Slipping under my radar, the University of Akron has extended head football coach Terry Bowden's contact for an additional two years. I think that at a time of coaching changes or contract extensions is a good reason to look at the on-field performance of the football team. So here is a look at Akron since Bowden's taking the reigns of the Zips for the 2012 season using the Complex Invasion College Football Production Model.
2013
At the time of Bowden's contract extension, Akron was 4-7 - with the best won-loss record since 2008. Akron's best victory to date was a 16-7 victory over currently ranked #89 Kent State and their worst loss was a 3-43 loss to currently ranked #83 Ohio. As of last weekend, Akron had the #97 ranked team in overall production. On the offensive side of the team, Akron was ranked as the #101 team and on the defensive side Akron was the #78 best defense.
Hence in terms of the Complex Invasion College Football Production Model, Akron is slightly more productive overall this season as compared to where the Zips finished the prior season, but only slightly. Akron's offense has slipped and their defense has improved as compared to the previous season. The Zips have played against a strength of schedule equal to 64.27 as compared to the "league's" strength of schedule (SOS) of 68.20, which is "average" since the Zips SOS is within one standard deviation from the leagues average SOS.
2012
Terry Bowden's first season as the head football coach of the Akron Zips was horrible. The Zips finished 1-11, with their only win over FCS Morgan State. Hence the Zips finished their second straight season without a victory against a FBS opponent. The good news was that Akron had what ended up as the worst team in the FBS - the Massachusetts Minutemen but the bad news is that the Zips were defeated by the worst team in all of the FBS. That said, Akron was not the worst team in the "league". In fact Akron had improved from the prior season in terms of their relative ranking in all of the Football Bowl Subdivision.
Akron finished as the #101 team in overall productivity, even with only one victory. This is a good time to recall that the NCAA FBS Production Model does not use wins and losses to rank teams, nor does it use points scored and points surrendered directly to rank teams. Rather the model looks at factors that allow teams to gain and maintain possession, move the ball forward, scoring efficiency and makes adjustments for teams in different conferences.
Turning back to the Zips on-field performance, we see that Akron had the #87 ranked offense and the #101 ranked defense. Both are improvements over the 2011 season, which if you are an optimist is a good sign. I am an economist, and I am unconvinced that teams (in general) show much production consistency. (Although I have not rolled up my sleeves to date and tested this hypothesis. Sounds like a off-season project). Anyway, the Zips played against an average strength of schedule of 69.00 as compared to the "league" average of 65.53.
Synopsis
Does this nearly two year result warrant a contract extension? Who know, but since Akron is improving in the win-loss column, it looks as if the university administration is at least willing to give Bowden more time to improve the football program. Since I am also unconvinced firing a coach will make teams better (again a hypothesis that I have not empirically tackled) giving Bowden more time seems to be a reasonable strategy.
Saturday, November 30, 2013
Friday, November 29, 2013
Iron Bowl - 2013
For the past few seasons, I have been using the NCAA FBS Production Model to give an overview of the "Iron Bowl" pitting the University of Alabama Crimson Tide and the Auburn University Tigers. At the end of this blog are the links to my Iron Bowl analysis going back to the 2010 season. For the game tomorrow, here is the analysis of how the two teams have fared so far this 2013 season.
The University of Alabama Crimson Tide
As of last weekend, Alabama was 11-0 overall and 7-0 in the SEC. The Crimson Tide's best game to date this season (win over highest ranked Complex Invasion College Football Production Model team) was a 38-17 victory over #16 currently ranked LSU. Yes, Alabama's toughest opponent this season to date was against a team not even in the top 10 in the Complex Invasion College Football Production Model's ranking.
In terms of the Crimson Tide's production on the field, Alabama's defense currently ranks as the #5 defense overall, and the Tides offense is currently ranked as the #9, giving Alabama's the #5 ranked team overall. Alabama has played against a strength of schedule equal to 71.00 which is average as compared to the "league" as a whole.
The Auburn University Tigers
As of last weekend, the Tigers are 10-1 overall and 6-1 in the SEC. The Tigers best game to date this season (win over highest ranked Complex Invasion College Football Production Model team) was a 45-41 victory over currently ranked #23 Texas A&M and their worst (and only) loss was a 21-35 defeat to currently ranked #16 LSU. Yes, Auburn has defeated all the teams that are currently ranked lower in the Complex Invasion College Football Production Model's than Auburn is currently ranked, and lost to the only team that is currently ranked higher than Auburn is currently ranked.
In terms of the Tigers production on the field, Auburn's defense currently ranks as the #55 defense overall, and the Tigers offense is currently ranked as the #14, giving the Tigers the #22 ranked team overall. Auburn has played against a strength of schedule equal to 63.91 which is average as compared to the "league" as a whole.
Previous Iron Bowl Analysis
2012
2011
2010
The University of Alabama Crimson Tide
As of last weekend, Alabama was 11-0 overall and 7-0 in the SEC. The Crimson Tide's best game to date this season (win over highest ranked Complex Invasion College Football Production Model team) was a 38-17 victory over #16 currently ranked LSU. Yes, Alabama's toughest opponent this season to date was against a team not even in the top 10 in the Complex Invasion College Football Production Model's ranking.
In terms of the Crimson Tide's production on the field, Alabama's defense currently ranks as the #5 defense overall, and the Tides offense is currently ranked as the #9, giving Alabama's the #5 ranked team overall. Alabama has played against a strength of schedule equal to 71.00 which is average as compared to the "league" as a whole.
The Auburn University Tigers
As of last weekend, the Tigers are 10-1 overall and 6-1 in the SEC. The Tigers best game to date this season (win over highest ranked Complex Invasion College Football Production Model team) was a 45-41 victory over currently ranked #23 Texas A&M and their worst (and only) loss was a 21-35 defeat to currently ranked #16 LSU. Yes, Auburn has defeated all the teams that are currently ranked lower in the Complex Invasion College Football Production Model's than Auburn is currently ranked, and lost to the only team that is currently ranked higher than Auburn is currently ranked.
