NCAA Bowl Championship Series Revenue Distribution Inequality

A few weeks ago I blogged about NCAA football bowl subdivision bowl revenue inequality for the teams that play in NCAA bowls.  Here I want to look at how equal (or as the title of the blog foreshadows - how unequal) the NCAA distributes Bowl Championship Series revenue back to the conference or teams in the NCAA.  To do this, I am using the data directly from the NCAA for the 2006-07 academic year to the 2010-11 academic year and also the data from the 2004-05 to 2008-09 for the 2004-05 and 2005-06 years not covered in the proceeding link.

As you look over those two .pdf files linked from the NCAA's website in the previous sentence, you will notice that some non-BCS conferences received BCS money, and that some individual schools receive BCS money (such as independents like Notre Dame, Army and Navy).  This presents some questions as to how to calculate the Gini coefficient (measure of income inequality or in this case revenue distribution inequality).  Should I include all who have received BCS money (both BCS conferences, non-BCS conferences and individual teams)?  If so, then this might give a biased picture, since individual teams and non-BCS conferences will normally receive much less than an entire conference leading to a higher level of income inequality that in reality.  So how should I account for this problem?  I decided that I would also calculate the Gini coefficient two additional ways.  One is that I will include all non-BCS conference and all individual teams as one category as a measure of revenue distribution inequality, and the other is that I will only include the BCS conferences and BCS independent teams - with the independent teams aggregated into one category, much as I do for the NCAA FBS Production Model.

Here are the measures of BCS revenue distribution inequality from 2004-05 to 2010-11.

		Gini 1		Gini 2		Gini 3
2004-05		0.614		0.400		0.360
2005-06		0.623		0.394		0.400
2006-07		0.663		0.376		0.384
2007-08		0.661		0.395		0.407
2008-09		0.662		0.396		0.400
2009-10		0.651		0.374		0.379
2010-11		0.675		0.393		0.406

As you can see including both teams and BCS and non-BCS conferences (Gini 1) has a much higher level of BCS revenue distribution inequality than if I aggregate all the teams and non-BCS conferences into one group (Gini 2).  There is not much of a difference between only BCS members when aggregating the independents into one group (Gini 3) with BCS revenue distribution measure Gini 2.

Either way, there is some BCS revenue distribution inequality, it is lower than what I found among the 70 participants in my blog about the NCAA bowl inequality - linked at the beginning of this blog.

Gary Bettman's 20th Year as NHL Commissioner

Scott Burnside has a nice piece on the 20th year anniversary of Gary Bettman taking over as NHL Commissioner.  (Some comments by yours truly in the middle).

Here are some more of the financial growth details not included in the article:

From 1994 to 2012 NHL average franchise values have increased at a compound annual growth rate of 8.85%.

From 1994 to 2011 (latest data I have) NHL average team total revenues have increased at a compound annual growth rate of 7.23%; average player costs have increased at a compound annual growth rate of 9.28%; and average team operating income has increased at a compound annual growth rate of 0.58%.

Finally, from 1995 to 2012 NHL average ticket prices have increased at a compound annual growth rate of 3.23% and the average of the Fan Cost Index (provided by Team Marketing Report) has increased at a compound annual growth rate of 3.10%.

2012-2013 NCAA FBS Conference Strength of Schedule

Last year I wrote about NCAA FBS Conference strength of schedule (SOS), so I thought that I would continue that for this year.  Now that all the NCAA bowl games have been completed - and I have some free time to work on this - here is the NCAA FBS Conference SOS's for the 2012-2013 season (including bowl games).

As a reminder, here is how I measure a team's strength of schedule for a season, (with an adjustment of including four new teams this year, making the total of 124 NCAA FBS schools, instead of 120 as when I originally wrote the blog post).  For conference strength of schedule, I take each team's individual strength of schedule within one conference and average of all of those strength of schedules to find the conference strength of schedule.  For example, let's suppose the four independent's were a "conference", then for Army, BYU, Navy and Notre Dame I am taking the average of each of the four schools individual strength of schedule measures to calculate their conference SOS.

The "league" as a whole has an average strength of schedule of 65.53, and a standard deviation of 9.55. The reason that the "league" average SOS is it not equal to 62.50 - which would be the average of 1 through 124, since there are currently 124 NCAA FBS schools - is because NCAA FBS teams play teams in the football championship subdivision (FCS) and I do not have a model to rank them, so each FCS school is given a rank of #125 for the season. Given the number of FCS schools on FBS teams schedule, the average for all FBS schools increases to 63.53 from 62.50, which is not that much of a difference.

Here are the results - with the SEC having the most difficult strength of schedule for the 2012-13 NCAA FBS season.

Conference	SOS
SEC	55.53
Big12	57.29
Pac 12	59.32
Big10	61.99
Big East	62.70
Ind	64.89
ACC	65.47
CUSA	70.56
Sun Belt	72.49
MidAmerican	72.88
WAC	73.38
Mountain West	73.97

Chip Kelly Leaves for the Philadelphia Eagles

Chip Kelly has accepted the head coaching job for the Philadelphia Eagles.  Here is a look at the Oregon Ducks during Kelly's tenure as head football coach.

