NCAA FBS Non-Conference SOS Part II

Yesterday I blogged about the NCAA FBS non-conference strength of schedule (SOS) from last season.  Today I am going to look at how the top five conferences (ACC, Big 10, Big 12, SEC and Pac 12) compared in terms of strength of schedule against other top five conferences during the regular season.  Here I have decided to drop all the independent teams, which dropping Notre Dame may or may not be acceptable.

Note that in terms of the amount of games, since both the Big 12 and the Pac 12 played nine games against conference teams - they each had fewer non-conference games and the Pac 12 had more non-conference games given they have more teams.

As shown in the table below the Big 12 played the fewest number of games against teams in the other four conferences, while the SEC played the most.  In terms of SOS, the SEC, Big 12 and ACC all played against similar schedules and the Pac 12 and Big 10 also played against easier strength of schedules.

Conf	Total NCG	Power 5 NCG	Percentage	SOS
ACC	56	13	0.232	44.231
Big 12	30	5	0.167	43.400
Big 10	48	9	0.188	63.333
Pac 12	37	9	0.243	58.111
SEC	56	14	0.250	42.143

Total NCG = total number of non-conference games played by the conference during the regular season.
Power 5 NCG = total number of ACC, Big 10, Big 12, Pac 12 or SEC games played outside the teams in the conference during the regular season.
Percentage = Power 5 NCG / Total NCG
SOS = average Strength of Schedule of non-conference games against ACC, Big 10, Big 12, Pac 12 or SEC opponents.

NCAA FBS Non Conference Strength of Schedule for 2013

Recently the Southeastern Conference announced a new scheduling requirement for their football teams - to play at least one non-conference game against one ACC, Big 12, Big Ten or Pac-12 football team starting in 2016.  So, let's take a look at last year's non-conference strength of schedule (SOS) for all of the NCAA FBS conferences.

First, here is the overall non-conference strength of schedule for all ten FBS football conferences (and the independent's grouped as a conference) along with the number of non-conference games played against FCS teams for each conference.

Conf	SOS	FCS
IND	66.138	7
MidAmerican	71.192	12
CUSA	71.895	7
MountainWest	77.589	18
SEC	78.375	14
ACC	79.911	16
P12	82.649	10
Big10	83.458	11
AAC	83.650	9
SunBelt	85.450	11
Big12	88.367	8

As you can see the "lower" conferences had a tougher (i.e. lower SOS number) non-conference strength of schedule (except for the Sun Belt conference).  Of the five major conferences, the SEC has a slight tougher non-conference schedule than the ACC, and the Big 12 has the easiest non-conference schedule last season.

Tomorrow I will look at non-conference SOS for the top five conference against other top five conferences.

Here are all the FBS-FCS games from 2013.

