Wednesday, July 29, 2015

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
F 0.6844
GK 0.3457
M 0.7108

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

Saturday, July 25, 2015

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.

Base Salary Gini
Guaranteed. Compensation Gini

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.

Friday, July 24, 2015

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
Base Salary Guaranteed Comp.
0.5197 0.5294
0.6064 0.6141
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.

Monday, April 13, 2015

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.

Monday, December 29, 2014

NFL Competitive Balance 2014

Now that the NFL regular season is wrapped up, it is time to take a look at how competitively balanced the NFL was last season.  In order to do this, I am going to look at the NFL season statistically as a sample and as a population - mainly for comparison purposes.  Additionally, I will take a look at the conferences separately in terms of competitive balance.  The data comes from

There are a lot of ways to measure competitive balance, and I have chosen to use a simple measure called the Noll-Scully Competitive balance measure.  By competitive balance we are looking at how well a league's standings are in relation to a league where wins and losses are determined randomly.  Here is a step-by-step guide if you want to calculate this on your own.  As a reference, here is the Noll-Scully using a sample or a population for the NFL from 1981 to 2012.

For the NFL in 2014 the Noll-Scully was 1.562 (population) and 1.587 (sample).  If you click on the link in the last sentence above, you will notice that the NFL's competitive balance for this past season is very similar to recent seasons. 

Since the 2000 NFL season, competitive balance in the NFL has been fairly stable as shown below:



Monday, December 15, 2014

2014 NCAA FBS Top 25 for Week 16

With only one game this past weekend, it may not matter much whether this game is included in the overall dataset, but I thought that I would be through as I have been in the past so I have included the latest rankings after the Army-Navy game.  So using the data from here is the last regular season ranking for 2014.  As you can see the ranking is similar to the previous week's ranking, with Oregon still the most productive team in all of college football.  So here is the final regular season top 25 using the Complex Invasion College Football Production Model.

Rank Team
1 Oregon
2 Marshall
4 Baylor
5 Ohio State
6 Michigan State
7 Alabama
8 Georgia
9 Louisiana Tech
10 Boise State
11 Mississippi State
12 Georgia Southern
13 Georgia Tech
14 Northern Illinois
15 Wisconsin
16 East Carolina
17 Mississippi
18 Memphis
19 Florida State
20 BYU
21 Arizona
22 Cincinnati
23 Oklahoma
24 USC
25 Western Kentucky

Previous 2014 NCAA FBS Top 25 Rankings
2014 NCAA FBS Top 25 for Week 15
2014 NCAA FBS Top 25 for Week 14
2014 NCAA FBS Top 25 for Week 13
2014 NCAA FBS Top 25 for Week 12
2014 NCAA FBS Top 25 for Week 11
2014 NCAA FBS Top 25 for Week 10
2014 NCAA FBS Top 25 for Week 9
2014 NCAA FBS Top 25 for Week 8
2014 NCAA FBS Top 25 for Week 7
2014 NCAA FBS Top 25 for Week 6
2014 NCAA FBS Top 25 for Week 5
2014 NCAA FBS Top 25 for Week 4
2014 NCAA FBS Top 25 for Week 3
2014 NCAA FBS Top 25 for Week 2