Below is a chart of offense, defense and total production of the University of Oregon football program during Kelly's tenure as head football coach, along with who would be the lowest ranked team during this time period (in purple) and the average team (sky blue).  As you can see below, Oregon was extremely productive during each of the four seasons as head football coach.  All rankings in this blog come from my Complex Invasion College Football Production Model.  More details about the program under Kelly are after the chart below, including a link to his employment contract are below.

Chip Kelly [2009 - 2012]

2009
The Ducks finished the regular season at 10-2 and bowl eligible, where they lost to #9 ranked Ohio State in the Rose Bowl by a score of (17-26) to finish the season overall at 10-3.  Oregon played against a "tougher" strength of schedule (SOS) as compared to the "league" average SOS, meaning that the Ducks SOS was between one and two standard deviations below the "league" average SOS.  The Ducks best game again was their victory over #23 ranked Utah (31-24) and their worst loss was to #48 ranked Stanford (42-51).  Oregon had the #15 ranked team in total production with the #15 ranked offense and the #31 ranked defense from the Complex Invasion College Football Production Model.

2010
Oregon finished the regular season undefeated at 12-0 and played in the BCS Championship game against #9 ranked Auburn, losing by a score of (19-22) to finish 12-1 overall.  The Ducks played against an "average" strength of schedule (SOS) as compared to the "league" average SOS, meaning that their SOS was plus or minus one standard deviation of the "league" average SOS.  The Ducks best game was their victory over #11 ranked Stanford (52-31).  Oregon had the #3 ranked team in total production with the #1 ranked offense and the #17 ranked defense from the Complex Invasion College Football Production Model.

2011
The Ducks finished the regular season at 11-2 going back to the Rose Bowl and defeated #2 ranked Wisconsin Badgers (45-38).  Oregon again played against an "average" strength of schedule (SOS) as compared to the "league" average SOS.  The Ducks best game was again their victory over #7 ranked Stanford (53-30) and their worst loss was to #27 ranked Southern California (35-38).  Oregon had the #8 ranked team in total production with the #5 ranked offense and the #33 ranked defense from the Complex Invasion College Football Production Model.

2012
Oregon finished the regular season at 11-1 and were bowl eligible and defeated #17 ranked Kansas State (35-17) to finish overall at 12-1.  The Ducks again played against an "average" strength of schedule (SOS) as compared to the "league" average SOS.  The Ducks best game again was their victory over #11 ranked Fresno State Bulldogs by a score of (42-25) and their only loss was to #28 ranked Stanford (14-17).  Oregon had the #3 ranked team in total production with the #1 ranked offense and the #25 ranked defense from the Complex Invasion College Football Production Model.

Iowa Athletic Department Makes a Loss

The Iowa City Press Citizen has an article about the athletic department making a loss.  (At the end there are a few quotes from yours truly).

2012 NFL Pay and Performance

Now that I have some free time, I thought that I would also look at the relationship between payroll and performance in the NFL after having done the competitive balance calculation.  Here is a step-by-step guide to doing this type of analysis using MLB as an example.  So just for the 2012 NFL season (which is admittedly a rather small sample) I am going to estimate the relationship between payroll and performance.  Historically the relationship between payroll and performance in the NFL has been rather small over long periods of time, so we should not expect much of an effect for just one season.

After collecting the data for winning percentage in the 2012 NFL regular season and for payroll for the 2012 regular season I ran the following regression:  winning percent = f(payroll).  There is no need to adjust for relative payroll if only using one season as the average payroll for all the teams is the same for just one season - again this is a small sample size for this type of analysis.

The regression (ordinary least squares) results show that for the 2012 season, payroll has no statistically significant effect on wining percentage.  In other words the effect of team payroll on team performance in the 2012 NFL regular season was statistically zero, as the t test was less than two in absolute value.

A BCS Model has Notre Dame Number 1

The USA Today has a story that one of the six BCS computer models (Colley Matrix) has Notre Dame as the number 1 team after their loss to Alabama.  My NCAA FBS Production Model has Notre Dame as the #9 most productive team in the nation at the end of the bowl season.

In the past my model has disagreed with the final ranking - say last season when the model had Wisconsin as the most productive team (with three losses) and not Alabama.  This to me is less of a concern for my model since I am interested in ranking teams based on their on-field production as measured by the marginal value of various on field statistics.  Hence a "computer model" can incorporate a number of factors that the human cannot or has an implicit bias as I blogged about over at the wagesofwins.

In an of itself, one model ranking Notre Dame as #1 does not mean that computer or statistical models are invalid, just like one human voting that is contrary to conventional wisdom means that humans should not rank NCAA FBS teams.  Rather, my argument is that there should be more computer NCAA FBS ranking models which would give a more accurate picture of how computer models rank team in the aggregate - as opposed to only six.