Friday, August 28, 2015

University of Illinois Dismisses Head Football Coach Tim Beckman

FootballScoop reports that the University of Illinois has dismissed head football coach Tim Beckman with cause before the 2015 NCAA FBS season.  Bill Cubit will be the interim head coach for the season and here is my analysis of Western Michigan under Cubit's last five seasons as head football coach.

So let's take a look at Illinois using the Complex Invasion College Football Production Model before and during Beckman's tenure.  I will be starting with the 2008 season and move forward through 2014.

Below is a chart of the Illinois Illini offense, defense and total production rankings since 2008 along with the rank of the worst team in the Football Bowl Subdivision during the time period.   More information from the model is presented below.

 
 
2008
Unfortunately, I do not have all the data to run the model before the 2008 season, so I am unable to show how Illinois performed in the previous Rose Bowl season.  As such, I can only evaluate based on the data that I have and the model that I am using is the same since the 2008 season.  That said, Illinois finished the 2008 season at 5-7 and bowl ineligible and played against an "average" strength of schedule (SOS) as compared to the "league" average, meaning that their actual SOS was within one standard deviation of the "league" average SOS.  The Illini's best game was a victory over #9 ranked Iowa and their worst loss was to #75 ranked Minnesota.  Overall, the Illini's had the #49 most productive college football team with the #45 ranked offense and the #54 ranked defense.

2009
Statistically this was Ron Zook's worst team during this time period (but better than Beckman's first season as I detail below).  Illinois finished the regular season at 3-9 and played against an "average" strength of schedule (SOS) as compared to the "league" average.  The Illini's best game was a victory over #79 ranked Michigan and their worst loss was to #89 ranked Indiana.  Overall, the Illini's had the #95 ranked team in terms of total production with the #84 ranked offense and the #92 ranked defense.

2010
Illinois significantly turned their production around in this season although the wins and losses do not reflect the relative productivity of the team in this season.  Illinois finished at 7-6 while playing against a "average" strength of schedule (SOS) as compared to the "league" average.  The Illini's best game was a victory over #8 ranked Northern Illinois and their worst loss was to #89 ranked Minnesota.  Illinois had the #18 ranked team in overall productivity with the #35 ranked offense and the #11 ranked defense.

2011
In Zook's last season as head football coach the Illini finished at 7-6.   Illinois played against an "average" strength of schedule (SOS) as compared to the "league" average.  The Illini's best game was a victory over #25 ranked Arkansas State and their worst loss was to #101 ranked Minnesota.  The Illini's had the #61 ranked team (just on the other side of below average) with the #91 ranked offense and the #31 ranked defense.

2012
Tim Beckman took over for Ron Zook this season and while Illinois played against an "average" strength of schedule (SOS) as compared to the "league" average, meaning that their actual SOS was within one standard deviation of the "league" average SOS.  Illinois finished the regular season at 2-10 and their worst winning percentage during this time period.  The Illini's best game was a victory over #65 ranked Western Michigan and their worst loss was to #103 ranked Indiana.  Overall, the Illini's had the #115 ranked team with the #118 ranked offense and the #91 ranked defense.  Clearly, a year to forget.

2013
In Beckman's second season as head football coach the Illini finished the regular season at 4-8 and were bowl ineligible.   Illinois played against a "tougher" strength of schedule (SOS) as compared to the "league" average, meaning that their actual SOS was between one and two standard deviations lower than the "league" average SOS.  The Illini's best game was a victory over #25 ranked Cincinnati and their worst loss was to #77 ranked Indiana.  Overall, the Illini had the #97 ranked team with the #63 ranked offense and the #117 ranked defense.

2014
In what turns out to be Beckman's last season as head football coach the Illini finished the regular season at 6-6 and lost to Louisana Tech in the bowl game.  Illinois again played against a "tougher" strength of schedule (SOS) as compared to the "league" average.  This season the Illini's best game was a victory over #45 ranked Minnesota and their worst loss was to #110 ranked Purdue.  Overall, the Illini had the #93 ranked team with the #76 ranked offense and the #111 ranked defense.

Thus Illinois has performed under Beckman worse on average than Zook's worst team during this time period.

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
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

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.

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.

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
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.

Monday, April 20, 2015

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.

Saturday, April 18, 2015

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.