevMinimumMaximum Symmetry97.232.3199945 82.470050299.6671000 Cap Value0.920.93225150.03310007.8953000 Plays57.0 55.55724910199.000000 Attempts47.648.20621540181.000000 Pro Bowler.1361.343225501 Secondary Quarterbacks

9. F INAL M ODEL R ESULTS (F OURTH D OWN ) Model: lnSAL = b 0 + b 1 *PYARDS + b 2 *CPASSATT + b 3 *EXP + b 4 *EXPSQ + b 5 * DRAFT1 + b 6 *DRAFT2 + b 7 *VET + b 8 *NEWTM + b 9 *lnOFFSAL + b 10 *PB + b 11 *SYMMETRY + e t (1)

10. E STIMATION Dependent Variable: Log of Salary Years: 1995 to 2006 n = 480, all QBs Robust standard errors reported. Qualifying condition is at least 1 play in previous season; rookies excluded OLS then Huber Robust Regression

11. OLS RESULTS VariableCoefficient Standard Errort-stat PYARDS*0.000250.000038.630 CPASSATT*0.000130.000034.880 EXP*0.1330.0522.570 EXPSQ*-0.0090.003-2.940 DRAFT1*0.8090.08010.160 DRAFT2*0.6140.1374.490 VET*0.4560.1153.950 NEWTM*-0.3530.082-4.290 lnOFFSAL**0.2870.1202.390 PB*0.1860.0692.680 SYM**0.0410.0172.470 R-squared0.64

12. VariableCoefficient Standard Errort-stat PYARDS*0.000260.0000310.240 CPASSATT*0.000110.000033.970 EXP*0.1690.0433.920 EXPSQ*-0.0100.002-4.390 DRAFT1*0.9020.07811.580 DRAFT2*0.7010.1335.280 VET*0.4720.1094.320 NEWTM*-0.3560.072-4.920 lnOFFSAL**0.2490.1182.100 PB*0.2010.0722.780 SYM**0.0380.0172.230

13. N OTEWORTHY I MPLICATIONS Variables Primary Parameter Estimates Secondary Parameter Estimates Symmetry * 0.03313 Black * * Black*Symmetry -0.12560 0.16980 Draft1 0.71490 2.71000 Draft2 * 0.39060 Pro Bowler 0.37220 0.43770 Experience 0.04630 0.09830 Experience 2 -0.01219 0.01189 QB Rating 0.00435 0.00183 Change Team 0.55060 0.17300 Year 0.04576 0.03940 Attempts 0.00234 0.00183 *Variable not statistically significant.

14. F UTURE R ESEARCH AND THANK YOU ( TOUCHDOWN ) Caveats Consider using one stat per QB (average, lifetime max?) Recent literature indicates CPI over-deflates: different deflators may give different results; earlier regressions had year summies Quantile Regression was used in JSE QB Race study QB & receiver performances interact-QBs and receivers are each credited in stats for yards gained- who was really responsible?