Miami of Ohio Fires Don Treadwell

ESPN reports that Miami of Ohio has fired head football coach Don Treadwell and offensive coordinator John Klacik, after an 0-5 start of the season.  Miami of Ohio is the third university this season to fire their head football coach, with USC first letting Lane Kiffin go and then UConn relieving Paul Pasqualoni of their head coaching duties.

As I did for last week's coaching causalties, let's take a look at the Miami (OH) RedHawks under former head football coach Don Treadwell's tenure (2011-2013) and also take a look at the RedHawks during the previous three seasons under head coach Michael Haywood during the 2009 and 2010 seasons and Shane Montgomery during his last season as head football coach in the 2008 season.  For a quick look at the Redhawks, take a look at the graph below.  A more detailed analysis of the Miami (OH) Redhawks follows below using the Complex Invasion College Football Production Model.

2013
At the end of last weekend Miami of Ohio was 0-5 with their worst loss to #116 ranked Central Michigan.  Miami (OH) was ranked as the #121 team in overall productivity (out of 125) with the #121 ranked offense and the #116 ranked defense.  The Redhawks played against a strength of schedule (SOS) of 58.00 which was "tougher" than the average schedule to date for the "league".  Again, a teams SOS is "tougher" if it is lower than between one and two standard deviations from the average "league" SOS, which is currently the case.  Given the Redhawks poor productivity, this seems to be a large reason why Miami of Ohio administrator's let Don Treadwell go.

2012
In 2012, the Redhawk's finished again at 4-8 and was the #114 ranked team in terms of overall productivity with the #103 ranked offense and the #108 ranked defense against an average SOS of 63.08.  The Redhawks best game was a 23-20 home win over #54 ranked Ohio and their worst performance was a 16-30 loss to #83 Central Michigan.

2011
In Don Treadwell's first season as head football coach the Redhawks finished 4-8.  Miami (OH) was the #75 ranked most productive team overall, with the #78 ranked offense and the #65 ranked defense.  Miami of Ohio best victory was a 35-28 victory over #77 ranked Army and the Redhawk's worst performance was a 23-29 loss to #104 ranked Minnesota Gophers against an average SOS of 62.33 as compared to the "league" SOS of 64.29.

2010
In what was coach Haywood's final season at Miami (OH), the Redhawks finished the regular season at 8-4, and won the MidAmerican Conference championship game by defeating #8 ranked Northern Illinois and then defeated the #74 ranked Middle Tennessee State Blue Raiders in the Go Daddy Bowl to finish at 10-4 overall.  The Redhawks were the #54 ranked team in terms of overall productivity; with the #61 ranked offense and the #44 ranked defense.  The Redhawks played against a SOS of 71.21, which is average.

2009
In Michael Haywood's inaugural season at Miami (OH) they finished the season at 1-11.  Miami of Ohio only win was over #96 ranked Toledo and of their eleven losses the worst was to #93 Western Michigan all against a SOS that average.  In terms of overall production, Miami of Ohio was the #121 ranked team with the #119 ranked offense and the #117 ranked defense.  All in all, a season to forget.

2008
In the last year with head coach Shane Montgomery, Miami of Ohio finished their season at 2-10.  The Redhawks was the #112 ranked team in overall productivity with the #105 ranked offense and the #100 ranked defense, while playing against an average SOS as compared to the league.  The Redhawks best performance was against $53 ranked Bowling Green and their worst loss was to #101 ranked Toledo.

2013 NCAA FBS Top 25 Ranking for Week 6

Here is the latest NCAA FBS Production Model Top 25 ranking for the end of Week #6.  Baylor still remains the most productive team in NCAA FBS with Oregon, Louisville and Florida State following close behind.  Some of the biggest changes is the addition of Alabama as the #8 ranked team in all of FBS, up from #31 ranked team overall last week and is now the most productive SEC team in all of NCAA FBS.  Likewise last week Maryland was ranked #9 and is now out of the top 25 given the Terps were dominated by Florida State.  Likewise Florida has now entered the Top 25 based on the Gators strong defense.  Rankings are based on the Complex Invasion College Football Production Model.

Rank	Team
1	Baylor
2	Oregon
3	Louisville
4	Florida State
5	Miami (Florida)
6	Wisconsin
7	Washington
8	Alabama
9	UCLA
10	Ohio State
11	Clemson
12	Texas Tech
13	Missouri
14	LSU
15	Oklahoma State
16	Oklahoma
17	Arizona
18	Florida
19	Cincinnati
20	Houston
21	Michigan State
22	Marshall
23	UCF
24	Utah State
25	Michigan

The rankings are based on regression analysis using the data posted at College Football Statistics.