In terms of the Tigers production on the field, Auburn's defense currently ranks as the #55 defense overall, and the Tigers offense is currently ranked as the #14, giving the Tigers the #22 ranked team overall. Auburn has played against a strength of schedule equal to 63.91 which is average as compared to the "league" as a whole.
Previous Iron Bowl Analysis
2012
2011
2010
Thursday, November 28, 2013
The Heroes Game: Iowa Nebraska - 2013
Happy Thanksgiving! Tomorrow kicks off with the Iowa - Nebraska game to start the end on the Big Ten regular season. This is the third after Thanksgiving game day for Iowa and Nebraska, and given some interest by readers in this game, let's take a look at how these two teams have fared so far during the 2013 regular season.
The University of Iowa Hawkeyes
As of last weekend, the Hawkeyes are 7-4 overall and 4-3 in the Big Ten. The Hawkeyes best game to date this season (win over highest ranked Complex Invasion College Football Production Model team) was a 24-21 victory over #56 currently ranked Michigan last weekend and their worst loss was a 27-30 defeat to currently ranked #10 Northern Illinois. Yes, all four of the Hawkeyes losses have been to teams that are currently ranked in the Complex Invasion College Football Production Model's top 10!
In terms of the Hawkeyes production on the field, the Hawkeye's defense currently ranks as the #17 defense overall, and the Hawkeyes offense is currently ranked as the #73, giving the Hawk's the #50 ranked team overall. Iowa has played against a strength of schedule equal to 63.09 which is average as compared to the "league" as a whole.
The University of Nebraska Cornhuskers
As of last weekend, the Cornhuskers are 8-3 overall and 5-2 in the Big Ten. The Cornhuskers best game to date this season (win over highest ranked Complex Invasion College Football Production Model team) was also a victory over #56 currently ranked Michigan and their worst loss was a 23-34 defeat to currently ranked #67 Minnesota. Nebraka's other two losses have been to teams that are currently ranked in the Complex Invasion College Football Production Model's top 25%.
In terms of the Cornhuskers production on the field, Nebraska's defense currently ranks as the #56 defense overall, and the Cornhuskers offense is currently ranked as the #43, giving the Husker's the #43 ranked team overall. Nebraska has played against a strength of schedule equal to 77.18 which is average as compared to the "league" as a whole.
The University of Iowa Hawkeyes
As of last weekend, the Hawkeyes are 7-4 overall and 4-3 in the Big Ten. The Hawkeyes best game to date this season (win over highest ranked Complex Invasion College Football Production Model team) was a 24-21 victory over #56 currently ranked Michigan last weekend and their worst loss was a 27-30 defeat to currently ranked #10 Northern Illinois. Yes, all four of the Hawkeyes losses have been to teams that are currently ranked in the Complex Invasion College Football Production Model's top 10!
In terms of the Hawkeyes production on the field, the Hawkeye's defense currently ranks as the #17 defense overall, and the Hawkeyes offense is currently ranked as the #73, giving the Hawk's the #50 ranked team overall. Iowa has played against a strength of schedule equal to 63.09 which is average as compared to the "league" as a whole.
The University of Nebraska Cornhuskers
As of last weekend, the Cornhuskers are 8-3 overall and 5-2 in the Big Ten. The Cornhuskers best game to date this season (win over highest ranked Complex Invasion College Football Production Model team) was also a victory over #56 currently ranked Michigan and their worst loss was a 23-34 defeat to currently ranked #67 Minnesota. Nebraka's other two losses have been to teams that are currently ranked in the Complex Invasion College Football Production Model's top 25%.
In terms of the Cornhuskers production on the field, Nebraska's defense currently ranks as the #56 defense overall, and the Cornhuskers offense is currently ranked as the #43, giving the Husker's the #43 ranked team overall. Nebraska has played against a strength of schedule equal to 77.18 which is average as compared to the "league" as a whole.
Tuesday, November 26, 2013
NCAA Athletic Department Spending
Last week I talked to a reporter (Josh O'Leary) for the Iowa City Press Citizen about NCAA athletic department capital spending - focusing on buildings and grounds expenditures. While Mr. O'Leary did a very good job, I thought that I would also share some things that did not make the article.
Using the data from USA Today's NCAA financial department database for the 2006 to 2011 academic years, I find that athletic department spending by Big Ten athletic departments on Buildings and Grounds is statistically different (and higher) for all of the other twenty-nine conferences except for the ACC, Big 12, Pac 12 (now) and SEC during the 2006 to 2009 seasons and for the 2010 and 2011 years is statistically different (and higher) for all the twenty-nine conferences except the ACC, Big 12 and SEC. Hence, Big Ten schools have some of the highest average expenditures on buildings and grounds as a conference.
Additionally, I said that there are "haves and have not's" among NCAA athletic departments. To back that statement up, I note that the degree of building and grounds expenditures is highly unequal. As a comparison group, I looked at income inequality as measure by the Gini coefficient, building and grounds expenditures are more unequal than any of the nations that the World Bank reports from 2009. The highest (most unequal) was South Africa in 2009 at 63.1. For all six years, NCAA athletic department expenditures on building and grounds had a Gini coefficient at a minimum of 72.6, which is more unequal than one of nations with the most unequally distribution of income.
Using the data from USA Today's NCAA financial department database for the 2006 to 2011 academic years, I find that athletic department spending by Big Ten athletic departments on Buildings and Grounds is statistically different (and higher) for all of the other twenty-nine conferences except for the ACC, Big 12, Pac 12 (now) and SEC during the 2006 to 2009 seasons and for the 2010 and 2011 years is statistically different (and higher) for all the twenty-nine conferences except the ACC, Big 12 and SEC. Hence, Big Ten schools have some of the highest average expenditures on buildings and grounds as a conference.
