Wednesday, April 30, 2014

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.

Tuesday, April 29, 2014

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.

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

Friday, April 18, 2014

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.

Thursday, April 17, 2014

NBA Payroll and the Playoffs

Last year (November 14) I wrote about being skeptical that payroll and performance are significantly related in the NBAKristi 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.

Wednesday, April 16, 2014

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
1 Tuukka Rask BOS
2 Semyon Varlamov COL
3 Carey Price MTL
4 Ben Bishop TBL
5 Jonathan Bernier TOR
6 Sergei Bobrovsky CBJ
7 Cam Talbot NYR
8 Josh Harding MIN
9 Anton Khudobin CAR
10 Henrik Lundqvist NYR
11 Alex Stalock SJS
12 Martin Jones LAK
13 Jaroslav Halak STL, WSH 1.692
14 Ben Scrivens LAK, EDM 1.677
15 Kari Lehtonen DAL
16 Roberto Luongo VAN, FLA 1.399
17 Cory Schneider NJD
18 Chad Johnson BOS
19 Ryan Miller BUF, STL 1.338
20 Frederik Andersen ANA

Tuesday, April 15, 2014

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  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.

Monday, April 14, 2014

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.

Wednesday, April 2, 2014

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.