Previous Top 25 Ranks for 2013
2013 NCAA FBS Top 25 Ranking for Week 5
2013 NCAA FBS Top 25 Ranking for Week 4
2013 NCAA FBS Top 25 Ranking for Week 3
2013 NCAA FBS Top 25 Ranking for Week 2

College Football Revenue Growth

One of the topics that I teach in my First Year Seminar for freshman about College Football Economics is with regard to college football revenues.  Here is a nice article on the top schools in terms of football revenues.  Be mindful that this is a limited sample of schools, so the conclusions from the article are for this limited sample - a broader view will be different from only looking at the top revenue generators.

I hope to come back to this later and give a more detailed analysis of the revenues and expenses in college athletics (and hopefully for just college football).

Strength of Schedule Revisited - Part II

Earlier this week I had a comment about why strength of schedule might be statistically insignificant, that being I measure all non-FBS schools as a 126 (for this season since there are 125 FBS schools) in the strength of schedule calculation.  So, what I did was take a look at last year's (2012) NCAA FBS data and delete out all the games played by FBS schools against FCS schools and then re-ran the regression controlling for heteroskedasticity (as I have done previously).  I plan on re-doing this at the end of the regular season this year and after the post-season (bowl games).

What I found was that strength of schedule is still statistically insignificant (i.e. t-Statistic) is less than two in absolute value, meaning that I am not at least 95% confident that strength of schedule (SOS) is different from zero in a statistic sense.  In fact I am not even 20% confident that SOS is different from zero statistically.  Given the estimated coefficient on SOS is also almost zero, even if it was significant (and it is not) the amount of impact that it has on winning percent is nearly nothing anyway.

Also note that the regression performs well in terms of R-squared and Adjusted R-Squared, and the probability of the F-Statistic is also very low indicating that all the independent variables are not jointly equal to zero in a statistical sense.

Here is the regression results for the 2012 NCAA FBS season run using E-Views.

Dependent Variable: WINPCT
Method: Least Squares
Sample: 1 124
Included observations: 124
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.441	0.059	7.525	0.000
PF	0.002	0.000	20.137	0.000
PA	-0.002	0.000	-14.501	0.000
SOS	0.000	0.001	0.204	0.839
R-squared	0.857
Adjusted R-squared	0.854
S.E. of regression	0.095
Sum squared resid	1.089
Log likelihood	117.598
Durbin-Watson stat	1.860
Mean dependent var	0.491
S.D. dependent var	0.249
Akaike info criterion	-1.832
Schwarz criterion	-1.741
F-statistic	240.316
Prob(F-statistic)	0.000

Connecticut Under Head Football Coach Pasqualoni

The Hartford Courant reports that University of Connecticut head football coach Paul Pasqualoni has been fired.  Yesterday I blogged about USC and the firing of their head coach Lane Kiffin.  Today, let's take a look at the University of Connecticut under both Paul Pasqualoni (2011-2013) and the last three years (2008-2010) under head coach Randy Edsall.

Below is a chart of the team's overall productivity ranking, their offensive production ranking and their defensive production ranking from 2008 to last week using the Complex Invasion College Football Production Model.  As you can see, Connecticut has been declining over the last five seasons.  In the three seasons under Pasqualoni the team has been below average in each season overall.  The bright spot was the defensive performance by UConn last season.  If you are interested, I have a more detailed analysis of UConn's football performance below using the Complex Invasion College Football Production Model.

2013
As of last weekend, the Huskies were 0-4 playing against an average strength of schedule (SOS) of 71.50 as compared to the "league" as a whole, SOS = 75.68.  [I define all teams that are within one standard deviation of the league SOS as average].  UConn is the #104 most productive team overall with the #119 most productive offense and the #60 most productive defense.  Given Connecticut's decline the university decided to fire their head coach.  What will be interesting to see is if this change fundamentally changes the Huskie's performance.  I doubt there will be any real change this season; but this question will best be answered in a few years - in which I plan to come back to and analyze.

2012
The Connecticut Huskies played against a SOS = 61.50, which was average as compared to the league strength of schedule of 65.53.  In the wins/loss column, UConn finished the regular season again at 5-7, with their best win (win over highest ranked opponent using the NCAA FBS Production Model) was a 23-20 victory over Big East rival #27 ranked Louisville, and their worse loss was (14-17) to #104 ranked Temple.  Overall, the Huskies were ranked as the #70 team in overall production, with the #108 ranked offense and #12 ranked defense.  The team performed slightly better overall than in the previous season mainly due to the jump in their defensive productivity.