Additionally, I said that there are "haves and have not's" among NCAA athletic departments. To back that statement up, I note that the degree of building and grounds expenditures is highly unequal. As a comparison group, I looked at income inequality as measure by the Gini coefficient, building and grounds expenditures are more unequal than any of the nations that the World Bank reports from 2009. The highest (most unequal) was South Africa in 2009 at 63.1. For all six years, NCAA athletic department expenditures on building and grounds had a Gini coefficient at a minimum of 72.6, which is more unequal than one of nations with the most unequally distribution of income.
Monday, November 25, 2013
2013 NCAA FBS Top 25 Ranking for Week 13
The top team has not changed from last week, nor has the second team (Baylor) even though they lost their first game of the season to Oklahoma State on Saturday. The reason is that Baylor's cumulative total offense and defense still are better than other teams in the FBS using the Complex Invasion College Football Production Model with the stats from College Football Stats. So listed below are the top teams in terms of on-field productivity.
Previous Top 25 Ranks for 2013
2013 NCAA FBS Top 25 Ranking for Week 12
2013 NCAA FBS Top 25 Ranking for Week 11
2013 NCAA FBS Top 25 Ranking for Week 10
2013 NCAA FBS Top 25 Ranking for Week 9
2013 NCAA FBS Top 25 Ranking for Week 8
2013 NCAA FBS Top 25 Ranking for Week 7
2013 NCAA FBS Top 25 Ranking for Week 6
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
| Rank | Team |
| 1 | Florida State |
| 2 | Baylor |
| 3 | Oregon |
| 4 | Louisville |
| 5 | Alabama |
| 6 | Ohio State |
| 7 | Wisconsin |
| 8 | Michigan State |
| 9 | Missouri |
| 10 | Northern Illinois |
| 11 | Oklahoma State |
| 12 | Clemson |
| 13 | Marshall |
| 14 | Fresno State |
| 15 | Washington |
| 16 | LSU |
| 17 | Bowling Green |
| 18 | East Carolina |
| 19 | Arizona State |
| 20 | Utah State |
| 21 | Cincinnati |
| 22 | Auburn |
| 23 | Texas A&M |
| 24 | Ball State |
| 25 | Houston |
Previous Top 25 Ranks for 2013
2013 NCAA FBS Top 25 Ranking for Week 12
2013 NCAA FBS Top 25 Ranking for Week 11
2013 NCAA FBS Top 25 Ranking for Week 10
2013 NCAA FBS Top 25 Ranking for Week 9
2013 NCAA FBS Top 25 Ranking for Week 8
2013 NCAA FBS Top 25 Ranking for Week 7
2013 NCAA FBS Top 25 Ranking for Week 6
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
Sunday, November 24, 2013
Friday, November 22, 2013
Mike Leach Contract Extension
ESPN reports that Washington State has extended head football coach Mike Leach's contract through 2018. So this is a great time to look at the Washington State program with head football coach Mike Leach at the helm. As a point of reference for those that are interested, I have previously analyzed Washington State under former head football coach Paul Wulff. Below is a chart with the teams total rank, offense rank, defense rank and the worst team rank (which WSU was in both 2008 and 2009) based on the Complex Invasion College Football Production Model. After the chart is a more details about the Washington State University Cougars football team since 2008.
2013
Currently Washington State's record is 5-5 with their best win (win over highest currently ranked opponent) was a 10-7 victory over currently #34 ranked USC. WSU's worst game (loss to the lowest currently ranked opponent) was a 17-55 loss to currently #39 ranked Stanford. In fact all five losses are to teams ranked in the top third of the NCAA Football Bowl Subdivision teams. Hence, Washington State beats teams that they should (Southern Utah, Idaho and California) and some they should not (USC and Arizona) - which makes Washington State an interesting team to watch for in terms of productivity over the next few years given Leach's contract extension. The Cougars are currently ranked as the #86 team in overall productivity with the #71 ranked offense and the #96 ranked defense. WSU has played against a strength of schedule (SOS) of 54.90 which is "tougher" than the average schedule to date for the "league" to date. A teams SOS is "tougher" if it is between one and two standard deviations lower than the average "league" SOS, which is currently the case.
2012
Mike Leach's first season as head football coach of the Washington State Cougars resulted in a 3-9 record. Washington State defeated in-state rival #59 ranked Washington 31-28 and was their best victory of the season. The Cougars worst game was a 34-35 loss to #123 ranked University of Colorado. WSU was the #113 ranked team in terms of overall productivity with again the #117 ranked offense and the #89 ranked defense against an average SOS.
2013
Currently Washington State's record is 5-5 with their best win (win over highest currently ranked opponent) was a 10-7 victory over currently #34 ranked USC. WSU's worst game (loss to the lowest currently ranked opponent) was a 17-55 loss to currently #39 ranked Stanford. In fact all five losses are to teams ranked in the top third of the NCAA Football Bowl Subdivision teams. Hence, Washington State beats teams that they should (Southern Utah, Idaho and California) and some they should not (USC and Arizona) - which makes Washington State an interesting team to watch for in terms of productivity over the next few years given Leach's contract extension. The Cougars are currently ranked as the #86 team in overall productivity with the #71 ranked offense and the #96 ranked defense. WSU has played against a strength of schedule (SOS) of 54.90 which is "tougher" than the average schedule to date for the "league" to date. A teams SOS is "tougher" if it is between one and two standard deviations lower than the average "league" SOS, which is currently the case.
2012
Mike Leach's first season as head football coach of the Washington State Cougars resulted in a 3-9 record. Washington State defeated in-state rival #59 ranked Washington 31-28 and was their best victory of the season. The Cougars worst game was a 34-35 loss to #123 ranked University of Colorado. WSU was the #113 ranked team in terms of overall productivity with again the #117 ranked offense and the #89 ranked defense against an average SOS.
