2012-2013 NBA Competitive Balance

Now that the NBA finals are upon us, I thought that I would look back at the NBA regular season and calculate competitive balance using the Noll-Scully measure of competitive balance.  As Dave Berri has written previously and as we wrote in The Wages of Wins, the NBA compared to the NFL, MLB and NHL is rather competitively imbalanced.  During David Stern's first 27 years as NBA commissioner, the NBA averaged a Noll-Scully competitive balance of 2.8 for the seasons 1984-85 to 2010-11.  So looking back at the 2012-2013 NBA regular season, I have calculated the Noll-Scully equal to 2.81 (using the sample standard deviation of winning percent) and 2.76 (using the population standard deviation of winning percent).  In either case the NBA is similar in its measure of competitive balance as it has during the last three decades.

2013 NHL Goalie Performance

This past weekend I heard a number of hockey announcers state that the most important player in the playoffs was the goaltender.  That reminded me that I had not finished the regular season NHL goalie analysis.  For our purposes, the NHL goalie measure analyzes the number of wins above average a NHL goalie produces in a given time period (in this case the lockout shortened season). So here are the top 10 NHL goalies using our Wins Above Average measure of an NHL goalie.  The updated coefficient on goals against is -0.341814 over the seasons from 1995/96 to 2013.  Using that measure of the impact of goals against on standings points, yields the following top 20 NHL goalies during the 2013 regular season.

	Player	Team		WAA		SV%
1	Sergei Bobrovsky	CBJ		3.720		0.932
2	Craig Anderson	OTT		3.364		0.941
3	Henrik Lundqvist	NYR		2.863		0.926
4	Tuukka Rask	BOS		2.860		0.929
5	Antti Niemi	SJS		2.518		0.924
6	Cory Schneider	VAN		2.152		0.927
7	Jimmy Howard	DET		2.137		0.923
8	James Reimer	TOR		2.054		0.924
9	Corey Crawford	CHI		1.850		0.926
10	Robin Lehner	OTT		1.745		0.936
11	Devan Dubnyk	EDM		1.563		0.920
12	Braden Holtby	WSH		1.550		0.920
13	Viktor Fasth	ANA		1.025		0.921
14	Ben Bishop	OTT, TBL		0.968		0.920
15	Ray Emery	CHI		0.792		0.922
16	Jason LaBarbera	PHX		0.791		0.923
17	Chad Johnson	PHX		0.777		0.954
18	Kari Lehtonen	DAL		0.732		0.916
19	Nikolai Khabibulin	EDM		0.712		0.923
20	Ryan Miller	BUF		0.668		0.915

NHL Attendance Analysis

Now that the NHL regular season is over, let's take a look at NHL team attendance and see what effect (if any) the NHL lockout had on fan attendance as compared to the past few years.

I grabbed the NHL attendance data from ESPN from the 2000/2001 season to the current 2012/2013 NHL regular season, sorted the data and then ran a t-test (much like I did after the recent NBA lockout).  Here is the average home attendance from 2005-2006 to 2012 (or actually 2013) NHL regular seasons.  The pink line is the most recent NHL regular season and notice that even thought there were 17 fewer home games the average attendance for the teams is very similar to the average home regular season attendance in the previous non-lockout regular seasons.  In other words, there is not much change in attendance from 2011-2012 to this season.

 

Using more formal statistical analysis, I 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 2011-2012 and 2013 seasons.  For those curious, the t-test was  0.318 using a two sample equal (and unequal) variance measure.

2013 NHL Pay and Performance

Today let's take a look at how well NHL (relative) team payroll and team regular season performance relate to each other.  I am interested in how payroll and team performance related for both just this (lock-out shortened) season and over a longer period of time, so I will present both below.

For NHL team payroll data, I usually use USA Today's payroll database, but USA Today does not have that data for the 2013 NHL season, so I found NHL team compensation data at the National Hockey Leagues Players Association (NHLPA) website, which I assume is very accurate.  Note this is total compensation, not compensation against the payroll (or salary) cap.  The NHLPA notes that their measure of team compensation "is comprised of base salary plus signing bonus for the current season".  Prior data comes from USA Today's NHL salary database, so there may be some differences in the two.  For NHL team performance data, I used the 2013 regular season standings data reported on  ESPN's website.  Prior regular seasons standings data come ESPN as well.  Here is the step-by-step details of how I calculated the relative payroll - team performance relationship.

So, for just the 2013 NHL season, running the numbers I find that the relationship between relative payroll and regular season performance is positive and statistically significant and results in that relative payroll "explains" 29.7% of NHL regular season performance, which is rather high as compared to the NFL.  Again by "explain" I mean that the amount of variation in relative payroll that is related to the amount of variation in regular season performance.  Even so that still leaves 70% of regular season performance not explained by payroll.  I will leave it to you to decide if that is a lot or a little.

Over a longer time period (2000-2001 to 2013) seasons (without the 2004-2005 cancelled season) we see that the relationship between relative payroll and regular season performance in the NHL is positive and statistically significant.  Relative payroll "explains" about 24.8% of NHL regular season performance, which is very similar to prior estimates, resulting in that relative payroll does not explain over three quarters of team performance.  As we contend in The Wages of Wins, the argument that team payrolls determine team regular season performance does not seem to be as big as some claim.

2013 NHL Competitive Balance

Now that the NHL regular season has finished, let's take a look at how competitive balanced the NHL has been this season.  I have written about NHL competitive balance last year, and concluded using the Noll-Scully measure of competitive balance concluded that the NHL was fairly balanced last season.  So what about this lock-out shortened season?  Does competitive balance change all that much with fewer games played?  The quick answer is no, let's see how using the Noll-Scully competitive balance measure.

As I mentioned 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 probabilty 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.  For transparency, I will also report for each season this probability estimate.

OK, with the measurement details noted, here are the Noll-Scully competitive balance numbers over the last few NHL seasons.  The first table used the binomial measure and the second table uses the trinomial measure.    The first column of numbers uses a sample standard deviation and the second column uses the population standard deviation.  The probability of a game ending in regulation tied is given in the far right of the second table.

Binomial	STD  Sample	STD Population
	Noll-Scully	Noll-Scully
2013	1.2503	1.2293
2011-2012	1.1380	1.1175
2010-2011	1.3065	1.2838
2009-2010	1.2081	1.5284
2008-2009	1.3690	1.3459
2007-2008	0.8845	0.8666
2006-2007	1.5990	1.5722
Trinomial	STD  Sample	STD Population	Probability
	Noll-Scully	Noll-Scully
2013	1.5800	1.5535	0.2250
2011-2012	1.4528	1.4267	0.2448
2010-2011	1.6704	1.6414	0.2405
2009-2010	1.5284	1.4998	0.2421
2008-2009	1.7381	1.7089	0.2293
2007-2008	1.1031	1.0808	0.2234
2006-2007	2.0281	1.9940	0.2285

While the NHL was slightly less competitively balanced than the previous season, there is not much difference over the last few NHL regular seasons, and I would conclude that the NHL lock-out shortened regular season was not significantly different in terms of competitive balance as compared to other NHL regular seasons since 2006-2007.

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%.