2011
Paul Pasqualoni's first year as the head coach for the UConn Huskies, the Huskies finished the regular season at 5-7.  Connecticut finished the season as the #79 most productive team using the NCAA FBS Production model, with the #101 most productive offense and the #56 most productive defense.  UConn played an average SOS = 62.75 compared to the average for the "league" of 64.29.  For this season, UConn's best win was a 16-10 decision over #30 ranked Big East rival #30 Southern Florida and their worst loss (20-24) was to #112 ranked Iowa State.

2010
In Randy Edsall's last year as the head coach for the Huskies, UConn finished the regular season at 8-4, and again finished at 8-5 with a bowl loss to #12 Oklahoma.  Connecticut finished as the #53 most productive team with the #62 most productive offense and the #41 most productive defense, which is similar in terms of productivity as compared to the 2009 season.  The Huskies played against an average SOS of 62.92 compared to the league average of 63.10.  The Huskies best win was a 16-13 victory over #15 ranked West Virginia and their worst loss was again to the #93 ranked Rutgers 24-27.

2009
Connecticut finished again at 8-5, including a 20-7 bowl victory over #57 ranked South Carolina.  The Huskies played against a SOS of 56.69 which was average as compare to the "league" as a whole.  Connecticut was the #46 most productive team overall with the #48 most productive offense and the #48 most productive defense, all less productive than the previous year.  UConn's best win was a 23-16 victory over #38 Ohio and their worst loss (10-12) was again to the University of North Carolina who finished ranked at #33 in the Football Bowl Subdivision.

2008
For the first year in which I have a complete set of data to use for the NCAA FBS Production Model, UConn finished 8-5, including a bowl victory over #61 ranked Buffalo.  Connecticut was the #26 ranked team in the nation in terms of overall on-field productivity, with the #36 ranked offense and the #15 ranked defense in the "league".  The Huskies best win was over #31 ranked Cincinnati (40-16) and their worst loss was to #47 ranked North Carolina.  UConn played against an average SOS = 59.31 as compared to a 62.92 SOS for the "league" as a whole.

Kiffin Out at USC

This past weekend Lane Kiffin was fired as head football coach of the University of Southern California Trojans even with a won/loss record at USC of 28-15 during his tenure.  Let's take a look at the Trojans under Kiffin using the Complex Invasion College Football Production Model starting with this season, and also look at last two seasons that Pete Carroll was at USC (2008 and 2009) before former head coach Kiffin's arrival for the 2010 season.

In the chart below are the total production ranking, offensive production ranking and defensive production ranking for USC from 2008 to week #5 of 2013 (Kiffin's last game at the helm of the Trojans).  What stands out to me is that USC's defense has been less productive since Kiffin's arrival than in the last two seasons under Carroll.  The season by season data is given below where all the analysis is derived from the Complex Invasion College Football Production Model.

2013
As of last weekend, USC was 3-2 playing against an average strength of schedule (SOS) as compared to the "league" as a whole.  [I define all teams that are within one standard deviation of the league SOS as average].  USC is the #51 most productive team overall with the #46 most productive offense and the #56 most productive defense.  Given that USC has not been in the top 10 in any category since 2008 and my guess is that current USC administrators did not feel that the program was moving in that direction, USC decided to fire their head coach.  What will be interesting to see is if this change fundamentally changes USC's performance.  I doubt there will be any real change this season; but this question will best be answered in a few years.

2012
The USC Trojans played against a SOS = 55.69, which was "tougher" than the league strength of schedule of 65.53.  In the wins/loss column, USC finished the regular season at 7-5, with their best win (win over highest ranked opponent using the NCAA FBS Production model) was a 38-17 victory over Pac 12 rival #24 ranked Arizona State - avenging last years defeat, and their worst loss was to Pac 12 rival #61 ranked Arizona (36-39).  USC subsequently lost to #50 ranked Georgia Tech (7-21) in the Sun Bowl.  Not helping the situation, the model predicted that USC was the better team. The Trojans finished the 2012 season at 7-6.  Overall, USC was ranked as the #46 team in overall production, with both the #42 ranked offense and defense for that season.  While a ranking of 46th overall is above average - it is not in that "elite" status where the Trojans are competing for the Pac 12 conference title and a "national championship" or Rose Bowl berth.