Monday, November 18, 2013
2013 NCAA FBS Top 25 Ranking for Week 12
We have a new most productive team this week, with Florida State now ranked as the most productive team in the Football Bowl Subdivision, jumping over Baylor which has moved down to #2. Here is the list of the top 25 using the Complex Invasion College Football Production Model. Data from www.cfbstats.com.
Previous Top 25 Ranks for 2013
2013 NCAA FBS Top 25 Ranking for Week 11
2013 NCAA FBS Top 25 Ranking for Week 10
2013 NCAA FBS Top 25 Ranking for Week 9
2013 NCAA FBS Top 25 Ranking for Week 8
2013 NCAA FBS Top 25 Ranking for Week 7
2013 NCAA FBS Top 25 Ranking for Week 6
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
| Rank | Team |
| 1 | Florida State |
| 2 | Baylor |
| 3 | Oregon |
| 4 | Louisville |
| 5 | Ohio State |
| 6 | Wisconsin |
| 7 | Alabama |
| 8 | Missouri |
| 9 | Northern Illinois |
| 10 | Texas A&M |
| 11 | East Carolina |
| 12 | Marshall |
| 13 | Michigan State |
| 14 | Arizona State |
| 15 | Cincinnati |
| 16 | Clemson |
| 17 | LSU |
| 18 | Utah State |
| 19 | Oklahoma State |
| 20 | Auburn |
| 21 | Houston |
| 22 | Fresno State |
| 23 | Washington |
| 24 | Ball State |
| 25 | UCLA |
Previous Top 25 Ranks for 2013
2013 NCAA FBS Top 25 Ranking for Week 11
2013 NCAA FBS Top 25 Ranking for Week 10
2013 NCAA FBS Top 25 Ranking for Week 9
2013 NCAA FBS Top 25 Ranking for Week 8
2013 NCAA FBS Top 25 Ranking for Week 7
2013 NCAA FBS Top 25 Ranking for Week 6
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
Sunday, November 17, 2013
EPL Pay and Performance
The Economist has a nice article on pay and performance in the English Premier League, making some interesting arguments as to why points per pound (UK currency) is different for different teams.
Saturday, November 16, 2013
NCAA Athletic Department Finances
I recently looked at how FBS and non-FBS athletic departments compare in terms of revenue streams and revenue inequality. To recap - I found that average generated revenues are statistically different and higher at FBS compared to non-FBS athletic departments and that non-generated revenues are not statistically different from each other at FBS and non-FBS athletic departments.
Additionally I looked at revenue inequality among NCAA FBS and non-FBS athletic departments by revenue streams and found that other than ticket sales, FBS athletic departments have greater revenue inequality as compared to non-FBS athletic departments.
I was subsequently asked about the difference between automatically qualifying (AQ) conferences and non-automatic qualifying conferences in the BCS. Great idea - so here I analyze both differences in revenue and revenue inequality among AQ and non-AQ conferences.
First - are revenue streams different among the AQ and non-AQ conferences? Short answer is yes. In fact using the data from USA Today's athletic department database from 2006 to 2011 every revenue category has higher average revenue by AQ athletic departments than non-AQ athletic departments.
Second - are revenues more unequal for AQ than non-AQ athletic departments? Short answer is that for some yes and others no. Specifically, AQ athletic department are more unequal for student fee revenue, school fund revenues and other revenues for each of the six years in the USA Today database and AQ athletic departments are more equal for ticket sales, contribution revenues, rights and licensing revenue, total revenue and generated revenues. Thus other than the two subsidy revenue streams, AQ athletic departments are only more unequal for other revenues.
Additionally I looked at revenue inequality among NCAA FBS and non-FBS athletic departments by revenue streams and found that other than ticket sales, FBS athletic departments have greater revenue inequality as compared to non-FBS athletic departments.
I was subsequently asked about the difference between automatically qualifying (AQ) conferences and non-automatic qualifying conferences in the BCS. Great idea - so here I analyze both differences in revenue and revenue inequality among AQ and non-AQ conferences.
First - are revenue streams different among the AQ and non-AQ conferences? Short answer is yes. In fact using the data from USA Today's athletic department database from 2006 to 2011 every revenue category has higher average revenue by AQ athletic departments than non-AQ athletic departments.
Second - are revenues more unequal for AQ than non-AQ athletic departments? Short answer is that for some yes and others no. Specifically, AQ athletic department are more unequal for student fee revenue, school fund revenues and other revenues for each of the six years in the USA Today database and AQ athletic departments are more equal for ticket sales, contribution revenues, rights and licensing revenue, total revenue and generated revenues. Thus other than the two subsidy revenue streams, AQ athletic departments are only more unequal for other revenues.
Friday, November 15, 2013
Baylor Extends Art Briles Contract
ESPN reports that Baylor has finalized head football coach Art Briles' contract for the next ten years. So this is a good time to look at the Baylor Bears football team since Briles took over the reigns of the Bears in 2008 using the Complex Invasion College Football Production Model. For those interested in how Baylor has ranked during Art Briles tenure, the graph below is a quick look at Baylor's offense, defense and total rankings over the last five plus years. I have also included what would be the worst ranked team (purple line) for some perspective.
2013
Currently Baylor is 8-0 with their best win (win over highest currently ranked opponent) over currently #37 ranked Kansas State. The Bears are currently ranked as the #1 team in overall productivity (and have been nine of the ten weeks I have ranked teams this season) with the #2 ranked offense (slight improvement - read sarcasm) and an immensely improved #4 ranked defense.
While Baylor gets a lot of attention for their offense, I would like to note how productive the are on defense. Specifically, in the model that I use, teams that allow their opponents to move the ball, maintain possession and score efficiently are ranked lower on defense. Given how quickly and how often Baylor scores on offense (currently leads the league in kickoffs to their opponent meaning the defense is on the field often), the Bears defense is phenomenally productive this season. As you can see in the graph above, defensive performance has been the big problem with Baylor under Briles tenure up to this year.
