WHAT DOES IT MEAN TO FIND THE
FACE OF THE FRANCHISE?
PHYSICAL ATTRACTIVENESS AND
THE EVALUATION OF ATHLETIC
PERFORMANCE
DAVE BERRI, ROB SIMMONS,
JENNIFER VAN GILDER & LISLE
O’NEILL
WEAI Portland June 30 2010
Economics of the NFL
UNIVERSAL BEAUTY
(FIRST DOWN)
“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

SYMMETRY: QUANTITATIVE BEAUTY
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

BEAUTY IN THE LABOR MARKET

Hamermesh and Biddle’s findings
1.
2.
Premium for beauty and penalty for ugliness
3 reasons for premium or penalty
Olson and Marshuetz (2005) suggest beauty has
a hiring impact
 Our paper differs through use of symmetry
analysis

DATA: WHY QUARTERBACKS?
(SECOND DOWN)
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

METHOD AND THEORY
(THIRD DOWN)
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 B2 is defined as the
coefficient on the beauty variable:
H0 : B2 = 0 [no impact of beauty on pay]
HA : B2 > 0 [beauty has a positive effect on
pay, given performance & experience]
SYMMETRY ANALYSIS
•Software: symmeter.com
•Three Examples of Analysis and Results
Symmetry Value:
98.87103438162 %
Symmetry Value:
75.28242925108034 %
Symmetry Value:
97.5382309740%
DESCRIPTIVE STATISTICS
Primary Quarterbacks
Variable
Mean
Std Dev
Minimum
Maximum
Symmetry
97.73
1.816
91.3
99.8
Cap Value
3.40
2.728
0
15.4
Plays
446.8
139.3
0
757
Attempts
386.5
125.5
160
691
Pro Bowler
.4625
.4991
0
1
Secondary Quarterbacks
Variable
Mean
Std Dev
Minimum
Maximum
Symmetry
97.23
2.3199945
82.4700502 99.6671000
Cap Value
0.92
0.9322515
0.0331000
7.8953000
Plays
57.0
55.5572491
0
199.000000
Attempts
47.6
48.2062154
0
181.000000
Pro Bowler
.1361
.3432255
0
1
FINAL MODEL RESULTS
(FOURTH DOWN)

Model:
 lnSAL
= b0 + b1*PYARDS +
b2*CPASSATT + b3*EXP + b4*EXPSQ +
b5* DRAFT1 + b6*DRAFT2 + b7*VET +
b8*NEWTM + b9*lnOFFSAL + b10*PB +
b11*SYMMETRY + et
(1)

ESTIMATION
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

Variable
PYARDS*
CPASSATT*
EXP*
EXPSQ*
DRAFT1*
DRAFT2*
VET*
NEWTM*
lnOFFSAL**
PB*
SYM**
R-squared
OLS RESULTS
Standard
Coefficient
Error
t-stat
0.00025
0.00003
8.630
0.00013
0.00003
4.880
0.133
0.052
2.570
-0.009
0.003
-2.940
0.809
0.080
10.160
0.614
0.137
4.490
0.456
0.115
3.950
-0.353
0.082
-4.290
0.287
0.120
2.390
0.186
0.069
2.680
0.041
0.017
2.470
0.64
Variable
PYARDS*
Standard
Coefficient
Error
0.00026
0.00003
t-stat
10.240
CPASSATT*
EXP*
EXPSQ*
DRAFT1*
DRAFT2*
VET*
NEWTM*
0.00011
0.169
-0.010
0.902
0.701
0.472
-0.356
0.00003
0.043
0.002
0.078
0.133
0.109
0.072
3.970
3.920
-4.390
11.580
5.280
4.320
-4.920
lnOFFSAL**
PB*
SYM**
0.249
0.201
0.038
0.118
0.072
0.017
2.100
2.780
2.230
NOTEWORTHY IMPLICATIONS
Variables
Primary
Parameter Estimates
Secondary
Parameter Estimates
Symmetry
Black
Black*Symmetry
Draft1
Draft2
Pro Bowler
Experience
*
*
-0.12560
0.71490
*
0.37220
0.04630
0.03313
*
0.16980
2.71000
0.39060
0.43770
0.09830
Experience 2
QB Rating
Change Team
Year
Attempts
-0.01219
0.00435
0.55060
0.04576
0.00234
0.01189
0.00183
0.17300
0.03940
0.00183
*Variable not statistically significant.
FUTURE RESEARCH 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?
