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.

Major League Soccer and Income Inequality

Over the last few years there has been greater awareness of income inequality, with trends showing that income inequality is rising here in the US.  I have previously looked at income inequality in Major League Soccer as well as Major League Baseball, NCAA Athletic Department Revenue and NCAA football bowl subdivision bowl revenue.  Here is how to calculate the Gini coefficient.  The Gini coefficient is bound between zero and one, with a zero Gini coefficient meaning that income in perfectly equal and a Gini coefficient equal to one meaning that income is perfectly unequal.

Recently the Major League Soccer Players Union has released players salaries, and mirroring this is the degree of income inequality in Major League Soccer.  In fact not only are salaries in MLS more unequal but the degree of salary inequality has been increasing.  I will only be looking at the players salaries for the 2013, 2014 and 2015 seasons.

		Gini Coefficient
Season		Base Salary	Guaranteed Comp.
2013		0.5197	0.5294
2014		0.6064	0.6141
2015		0.6487	0.6492

As you can see in the table above, in both base salary and guaranteed compensation the Gini coefficient has been increasing in MLS, or that MLS salaries are becoming more unequal.

2015 NBA Payroll and Performance

Today I am going to look at NBA payroll and NBA performance.  Over the last few years, NBA payroll has not been a very good predictor of NBA regular season performance.  Let's see if that trend holds up for the last two NBA regular seasons.  Thus I have grabbed data from the internet on NBA payrolls  for 2013/14 regular season and the 2014/15 regular season and NBA performance and run a regression on how well relative payroll is related to regular season performance.

What I find is that over the last two NBA regular seasons is that relative payroll is positive and statistically significant, but only explains about 12.6% of regular season winning percentage.

2014-2015 NBA Competitive Balance

At the end of the regular season last year I blogged about competitive balance in the NBA since the 99/00 season.  Now I will update this given the end of this year's regular season.  For those interested here is a guide to calculating competitive balance using the Noll-Scully method on your own.  The only difference here is that I will give both the sample and population measures of the Noll-Scully for this season.

After downloading the data from basketball reference, I found that the NBA is still highly uncompetitive as compared to other sports leagues, with a Noll-Scully of 2.97 (sample) and 2.92 (population).  Either way, the NBA is still very uncompetitive relative to other sports leagues.

2015 NBA Attendance Analysis

Now that the NBA regular season is in the books, let's take a look at how home attendance has changed (or not changed) over the last two seasons using the NBA attendance numbers from ESPN.  So specifically I want to know if average regular season home attendance has changed from the 2013/14 season to the 2014/15 season statistically.  I choose to use a t-test (assuming unequal variance) in NBA teams average home regular season attendance.  What I find is exactly what I found previously, and that is that NBA average home regular season attendance has not changed in a statistical sense.  In this case, the value of the t-test for NBA average home regular season attendance between the two seasons is 0.215, which is greater than generally accepted confidence level of 0.05.  Hence, I conclude that NBA average home regular season attendance in the 2014/15 season is statistically no different than in the 2013/14 season.

2015 NHL Attendance Analysis

Now that the NHL regular season is over, let's take a look at NHL team attendance and see how fan attendance compares over the last two NHL seasons and see if anything is different from earlier analysis I have done for NHL average home regular season attendance.

First, I downloaded the NHL attendance data from ESPN for the last two NHL regular seasons, sorted the data and then calculated the t-test for the last two NHL regular seasons and found that there is no statistical difference between the home regular season attendance in the two seasons since the partial lockout.

For those curious, the t-test was 0.406 comparing the lockout season and the 2013/14 NHL season using a two sample equal (and unequal) variance measure and the t-test was 0.445 comparing the 2013/14 and 2014/15 NHL season using a two sample equal (and unequal) variance measure.

2014-15 NHL Competitive Balance

With the 2014-15 NHL regular season in the books, let's take a look at how competitively balanced the season was (using the Noll-Scully measure of competitive balance) and compare this with recent seasons.

There are two ways of measuring competitive balance in hockey since unlike baseball or basketball, hockey games can end up tied at the end of regulation.  So I will report both the binomial and the trinomial Noll-Scully measure.  Additionally, there are two ways of reporting both the binomial and trinomial Noll-Scully measure:  one using the standard deviation of a sample and the other using the standard deviation of the population.  Again, I will report both.

Additionally, I will have to compute (for the trinomial distribution) the probability of a tie under equal playing strength.  In the past, I used Richardson's estimate from Stanley Cup playoff games.  In this case I will change and just assume that the probability of games that go into overtime occurs among teams with equal playing strength.  Feel free to quibble with this, as this is simplification of the estimated probability.

OK, with the measurement details noted, here are the Noll-Scully competitive balance numbers for the recent NHL season.  Using the sample the Noll-Scully under the binomial distribution was 1.5634 and under the trinomial distribution was 2.0286.  For the population the Noll-Scully under the binomial distribution was 1.5372 and under the trinomial distribution was 1.9945.

Compared to recent years, this year was less competitive balanced, but overall the level of competitive balance in the NHL is still rather similar to recent historical numbers.