The knock on Baylor is that they have played against a strength of schedule (SOS) of 86.13 which is "easier" than the average schedule to date for the "league" to date. A teams SOS is "easier" if it is between one and two standard deviations higher than the average "league" SOS, which is currently the case. Yet from the model's perspective Baylor is a national championship caliber team. Of the four regular season games still on Baylor's schedule, they have to play three above average teams in terms of production, with Oklahoma State the toughest challenge for the Bears. If they can make it though conference play unscathed, Baylor has a legitimate case to play in the "national championship" game in January.
2012
The 2012 Bears are a tale of two teams, one highly productive (offense) and one highly unproductive (defense) The Bears finished the regular season bowl eligible at 7-5. Baylor defeated #36 ranked UCLA in the Bridgeport Education Holiday Bowl to finish the season at 8-5 overall. Baylor was the #49 ranked team in terms of overall productivity with again the #3 ranked offense and the #123 ranked defense against an average SOS. Baylor University's best game was a 52-24 away win over #17 ranked Kansas State and their worst performance was a 21-35 loss to #72 ranked Iowa State.
2011
Baylor University finished the regular season at 9-3 and defeated the #69 ranked University of Washington 67-56 - which seems more like a basketball score than a football score. Baylor was the #26 ranked NCAA FBS team in overall production, with the #3 ranked offense and the #98 ranked defense. Baylor's best victory was a 50-48 victory over #9 ranked Texas Christian University and the Bears worst performance was a 35-36 loss to #60 ranked Kansas State. The Bears played against an average SOS of 62.92 as compared to the "league" SOS of 63.24.
2010
Baylor University finished the regular season bowl eligible at 7-5, but were defeated in the post-season by #18 ranked Illinois. The Bears were much improved over the previous two seasons and finished as the #51 ranked team in terms of overall productivity; with the #27 ranked offense and the #95 ranked defense. Baylor University played against a SOS of 60.92, which is "average" (i.e. within one standard deviation of the "league" average SOS of 63.10).
2009
The Bears against finished the season at 4-8 and bowl ineligible. Baylor's best game was 40-32 victory over #47 Missouri and their worst loss was to #82 Iowa State all against a SOS that was "tougher" as compared to the average SOS for the "league". In terms of overall production, Baylor University was the #100 ranked team with the #102 ranked offense and the #68 ranked defense.
2008
In head football coach Art Briles first season at Baylor, the Bears finished the regular season at 4-8 and were bowl ineligible. Baylor's best game was 38-10 victory over #104 Iowa State (yes all four of their victories were against teams ranked 104 or lower in overall production) and their worst loss was to #40 Nebraska all against a SOS that was "tougher" as compared to the average SOS for the "league". In terms of overall production, Baylor University was the #50 ranked team with the #46 ranked offense and the #53 ranked defense.
2013
Currently Baylor is 8-0 with their best win (win over highest currently ranked opponent) over currently #37 ranked Kansas State. The Bears are currently ranked as the #1 team in overall productivity (and have been nine of the ten weeks I have ranked teams this season) with the #2 ranked offense (slight improvement - read sarcasm) and an immensely improved #4 ranked defense.
While Baylor gets a lot of attention for their offense, I would like to note how productive the are on defense. Specifically, in the model that I use, teams that allow their opponents to move the ball, maintain possession and score efficiently are ranked lower on defense. Given how quickly and how often Baylor scores on offense (currently leads the league in kickoffs to their opponent meaning the defense is on the field often), the Bears defense is phenomenally productive this season. As you can see in the graph above, defensive performance has been the big problem with Baylor under Briles tenure up to this year.
The knock on Baylor is that they have played against a strength of schedule (SOS) of 86.13 which is "easier" than the average schedule to date for the "league" to date. A teams SOS is "easier" if it is between one and two standard deviations higher than the average "league" SOS, which is currently the case. Yet from the model's perspective Baylor is a national championship caliber team. Of the four regular season games still on Baylor's schedule, they have to play three above average teams in terms of production, with Oklahoma State the toughest challenge for the Bears. If they can make it though conference play unscathed, Baylor has a legitimate case to play in the "national championship" game in January.
2012
The 2012 Bears are a tale of two teams, one highly productive (offense) and one highly unproductive (defense) The Bears finished the regular season bowl eligible at 7-5. Baylor defeated #36 ranked UCLA in the Bridgeport Education Holiday Bowl to finish the season at 8-5 overall. Baylor was the #49 ranked team in terms of overall productivity with again the #3 ranked offense and the #123 ranked defense against an average SOS. Baylor University's best game was a 52-24 away win over #17 ranked Kansas State and their worst performance was a 21-35 loss to #72 ranked Iowa State.
2011
Baylor University finished the regular season at 9-3 and defeated the #69 ranked University of Washington 67-56 - which seems more like a basketball score than a football score. Baylor was the #26 ranked NCAA FBS team in overall production, with the #3 ranked offense and the #98 ranked defense. Baylor's best victory was a 50-48 victory over #9 ranked Texas Christian University and the Bears worst performance was a 35-36 loss to #60 ranked Kansas State. The Bears played against an average SOS of 62.92 as compared to the "league" SOS of 63.24.
2010
Baylor University finished the regular season bowl eligible at 7-5, but were defeated in the post-season by #18 ranked Illinois. The Bears were much improved over the previous two seasons and finished as the #51 ranked team in terms of overall productivity; with the #27 ranked offense and the #95 ranked defense. Baylor University played against a SOS of 60.92, which is "average" (i.e. within one standard deviation of the "league" average SOS of 63.10).
2009
The Bears against finished the season at 4-8 and bowl ineligible. Baylor's best game was 40-32 victory over #47 Missouri and their worst loss was to #82 Iowa State all against a SOS that was "tougher" as compared to the average SOS for the "league". In terms of overall production, Baylor University was the #100 ranked team with the #102 ranked offense and the #68 ranked defense.
