The document discusses several studies that examined the impact of high-salaried designated players in MLS. Some of the key findings include: 1) Spending more money on player salaries did not necessarily lead to better competitive results for MLS teams according to a 2011-2012 study and a 2015 update. 2) Salary inequality among MLS players grew between 2011-2015 according to the Gini coefficient measure, indicating a few players received a larger share of total salaries. 3) High-profile designated players like David Beckham helped increase MLS ticket sales and jersey sales, demonstrating their off-the-field commercial impact for the league.

1. Exploring the
Impact of
High
Salaried
Designated
Players
Coming to
MLS

2. MLS statistics in 2015
• 4 of top 5 paid players enter
• 10 biggest contracts account for
35.5% of salariy distribution
• 5 teams spend over half of
league’s entire spending ($83 or
$160 million)

3. Impact of David Beckham
• Joins from Real Madrid in 2007
• $5.5 mil base salary
– $2.1 mil for rest of LA
• Designated Player Era
• Impact on & off the field

4. MLS Player’s Union
• Annual publishing of
salaries
• Makes analysis of team
and player comparisons
possible
• Purpose of exemplifying
the disparity that still
exists

5. Title
• Text

7. Sonntag & Sommers Study
• Measured salary inequality, competitive results, and
natural log of team salaries
– 2011 & 2012
• Expected: Increased spending led to better results
– 10% more $ = .0373/.0383 more likely to win a game
• Unexpected: Greater salary inequality led to worse
results
– Increase by 1 stand. dev. = .15/.19 less likely to win a game

8. Findings from 2015 Update
• Hypothesis: Increased inequality would lead to a
much lower chance at winning
• Results: No significant relationship in either case
from Sonntag & Sommers
– Neither spending nor equality had an effect on results
– AT LEAST proves that spending more doesn’t lead to
better results

9. Findings from 2015 Update
Descriptive Statistics
Mean Std. Deviation N
WIN .50 .444 680
INSAL 15.903653136 .7040374286 680
GINI .5464847 .13748853 680
Correlations
WIN INSAL GINI
Pearson Correlation WIN 1.000 -.039 -.030
INSAL -.039 1.000 .623
GINI -.030 .623 1.000
Sig. (1-tailed) WIN . .153 .217
INSAL .153 . .000
GINI .217 .000 .
N WIN 680 680 680
INSAL 680 680 680
GINI 680 680 680
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .040
a
.002 -.001 .445

10. Other Findings
• Inequality of salary distribution grew (GINI)
• GINI of 1 = One player makes all $
• GINI of 0 = $ distribution is perfect
– 2011: .460
– 2012: .470
– 2015: .547
• This proves there fewer players take up a much larger %
of total salary than previous seasons

11. Off-the-field DP Impact
• Jersey Sales
• Same 11/20 players were top earners & sold most
jerseys
• Ticket Sales
• Top 5 season attendance
• Top 5 salary caps

12. Implications/Updates
• TAM announcement
– $1.7 million
• Level of players 8-11 needs raised
– Competing abroad
– Perception of league

13. Questions?