Team	Opp	Date	Location
Air Force	Colgate	8/31/2013	Home
Akron	James Madison	9/7/2013	Home
Alabama	Chattanooga	11/23/2013	Home
Arizona	Northern Ariz.	8/30/2013	Home
Arizona St.	Sacramento St.	9/5/2013	Home
Arkansas	Samford	9/7/2013	Home
Arkansas St.	Ark.-Pine Bluff	8/31/2013	Home
Army	Morgan St.	8/30/2013	Home
Auburn	Western Caro.	10/12/2013	Home
Ball St.	Illinois St.	8/29/2013	Home
Baylor	Wofford	8/31/2013	Home
Boise St.	UT Martin	9/7/2013	Home
Boston College	Villanova	8/31/2013	Home
Bowling Green	Murray St.	9/21/2013	Home
Buffalo	Stony Brook	9/14/2013	Home
BYU	Idaho St.	11/16/2013	Home
California	Portland St.	9/7/2013	Home
Central Mich.	New Hampshire	9/7/2013	Home
Cincinnati	Northwestern St.	9/14/2013	Home
Clemson	South Carolina St.	9/7/2013	Home
Clemson	Citadel	11/23/2013	Home
Colorado	Central Ark.	9/7/2013	Home
Colorado	Charlestown So	10/19/2013	Home
Colorado St.	Cal Poly	9/14/2013	Home
Connecticut	Towson	8/29/2013	Home
Duke	N.C. Central	8/31/2013	Home
East Carolina	Old Dominion	8/31/2013	Home
Eastern Mich.	Howard	8/31/2013	Home
FIU	Bethune-Cookman	9/14/2013	Home
Florida	Ga. Southern	11/23/2013	Home
Florida St.	Bethune-Cookman	9/21/2013	Home
Fresno St.	Cal Poly	9/7/2013	Home
Georgia	Appalachian St.	11/9/2013	Home
Georgia St.	Samford	8/30/2013	Home
Georgia St.	Chattanooga	9/7/2013	Home
Georgia St.	Jacksonville St.	9/21/2013	Home
Georgia Tech	Elon	8/31/2013	Home
Georgia Tech	Alabama A&M	11/23/2013	Home
Houston	Southern U.	8/30/2013	Home
Idaho	Old Dominion	11/9/2013	Home
Illinois	Southern Ill.	8/31/2013	Home
Indiana	Indiana St.	8/29/2013	Home
Iowa	Missouri St.	9/7/2013	Home
Iowa St.	UNI	8/31/2013	Home
Kansas	South Dakota	9/7/2013	Home
Kansas St.	North Dakota St.	8/30/2013	Home
Kent St.	Liberty	8/29/2013	Home
Kentucky	Alabama St.	11/2/2013	Home
La.-Lafayette	Nicholls St.	9/14/2013	Home
La.-Monroe	Grambling	9/7/2013	Home
Louisiana Tech	Lamar	9/7/2013	Home
Louisville	Eastern Ky.	9/7/2013	Home
LSU	Furman	10/26/2013	Home
Marshall	Gardner-Webb	9/7/2013	Home
Maryland	Old Dominion	9/7/2013	Home
Massachusetts	Maine	9/7/2013	Home
Memphis	UT Martin	11/9/2013	Home
Miami (FL)	Savannah St.	9/21/2013	Home
Michigan St.	Youngstown St.	9/14/2013	Home
Middle Tenn.	Western Caro.	8/29/2013	Home
Minnesota	Western Ill.	9/14/2013	Home
Mississippi St.	Alcorn	9/7/2013	Home
Missouri	Murray St.	8/31/2013	Home
Navy	Delaware	9/14/2013	Home
Nebraska	South Dakota St.	9/21/2013	Home
Nevada	UC Davis	9/7/2013	Home
New Mexico St.	Abilene Christian	10/26/2013	Home
North Carolina	Old Dominion	11/23/2013	Home
North Carolina St.	Richmond	9/7/2013	Home
Northern Ill.	Eastern Ill.	9/21/2013	Home
Northwestern	Maine	9/21/2013	Home
Ohio	Austin Peay	9/21/2013	Home
Ohio St.	Florida A&M	9/21/2013	Home
Oklahoma St.	Lamar	9/14/2013	Home
Ole Miss	Southeast Mo. St.	9/7/2013	Home
Oregon	Nicholls St.	8/31/2013	Home
Oregon St.	Eastern Wash.	8/31/2013	Home
Pittsburgh	Old Dominion	10/19/2013	Home
Purdue	Indiana St.	9/7/2013	Home
Rutgers	Norfolk St.	9/7/2013	Home
San Diego St.	Eastern Ill.	8/31/2013	Home
San Jose St.	Sacramento St.	8/29/2013	Home
SMU	Montana St.	9/7/2013	Home
South Ala.	Southern Utah	8/29/2013	Home
South Carolina	Coastal Caro.	11/23/2013	Home
South Fla.	McNeese St.	8/31/2013	Home
Syracuse	Wagner	9/14/2013	Home
TCU	Southeastern La.	9/7/2013	Home
Temple	Fordham	9/14/2013	Home
Tennessee	Austin Peay	8/31/2013	Home
Texas A&M	Sam Houston St.	9/7/2013	Home
Texas St.	Praire Vew A&M	9/7/2013	Home
Texas Tech	Stephen F. Austin	9/7/2013	Home
Toledo	Eastern Wash.	9/14/2013	Home
Troy	Savannah St.	9/7/2013	Home
Tulane	Jackson St.	8/29/2013	Home
UAB	Northwestern St.	9/21/2013	Home
UNLV	Western Ill.	9/21/2013	Home
Utah	Weber St.	9/7/2013	Home
Utah St.	Weber St.	9/14/2013	Home
Vanderbilt	Austin Peay	9/7/2013	Home
Virginia	VMI	9/21/2013	Home
Virginia Tech	Western Caro.	9/7/2013	Home
Wake Forest	Presbyterian	8/29/2013	Home
Washington	Idaho St.	9/21/2013	Home
Washington St.	Southern Utah	9/14/2013	Home
West Virginia	William & Mary	8/31/2013	Home
Western Ky.	Morgan St.	9/21/2013	Home
Western Mich.	Nicholls St.	9/7/2013	Home
Wisconsin	Tennessee Tech	9/7/2013	Home
Wyoming	Northern Colo.	9/14/2013	Home