2008
In head football coach Art Briles first season at Baylor, the Bears finished the regular season at 4-8 and were bowl ineligible. Baylor's best game was 38-10 victory over #104 Iowa State (yes all four of their victories were against teams ranked 104 or lower in overall production) and their worst loss was to #40 Nebraska all against a SOS that was "tougher" as compared to the average SOS for the "league". In terms of overall production, Baylor University was the #50 ranked team with the #46 ranked offense and the #53 ranked defense.
Thursday, November 14, 2013
NBA Payroll and Performance
In our book The Wages of Wins we talk about when performing statistical analysis; for samples, size matters. Specifically, the larger the sample size (more relevant data), the more accurate (smaller the standard error) are the statistical results. Thus looking at a team over a few games or a league over a couple of years and making definitive statements about the team or the league is a highly dubious endeavor.
How do we come up with such a conclusion? Here is a guide for further reading:
Why are small sample sizes a problem, and thus need to use a longer time period?
A step-by-step guide on how to perform the payroll and performance analysis.
Why do I use relative payroll (3rd paragraph)?
Why do I use R-squared (or adjusted R-squared) as our measuring stick?
Recently I read the following: NBA teams in the top 10 of payroll for the past two years have all made the playoffs, and thus the author concludes that the following teams will make the playoffs this year: Nets, Knicks, Heat, Bulls, Lakers, Raptors, Clippers, Celtics, Thunder and Pacers. Others looking at on-court performance have left out some of the teams in the payroll top 10 in their predictions.
So payroll "predicts" ten of the sixteen NBA playoff teams. 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.
So, what I thought I would do was look at the relationship between NBA (relative) payroll and NBA regular season performance. To do that I need to get NBA payroll data, which is provided at University of Michigan Professor Rodney Fort's website (direct NBA payroll link) and since I was already there, I used his data for NBA regular season winning percentage (direct NBA winning percentage link) to perform this statistical analysis.
Starting with the 1990-91 NBA regular season and finishing with the most completed NBA regular season (2012-2013), performing a linear regression (controlling for heteroskedastity) on the plus side the statistical analysis results in relative payroll to be positive and statistically significant but on the down side reveals that relative payroll and winning percent have almost 11% of common variation, meaning that relative payroll fails to explain about 90% of regular season winning percentage. Frankly, that is rather poor if you are using payroll to "predict" performance. If we take a look at the last two NBA seasons (which one was a lockout season), we see that the relationship between relative payroll and regular season winning percent improves to about 23% using adjusted R-squared. Again, for the last two seasons (one with a lockout) the variation in relative payroll doesn't even explain a quarter of the variation in NBA team winning percent.
If we use a bigger recent sample (five years which covers the 2008-09 to 2012-13 NBA regular seasons) we see that the amount of variation in common between relative payroll and regular season winning percent is still about 23%. Taking the five years before that (2003-04 to 2007-08 seasons) things are vastly different. Relative Payroll is statistically insignificant, or in other words, statistically has ZERO impact on winning percent. Even if you can overlook that the amount of variation in common between the two is 1%. Hence the relationship between relative payroll and winning percent has improved in the past five years as compared to the previous five year period. Again, to me not that convincing of a relationship.
As always, the proof is in the pudding - so I will come back to this at the end of the 2013-14 NBA regular season, let's take a look at how well payroll relates to getting into the NBA playoffs.
How do we come up with such a conclusion? Here is a guide for further reading:
Why are small sample sizes a problem, and thus need to use a longer time period?
A step-by-step guide on how to perform the payroll and performance analysis.
Why do I use relative payroll (3rd paragraph)?
Why do I use R-squared (or adjusted R-squared) as our measuring stick?
Recently I read the following: NBA teams in the top 10 of payroll for the past two years have all made the playoffs, and thus the author concludes that the following teams will make the playoffs this year: Nets, Knicks, Heat, Bulls, Lakers, Raptors, Clippers, Celtics, Thunder and Pacers. Others looking at on-court performance have left out some of the teams in the payroll top 10 in their predictions.
So payroll "predicts" ten of the sixteen NBA playoff teams. 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.
So, what I thought I would do was look at the relationship between NBA (relative) payroll and NBA regular season performance. To do that I need to get NBA payroll data, which is provided at University of Michigan Professor Rodney Fort's website (direct NBA payroll link) and since I was already there, I used his data for NBA regular season winning percentage (direct NBA winning percentage link) to perform this statistical analysis.
Starting with the 1990-91 NBA regular season and finishing with the most completed NBA regular season (2012-2013), performing a linear regression (controlling for heteroskedastity) on the plus side the statistical analysis results in relative payroll to be positive and statistically significant but on the down side reveals that relative payroll and winning percent have almost 11% of common variation, meaning that relative payroll fails to explain about 90% of regular season winning percentage. Frankly, that is rather poor if you are using payroll to "predict" performance. If we take a look at the last two NBA seasons (which one was a lockout season), we see that the relationship between relative payroll and regular season winning percent improves to about 23% using adjusted R-squared. Again, for the last two seasons (one with a lockout) the variation in relative payroll doesn't even explain a quarter of the variation in NBA team winning percent.
If we use a bigger recent sample (five years which covers the 2008-09 to 2012-13 NBA regular seasons) we see that the amount of variation in common between relative payroll and regular season winning percent is still about 23%. Taking the five years before that (2003-04 to 2007-08 seasons) things are vastly different. Relative Payroll is statistically insignificant, or in other words, statistically has ZERO impact on winning percent. Even if you can overlook that the amount of variation in common between the two is 1%. Hence the relationship between relative payroll and winning percent has improved in the past five years as compared to the previous five year period. Again, to me not that convincing of a relationship.
As always, the proof is in the pudding - so I will come back to this at the end of the 2013-14 NBA regular season, let's take a look at how well payroll relates to getting into the NBA playoffs.