2011
The Trojans finished the regular season at 10-2 (bowl ineligible due to NCAA sanctions).  USC finished the season as the #27 most productive team using the NCAA FBS Production model, with the #16 most productive offense and the #47 most productive defense.  USC played an average SOS = 63.75 compared to the average for the "league" of 64.29.  For this season, USC's best win was a 38-35 decision over #8 ranked Pac12 rival #8 ranked Oregon Ducks and their worst loss (22-43) was to Pac 12 rival #63 ranked Arizona State.  This was by far Kiffen's best team (in terms of production) during Kiffen's tenure at USC.

2010
Lane Kiffin's first year as the head coach for the USC Trojans, the Trojans finished the regular season at 8-5 (bowl ineligible due to NCAA sanctions).  USC finished as the #52 most productive team with the #42 most productive offense and the #75 most productive defense, which is a substantial reduction in productivity from 2009.  USC played against an average SOS of 61.15 compared to the league average of 63.10.  The Trojans best win was a 49-36 victory over #13 ranked Hawai'i and their worst loss was again to the #87 ranked Washington Huskies by one point, 31-32.

2009
In what was Pete Carroll last season with USC, the Trojans finished the regular season at 8-4 and won their bowl game against #51 ranked Boston College (24-13) to finish 9-4.  The Trojans played against a SOS of 58.08 which was average as compare to the "league" as a whole.  USC was the #31 most productive team overall with the #33 most productive offense and the #36 most productive defense, all less productive than the previous year.  USC's best win was a 18-15 victory over #9 ranked Ohio State and their worst loss (13-16) was to #76 ranked Washington.

2008
This was an excellent year for USC football.  The Trojans finished 12-1, including a Rose Bowl victory over #4 ranked Penn State 38-24.  USC was the #2 ranked team in the nation in terms of overall on-field productivity, with the #12 ranked offense and the #1 ranked defense in the "league".  USC only loss was to Pac 10 rival #28 ranked Oregon State (21-27) and played against an average SOS = 56.92 as compared to a 62.92 SOS for the "league" as a whole.

NHL Goalie Consistency Revisited

With the 2013-2014 NHL regular season underway today, I thought it would be a good time to look at how consistent NHL goalies have been since the 1997 regular season including data through last regular season.  In order to do this, I looked at NHL goalies who started at least 10% of the regular season games for a given season for two consecutive seasons.  That is to take care of issues where goalies are injured or were not considered by the team to be productive enough to be an integral part of the team.  While the 10% cut-off may seem to high or too low, it is a good starting point to use so that I do include both the starting and back-up goalies for a given team in the analysis.

Thus, what I need is a measure for evaluating NHL goalie performance and a way of measuring consistency from one season to another.

In a paper published in the Journal of Sports Economics, David Berri and I measured NHL goalie performance. WAA is our measure of NHL goalie productivity and is based on the absolute value (since a goal against has a negative effect on team wins) of the marginal value of a goal against divided by two (since each win is worth two standings points) times the number of shots on goal that goalie faces times the difference in the save percentage of the goalie and the average save percentage of all goalies for that season.

To measure consistency, I ranked each goalie who had started at least 10% of the regular season games for that season and "graded" each goalie based on where they were in the ranking.  Specifically, all goalies that were in the top 20% were "graded" as an A, between 21% and 40% were "graded" as a B, and so on.  This is the same way we measured NFL QB's, MLB batters and NBA players in chapter 9 of The Wages of Wins.

Then I found out if that goalie started at least 10% of regular season games in consecutive regular seasons and evaluated whether they kept their same grade from one regular season to the next (which I called consistent), whether they moved up or down only one letter grade (which I call near consistent) or if they moved up or down more than two grades (which I call inconsistent).

From the 1997-98 regular season to the 2012-13 regular season, this happened 758 times (consecutive regular seasons starting at least 10% of the games for a goalie).  Of those 758 observations, NHL goalies kept the same grade about 25% of the time, moved up or down one grade about 35% of the time and moved two or more grade from one regular season to the next about 40% of the time.  Since all that I am looking at here is whether they kept the same grade (i.e. A's or F's in consecutive regular seasons), this seems to be a rather volatile amount of performance, and hence I would conclude that NHL goalies on the whole are rather inconsistent.

Of the 192 NHL goalies that kept their same grade, 34% were "A's", 13% were "B's", 18% were "C's", 14% were "D's" and 21% were "F's".  Of those that moved up or down one grade 16% of the total (121 observations) moved down one grade and 19% (or 141) moved up one grade.

Over the last two regular seasons performing the same analysis results in 52 observations and NHL goalies keeping their same "grade" 21% of the time, moving up or down one "grade" 33% of the time and moving two or more grades 46% of the time.  Again, lots of volatility from regular season to regular season.