MLB Payroll Visualized

The LA Times has a nice article showing MLB payrolls!

NBA Competitive Balance

Yesterday I blogged about the relationship between pay and performance in the NBA.  Today I want to look at competitive balance in the NBA.  For those interested, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance.

Since the 1999/00 regular season here is the results.  As you can see for the 2013/14 season the NBA's level of competitive balance is very similar.

Season	Noll-Scully
1999-00	2.916
2000-01	2.846
2001-02	2.495
2002-03	2.612
2003-04	2.465
2004-05	2.803
2005-06	2.469
2006-07	2.396
2007-08	3.056
2008-09	3.117
2009-10	2.951
2010-11	2.910
2011-12	2.537
2012-13	2.811
2013-14	2.853

Compared to other leagues (American football, hockey and baseball) the NBA is fairly uncompetitive, with higher Noll-Scully measures than any of those leagues during the same time period.

NBA Payroll and the Playoffs

Last year (November 14) I wrote about being skeptical that payroll and performance are significantly related in the NBA.  Kristi Dosh (ESPN writer) is not skeptical about the relationship between pay and performance in the NBA and predicted that the top 10 payrolls will make the playoffs.  So, how did that work out?

Dosh predicts that the following teams will make the 2013-14 NBA playoffs:  Nets, Knicks, Heat, Bulls, Lakers, Raptors, Clippers, Celtics, Thunder and Pacers and the teams in bold did not actually make the playoffs.  As I have written previously, "I think this would be much more convincing if the top 16 teams in terms of payroll made the playoffs for say ten NBA seasons.  That would give much more credibility to this type of statement.  Since this statement only looks at ten of the possible sixteen playoff spots the prediction is only 62.5% accurate - or leaves 37.5% of the playoffs teams unexplained.  For statisticians this is a large error."  Now the error is compounded with three of the teams in the top ten of payroll not making the playoffs, which is a forecast error of 30%!

As we have written in The Wages of Wins, relative payroll is not a good predictor of team performance in the sense that it does not have a lot of commonality between the two variables.
Here is a look at the statistical relationship between NBA payroll and NBA regular season performance over different time periods.

Since the 2004/05 NBA season (i.e. the last decade) the relationship between relative payroll and regular season performance is statistically significant (95% level), but relative payroll only "explains" 7.2% of the variation in regular season performance (which is statistically called r-squared) adjusted for heteroskedasticity in the data (unequal scatter).  If we look at the adjusted r-squared that falls slightly to 6.9%.

Taking the time period of Mrs. Dosh (2011/12 to now) the statistical relationship between relative payroll and regular season performance is statistically significant (95% level) and the amount that relative payroll is in common with regular season performance is higher with an r-squared of 0.188 and an adjusted r-squared of 0.179.  Still relative payroll misses over 80% of the variation in regular season performance.  I will let you decide if that is a good predictor or not.

Finally, just taking this season into account, now the statistical relationship between relative payroll and regular season team performance is statistically insignificant at the 95% level.  Meaning from a statistical viewpoint that payroll is NOT related to team regular season performance.

NHL Regular Season Goalie Performance for 2013-14

For the past few years I have been looking at NHL goalies and evaluating their regular season performance. So now that the 2013/14 NHL regular season is in the books, let's look at NHL goalie performance using our NHL goalie measure called Wins Above Average.  For those interested in how I calculate this measure, here is a step-by-step guide to measure WAA.  As mentioned in the previous link, I will need a measure of the impact that goals against has on team points, and for the 1995 to 2013 NHL regular seasons, this is equal to -0.340858.

So who is the best NHL goalie for the regular season?  Tuukka Rask, followed very closely by Semyon Varlamov.  Here are the top 20 goalies this season ranked by WAA.