Wednesday, November 13, 2013
Eastern Michigan University Head Football Coach Relieved of Duties
CBS Sports reports that Eastern Michigan University head football coach Ron English has been relieved of his duties. This makes the fifth head coaching change so far this season. Using the Complex Invasion College Football Production Model, let's take a look at the Eastern Michigan University Eagles under former head coach English's tenure. Here is how the Eagles have ranked since 2009, with more details below this graph.
2013
At the end of the first weekend in November, Eastern Michigan University was 1-8 with their best (and only) win (win over highest currently ranked opponent) over Football Championship Subdivision (FCS) Howard and their worst loss (loss to lowest currently ranked opponent) to #80 ranked Rutgers University. The Eagles were ranked as the #119 team in overall productivity (out of 125) with the #94 ranked offense and the #123 ranked defense. The Eagles have played against a strength of schedule (SOS) of 54.22 which was "tougher" than the average schedule to date for the "league". 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.
2012
The Eagles finished 2-10 and were the #116 ranked team in terms of overall productivity with the #110 ranked offense and the #104 ranked defense against an average SOS of 65.75. Eastern Michigan's best game was a 29-23 away win over #65 ranked Western Michigan and their worst performance was a 14-31 loss to FCS Illinois State.
2011
Eastern Michigan University finished 6-6 and while they had six wins, which normally would make them bowl eligible, two of those victories were against FCS teams and according to NCAA rules only one victory over an FCS school counts towards bowl eligibility, thus EMU did not receive a bowl game invitation. EMU was the #72 ranked NCAA FBS team in overall production, with the #86 ranked offense and the #54 ranked defense. Eastern Michigan's best victory was a 14-10 victory over #52 ranked Western Michigan University and the Eagles worst performance was a 31-33 loss to #105 ranked Ball State. The Eagles played against an easier SOS of 73.67 as compared to the "league" SOS of 63.24.
2010
Eastern Michigan Univeristy finished the regular season at 2-10. The Eagles were the #117 ranked team in terms of overall productivity; with the #111 ranked offense and the #119 ranked defense. Eastern Michigan University played against a SOS of 61.42, which is "average" (i.e. within one standard deviation of the "league" average SOS of 63.10).
2009
The Eagles finished the season at 0-12. EMU's worst loss was to #110 Ball State all against a SOS that was average as compared to the average SOS for the "league". In terms of overall production, Eastern Michigan University was the #113 ranked team with the #113 ranked offense and the #101 ranked defense.
2013 NCAA FBS Head Coach Changes
Florida Atlantic and Carl Pelini
Miami of Ohio and Don Treadwell
UConn and Paul Pasqualoni
USC and Lane Kiffen
2013
At the end of the first weekend in November, Eastern Michigan University was 1-8 with their best (and only) win (win over highest currently ranked opponent) over Football Championship Subdivision (FCS) Howard and their worst loss (loss to lowest currently ranked opponent) to #80 ranked Rutgers University. The Eagles were ranked as the #119 team in overall productivity (out of 125) with the #94 ranked offense and the #123 ranked defense. The Eagles have played against a strength of schedule (SOS) of 54.22 which was "tougher" than the average schedule to date for the "league". 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.
2012
The Eagles finished 2-10 and were the #116 ranked team in terms of overall productivity with the #110 ranked offense and the #104 ranked defense against an average SOS of 65.75. Eastern Michigan's best game was a 29-23 away win over #65 ranked Western Michigan and their worst performance was a 14-31 loss to FCS Illinois State.
2011
Eastern Michigan University finished 6-6 and while they had six wins, which normally would make them bowl eligible, two of those victories were against FCS teams and according to NCAA rules only one victory over an FCS school counts towards bowl eligibility, thus EMU did not receive a bowl game invitation. EMU was the #72 ranked NCAA FBS team in overall production, with the #86 ranked offense and the #54 ranked defense. Eastern Michigan's best victory was a 14-10 victory over #52 ranked Western Michigan University and the Eagles worst performance was a 31-33 loss to #105 ranked Ball State. The Eagles played against an easier SOS of 73.67 as compared to the "league" SOS of 63.24.
2010
Eastern Michigan Univeristy finished the regular season at 2-10. The Eagles were the #117 ranked team in terms of overall productivity; with the #111 ranked offense and the #119 ranked defense. Eastern Michigan University played against a SOS of 61.42, which is "average" (i.e. within one standard deviation of the "league" average SOS of 63.10).
2009
The Eagles finished the season at 0-12. EMU's worst loss was to #110 Ball State all against a SOS that was average as compared to the average SOS for the "league". In terms of overall production, Eastern Michigan University was the #113 ranked team with the #113 ranked offense and the #101 ranked defense.
2013 NCAA FBS Head Coach Changes
Florida Atlantic and Carl Pelini
Miami of Ohio and Don Treadwell
UConn and Paul Pasqualoni
USC and Lane Kiffen
Monday, November 11, 2013
2013 NCAA FBS Top 25 Ranking for Week 11
The latest NCAA FBS top 25 rankings are now done using the Complex Invasion College Football Production Model. Baylor regained it's number one ranking from Oregon, which given Baylor's better production on the field as compared to Oregon is no surprise. Additionally, we see that Alabama has moved up a little and LSU has dropped.
