MLS Competitive Balance

Now that the 2017 MLS regular season is in the books, I decided to look at MLS competitive balance.  I did this a few years ago, but thought that I would update it.

Using the Noll-Scully measure of competitive balance under the trinomial distribution (since ties are a part of the MLS) I find that competitive balance is higher for the 2017 regular season than the average over the last 18 seasons.  The average Noll-Scully has been fairly stable over the last five years.  The Noll-Scully for the MLS 2017 regular season is 1.383 which is still more competitive than the other "major" US sports leagues.  Here is the Noll Scully since the 2000 regular season.




Want to try to calculate it yourself?  Here is my Noll-Scully competitive balance measure step-by-step guide.  Note:  I used the trinomial distribution.

2015 MLS Position Income Inequality

Today, I want to finish measuring income inequality in MLS by looking at how salaries are distributed by player position.  Taking the data from MLSPU, I have found for each player (except one) their position and then evaluated the level of income inequality by each position.  For players that are listed at multiple positions I have included them for each position.

Last year the position with the most equal salary were goal keepers and the most unequal were forwards.  This season goal keepers are still the most equal, but now midfielders are the most unequal.  Here is the Gini coefficients for this season using MLSPU's data.

Pos	Base Salary		Guaranteed Comp.
D	0.4137		0.4183
F	0.6844		0.6772
GK	0.3457		0.3518
M	0.7108		0.7109

As you can see midfielder are twice as unequal as goal keepers.  Defenders are close to goal keepers and forwards are close to midfielders for the league as a whole.

Prior posts on 2015 MLS Income Inequality:
MLS Team Income Inequality
MLS Overall Income Inequality

2015 MLS Team Income Inequality

Yesterday's I examined income inequality in Major League Soccer for the current season in terms of all players listed on the Major League Soccer Players Union salary release and I noted that income inequality has been increasing during the past three seasons.  Today, I want to look at the level of income inequality at the team level.

So looking at each MLS club here are the measures of income inequality for the 2015 season using the data from the MLSPU in the table below.

Team		Base Salary Gini		Guaranteed. Compensation Gini
CHI		0.564		0.560
CLB		0.342		0.371
COL		0.459		0.463
DAL		0.346		0.337
DC		0.377		0.394
HOU		0.436		0.448
KC		0.460		0.450
LA		0.777		0.774
MTL		0.367		0.368
NE		0.619		0.625
NY		0.419		0.450
NYCFC		0.777		0.772
ORL		0.740		0.739
PHI		0.422		0.420
POR		0.495		0.485
RSL		0.409		0.414
SEA		0.713		0.732
SJ		0.476		0.483
TOR		0.784		0.790
VAN		0.509		0.503

From the table above notice that TOR (Toronto) has the most amount of salary inequality followed closely by LA, NYCFC, ORL and SEA.  In terms of income equality, CLB (Columbus) is the most equal followed by DAL, MTL and DC.

Over the last few seasons here are two charts of team Gini coefficients (excluding NYCFC).  The first chart is team by team Gini coefficients for Base Salary.

The second is team by team Gini coefficients for Guaranteed Compensation.

While there has been some variation in how equal (or unequal) salaries are distributed among MLS clubs, overall many teams have similar levels of Gini coefficients; most likely due to long term contracts for relatively high paid MLS players.

Payroll and Performance in MLS

One of the ideas that we focus on in our book The Wages of Wins is the relationship between team payroll and team performance.  We focus primarily on Major League Baseball as that is the sport that the payroll and performance discussion tends to get the most talk.  What we find is that using team performance (regular season winning percentage) and team payroll (relative payroll when comparing multiple seasons or total payroll for one season) we find that there is a positive relationship between team performance and team payroll.  Additionally, we find that that relationship is statistically significant over multiple seasons, meaning that from a statistical viewpoint the two variables are different from zero.  Each of these we find evidence for.  Yet in terms of the amount that the two variables have in common, we find is less than 20% over most time periods.

So, let's take a look at MLS for just the 2014 regular season.  First let me note that since there are only 19 teams in MLS this season, the number of observations is suspect.  So take the following with a statistical "grain of salt".  Eventually, I will like to come back and analyze this over a much longer time period, but it will take some time for me to get the salary data cleaned up from the pdf's on the internet. 

Thus given I am only looking at the 2014 regular season the statistical results are not very promising.  For the 2014 regular season neither base salary nor guaranteed salary are statistically significant with respect to standings points.  In other words, from a statistically viewpoint either base salary or guaranteed salary do not explain end of regular season standings points.

Competitive Balance in MLS

Now that the Major League Soccer regular season is over let's take a look at competitive balance in MLS for the 2014 season.  Using the Noll-Scully measure of competitive balance under the trinomial distribution (since ties are a part of the MLS) I find that competitive balance is higher for the 2014 regular season than the average over the last 15 seasons.  The Noll-Scully for 2014 is 1.488 which is still more competitive than the other "major" US sports leagues.  Yet the average Noll-Scully has been increasing - meaning that competitive balance is decreasing over the last five years.  Here is the Noll Scully since the 2000 regular season.

Competitive Balance in Major League Soccer

Today let's look at competitive balance in Major League Soccer (MLS).  Major League Soccer re-started in 1996, but for the first few years (1996-1999), there were no ties during the regular season and since 2000 ties are possible during the regular season, so I am truncating the overall data starting with the 2000 regular season.  I will use the Noll-Scully measure of competitive balance to measure competitive balance in Major League Soccer.

The Noll-Scully measure captures how far a particular sport or league’s distribution of wins deviates from a purely random outcome of wins and losses.  Thus a Noll-Scully equal to one would be one that is purely random.  Since the 2000 season, the average Noll-Scully in MLS equals 1.294, which is more competitive than the National Hockey League, the National Football League and the National Basketball Association.

(If you are interested in replicating these results, I got the data from MLS website and here is a step-by-step method to calculate the Noll-Scully measure of competitive balance - where I used the trinomial method under a standard deviation of the population.  I also used the probability of a tie to be the actual yearly average of games that were tied).

Here is a chart of how competitively balanced Major League Soccer has been since 2000.



For those interested in replicating the numbers, here is a table of the results.

Noll-Scully		Season
1.180		2000
1.757		2001
0.719		2002
1.189		2003
0.844		2004
1.834		2005
0.874		2006
1.287		2007
0.943		2008
1.160		2009
1.553		2010
1.396		2011
1.752		2012
1.631		2013