	Player	Team		WAA		SV%
1	Tuukka Rask	BOS		4.521		0.930
2	Semyon Varlamov	COL		4.517		0.927
3	Carey Price	MTL		4.102		0.927
4	Ben Bishop	TBL		3.046		0.924
5	Jonathan Bernier	TOR		2.792		0.923
6	Sergei Bobrovsky	CBJ		2.654		0.923
7	Cam Talbot	NYR		2.593		0.941
8	Josh Harding	MIN		2.254		0.933
9	Anton Khudobin	CAR		2.231		0.926
10	Henrik Lundqvist	NYR		1.902		0.920
11	Alex Stalock	SJS		1.768		0.932
12	Martin Jones	LAK		1.718		0.934
13	Jaroslav Halak	STL, WSH		1.692		0.921
14	Ben Scrivens	LAK, EDM		1.677		0.922
15	Kari Lehtonen	DAL		1.662		0.919
16	Roberto Luongo	VAN, FLA		1.399		0.919
17	Cory Schneider	NJD		1.352		0.921
18	Chad Johnson	BOS		1.347		0.925
19	Ryan Miller	BUF, STL		1.338		0.918
20	Frederik Andersen	ANA		1.223		0.923

NHL Pay and Performance

Yesterday I blogged about competitive balance in the NHL, and today I want to extend that to take a look at pay and performance in the NHL during the regular season.  Recently a new website has emerged which focuses on the payroll cap in the NHL, called capgeek.com.  It is excellent and I know of no better website for salary and team payroll information on the internet.  So, I am going to use their information to estimate the relationship between regular season payroll and regular season performance from the 2009/10 NHL regular season to the 2013/14 regular season.

To do that, I will run a linear regression on relative payroll and team points (team performance measure).  For those interested, here is a step-by-step guide as to how to do this yourself.  You may be wondering why I use relative payroll and why I am focusing on the statistical measure called r-squared.  I answer (or link) to those directly below.

Why relative payroll?  This is for statistical purposes, but simply stated - the average of team payrolls are rising over the time period and the average of team performance is remaining relatively constant.  So if you just take a look at payroll and performance you are comparing a variable that is increasing to one that is constant (or stationary) and you will get a lower statistical relationship that what is truly taking place.  In fact the correlation coefficient (called r) between payroll and performance falls dramatically over this time period as opposed to the coefficient of determination (r-squared).  For this time period, for payroll and performance r = 0.034 while for relative payroll and performance r = 0.235, and thus for payroll and performance r-squared = 0.001 and for relative payroll and performance r-squared = 0.055.

Why do I use r-squared?  I have answered that here.

So, what is the relationship between team relative payroll and team regular season performance?  For the the 2009/10 NHL season to the 2013/14 NHL regular season that I have data from capgeek, relative payroll is statistically significant (at the 99% confidence level) with respect to team points. Yet, in terms of the common variation between the two variables, there is not much with the R-squared = 0.055.  If I include payments of long-term injured reserve and also include bonuses, the r-squared actually decreases.  Thus relative payroll "explains" less than 6% of NHL team regular season performance since 2009/10.

Hence, I conclude that NHL regular season team payrolls are not a good indicator of NHL regular season performance.

NHL Competitive Balance

With the 2013-14 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 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 for the last NHL season.  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. 

Binomial Distribution	Sample	Population
Standard Deviation of Winning Percent =	0.093023	0.091459
Average of Winning Percent =	0.5624	0.5624
Square root of Games Played =	9.055385	9.055385
Noll-Scully Measure of Comp. Balance =	1.497792	1.472617
Trinomial Distribution	Sample	Population
Richardson EEJ 2000
Idealized Standard Deviation=	0.047192	0.047192
Probability of a Tie =	0.269512	0.269512
Number of Games Played =	82	82
Noll-Scully Measure of Comp. Balance =	1.971147	1.938017

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.

LA Angels and Data Analysis

The LA Angels have hired a player data analyst. 

World Cup and Economic Impact

Moody's forecasts that the World Cup will not have a large economic impact.  While there will be lots of tourists generating economic activity, there will also be a decline in economic activity that normally takes place to offset the gains.  Sports economists have found this same result numerous times here in the US.