Previous Top 25 Ranks for 2013
2013 NCAA FBS Top 25 Ranking for Week 10
2013 NCAA FBS Top 25 Ranking for Week 9
2013 NCAA FBS Top 25 Ranking for Week 8
2013 NCAA FBS Top 25 Ranking for Week 7
2013 NCAA FBS Top 25 Ranking for Week 6
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
| Rank | Team |
| 1 | Baylor |
| 2 | Florida State |
| 3 | Oregon |
| 4 | Louisville |
| 5 | Ohio State |
| 6 | Missouri |
| 7 | Alabama |
| 8 | Texas A&M |
| 9 | Northern Illinois |
| 10 | Houston |
| 11 | Wisconsin |
| 12 | LSU |
| 13 | Marshall |
| 14 | Ball State |
| 15 | Washington |
| 16 | Fresno State |
| 17 | Auburn |
| 18 | Utah State |
| 19 | Michigan State |
| 20 | Arizona State |
| 21 | East Carolina |
| 22 | Cincinnati |
| 23 | Oklahoma State |
| 24 | Clemson |
| 25 | UCLA |
Previous Top 25 Ranks for 2013
2013 NCAA FBS Top 25 Ranking for Week 10
2013 NCAA FBS Top 25 Ranking for Week 9
2013 NCAA FBS Top 25 Ranking for Week 8
2013 NCAA FBS Top 25 Ranking for Week 7
2013 NCAA FBS Top 25 Ranking for Week 6
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
Sunday, November 10, 2013
Firing Coaches and Performance
The BBC has a nice piece on whether statistically it matters (in terms of performance) if the head coach (manager) is fired (sacked). I wonder if there is any similarities in US sports?
Saturday, November 9, 2013
NCAA Athletic Department Revenue Inequality
Last year I looked at NCAA FBS bowl game payout inequality to bowl participants, and found that there is a substantial level of payout inequality. Now I am turning my attention to income (or revenue) inequality at FBS and non-FBS schools that are in USA Today's NCAA athletic department database. As I stated yesterday, not all schools are in the USA Today's database.
Later I will investigate the Equity in Athletics data. The advantage is that I can include more schools and look at total revenue by sport, but for now, I using USA Today's database since I am interested in revenue inequality among various revenue streams in athletic departments, not solely total revenue at athletic departments.
Economist's measure income inequality using a Gini coefficient. The Gini coefficient measures the total difference between perfect income equality and the actual income distribution which is called a Lorenze curve. For those interested - here's a step-by-step guide to calculate the Gini coefficient.
Given we have an income inequality measure and NCAA athletic department income, let's take a look at the various athletic department's revenue streams in the USA Today database from 2006 to 2011 by FBS and non-FBS schools. The table below has the Gini coefficients. As I expected FBS schools in general have greater revenue inequality than non-FBS schools except for ticket sales for each of the seasons during this time period and contributions in 2007, 2009 and 2011. Otherwise FBS schools have greater income inequality than non-FBS schools.
| Gini | Gini | |||
| 2006 | FBS | Non-FBS | ||
| Ticket Sales | 0.4988 | 0.5693 | ||
| Student Fees | 0.5708 | 0.4753 | ||
| School Funds | 0.5310 | 0.4361 | ||
| Contributions | 0.5852 | 0.5315 | ||
| Rights & Licensing | 0.4390 | 0.3919 | ||
| OtherRevenue | 0.3882 | 0.3637 | ||
| Total Revenue | 0.3492 | 0.2540 | ||
| Generated Revenue | 0.4685 | 0.3809 | ||
| Gini | Gini | |||
| 2007 | FBS | Non-FBS | ||
| Ticket Sales | 0.5067 | 0.5610 | ||
| Student Fees | 0.5641 | 0.4882 | ||
| School Funds | 0.5137 | 0.4441 | ||
| Contributions | 0.4971 | 0.5398 | ||
| Rights & Licensing | 0.4474 | 0.3790 | ||
| OtherRevenue | 0.3750 | 0.3629 | ||
| Total Revenue | 0.3293 | 0.2601 | ||
| Generated Revenue | 0.4469 | 0.3747 | ||
| Gini | Gini | |||
| 2008 | FBS | Non-FBS | ||
| Ticket Sales | 0.4964 | 0.5818 | ||
| Student Fees | 0.5767 | 0.4917 | ||
| School Funds | 0.5108 | 0.4299 | ||
| Contributions | 0.5165 | 0.5121 | ||
| Rights & Licensing | 0.4525 | 0.3710 | ||
| OtherRevenue | 0.4046 | 0.3273 | ||
| Total Revenue | 0.3333 | 0.2525 | ||
| Generated Revenue | 0.4503 | 0.3648 | ||
| Gini | Gini | |||
| 2009 | FBS | Non-FBS | ||
| Ticket Sales | 0.5061 | 0.5704 | ||
| Student Fees | 0.5832 | 0.5102 | ||
| School Funds | 0.4983 | 0.4037 | ||
| Contributions | 0.5108 | 0.5631 | ||
| Rights & Licensing | 0.4465 | 0.3617 | ||
| OtherRevenue | 0.4946 | 0.3212 | ||
| Total Revenue | 0.3219 | 0.2496 | ||
| Generated Revenue | 0.4485 | 0.3640 | ||
| Gini | Gini | |||
| 2010 | FBS | Non-FBS | ||
| Ticket Sales | 0.5074 | 0.5771 | ||
| Student Fees | 0.5817 | 0.5127 | ||
| School Funds | 0.5361 | 0.3992 | ||
| Contributions | 0.5394 | 0.5295 | ||
| Rights & Licensing | 0.4538 | 0.3539 | ||
| OtherRevenue | 0.4071 | 0.2982 | ||
| Total Revenue | 0.3380 | 0.2433 | ||
| Generated Revenue | 0.4602 | 0.3491 | ||
| Gini | Gini | |||
| 2011 | FBS | Non-FBS | ||
| Ticket Sales | 0.5131 | 0.5687 | ||
| Student Fees | 0.5901 | 0.5071 | ||
| School Funds | 0.5449 | 0.4017 | ||
| Contributions | 0.4962 | 0.5309 | ||
| Rights & Licensing | 0.4544 | 0.3377 | ||
| OtherRevenue | 0.4073 | 0.2891 | ||
| Total Revenue | 0.3250 | 0.2400 | ||
| Generated Revenue | 0.4521 | 0.3415 | ||
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