The Cost of Scheduling a Sacrificial Lamb for Major College Football Programs

Danny Kanell of ESPN has a great idea.  Make the non-conference exhibition games we just witnessed this weekend part of spring football practice.  The lesser schools who need the money to fund their athletic programs can still get paid and the games, as lopsided as they might be, are certainly more entertaining than a spring practice game.

There were 14 non-conference games between top 25 college football programs this weekend with an average score of 49 to 10.  In the 5 conference games involving top 25 teams the average score was 35 to 18.

Darren Rovell of ESPN regularly reports the financial payouts that major college football programs make to smaller programs when they come to play and often lose by 4 touchdowns or more.  For example, this week Savannah State was a 59.5 point underdog at the University of Miami.  Savannah State is receiving $375,000 for travelling a little over 400 miles to Miami to be one of the biggest underdogs in the history of NCAA football.

By my count, Rovell has reported 26 of these non-conference payouts over the past 3 weekends.  Over the past 3 weekends:

  • The average payout received by a visiting team is $613,000.
  • The average distance travelled by a visiting team is 530 miles.
  • The average Sagarin rank of a visiting team is 145 of 247 (end of 2012 season)
  • Only 31% of visiting teams ranked in Sagarin’s top 100 (end of 2012 season)

I estimated a simple regression of the relationship between a visiting team’s payout, its Sagarin rank and the distance travelled to the game.  I found no evidence that higher ranked non-conference opponents commanded a higher payout, but each 10% increase in distance travelled is associated with a 1.8% higher payout.

So while major college football programs can bring in a lesser team to beat up in front of season ticket holders for about $600,000, they should expect to pay about $205 more per mile travelled for each sacrificial lamb.

Pay College Football Players Rather Than Spending Millions on College Coaches

Elite college football coaches are the biggest beneficiaries of the NCAA’s prohibition of salaries or stipends for college football players.  College football is a big business generating billions of dollars per year in television revenue.  The way to build a better college football program is not to buy better players, because that is prohibited.  The way to become a college football power is to attract one of the best coaches who will assemble a highly paid staff and use millions of dollars in resources to recruit players by offering them non-wage benefits.  Top coaches are incredibly expensive because they can deliver the best players.  It is time for college presidents to face the truth, eliminate the coach as middleman and pay college football players directly.

Nick Saban, coach of the top-ranked Alabama Crimson Tide, is the highest paid coach in college football earning over $5 million per year.  He also lives and works in Tuscaloosa, Alabama where housing costs $92 per square foot.  In other words, Saban’s annual pre-tax salary is enough to purchase 57,609 square feet of housing every year making him the highest paid coach in college football.  Based on the median size of a new home in the U.S., Saban’s salary would purchase 26.56 new homes per year or a new home every two weeks.

The table below shows the salaries of the coaches of the top 25 ranked teams in the country going into this weekend.  The average coach of a ranked team earns enough (before taxes) each year to buy 11.85 per year.  Using this metric the lowest paid coach in this group is Stanford’s David Shaw whose pre-tax salary would just be enough to purchase one home per year in Palo Alto.  Measured relative to housing costs Saban’s salary is 26 times higher than Shaw’s.

Coach School Relative Salary(# homes/yr) Rank
Nick Saban Alabama Crimson Tide 26.56 1
Bob Stoops Oklahoma Sooners 24.77 13
Brian Kelly Notre Dame Fighting Irish 20.64 7
Mark Richt Georgia Bulldogs 20.37 14
Will Muschamp Florida Gators 16.62 4
Les Miles LSU Tigers 16.35 9
Steve Spurrier South Carolina Gamecocks 13.56 3
Jimbo Fisher Florida State Seminoles 12.35 12
Dan Mullen Mississippi State Bulldogs 12.23 19
Urban Meyer Ohio State Buckeyes 11.60 8
Chris Kelly Oregon Ducks 11.44 2
Kevin Sumlin Texas A&M Aggies 11.27 22
Charlie Strong Louisville Cardinals 11.07 18
Bill Snyder Kansas State Wildcats 11.01 6
Lane Kiffin USC Trojans 10.87 11
Chris Peterson Boise State Broncos 9.81 24
Dabo Swinney Clemson Tigers 9.32 16
Mack Brown Texas Longhorns 9.10 15
Brady Hoke Michigan Wolverines 8.35 25
Butch Jones Cincinnati Bearcats 7.83 21
Dana Holgerson West Virginia Mountaineers 6.90 5
Mike Riley Oregon State Beavers 5.50 10
Kyle Flood Rutgers Scarlet Knights 4.02 20
Sonny Dykes Louisiana Tech Bulldogs 3.75 23
David Shaw Stanford Cardinal 1.03 17

75 college football coaches earn at least one million dollars per year because of lucrative TV contracts for their schools.  As noted above, the NCAA prohibition on payments or stipends to players means that competition for players and recruits inflates coaches’ salaries.  College presidents would rather pay high salaries to coaches than allow direct payments to players.  Nick Saban, Bob Stoops, Brian Kelly and other top coaches earn economic rents because of the restrictions on payments to players.  Rival programs could compete more effectively with Alabama, Oklahoma and Notre Dame if they could pay recruits.  This type of direct competition for recruits would drive up salaries of college athletes and drive down the salaries of college coaches.  College presidents should be honest with the public, admit that college football is a big business, and stop funneling the revenue generated by players to college football coaches.  Pay the players directly.  It is more efficient than paying millions of dollars per year to coaches and recruiters.

Note: Housing prices in college towns are from the Wall Street Journal and Trulia.  Coaches’ salaries are from Coaches’ Hot Seat.

To Punt or not to Punt: Policy Advice based on Observational Data

Rocky Long, second year head football coach of the San Diego State Aztecs, is facing a problem that is similar, in some respects, to what voters face.  Coach Long may choose to never punt the football once the Aztecs cross midfield, regardless of distance on fourth downs.  He is listening to advice suggesting that the strategies of many previous coaches were wrong.  Coach Long says “We had one of our professors in our business school … go over a system we are thinking about using.  We’ll have a chart come game time that will determine what we do in different situations.”

Voters must decide whether the policies of another Obama Administration will help turn the economy around and contribute to more job creation, or if the Romney campaign proposals will be more successful in achieving these goals.  Football and economic policy advice are both derived from observational, not experimental, data.  It is difficult to know what might happen if alternative policies were to be enacted.  Analysts look to history, in similar circumstances with similar policy options, and hope this provides useful guidance.

Coach Long’s decision apparently follows the strategy of high school coach Kevin Kelley of Pulaski Academy in Little Rock, Arkansas who has been remarkably successful.  But college football is not high school football.  The strategy also appears similar to advice given by Berkeley macroeconomist David Romer, who several years ago found that NFL teams kick surprisingly often on fourth down.

Romer concluded that teams are not pursuing strategies that would maximize their chance of winning the game.  Romer may be correct, but we should be cautious because his study is based purely on observational data.  It is possible that the real world problem is more complicated than the model used to analyze the data.  Whenever economics (or other social science) professors explain that agents motivated by self-interest are making choices not in their self-interest, the professors may have mis-specified their model or may misunderstand the real-world problem that people are attempting to solve.

Romer’s paper is interesting, well written, and well executed.  All of the criticisms raised here are ones that Romer acknowledges, but they aren’t enough to sway his opinion and policy advice.  The data Romer analyzed indicated that NFL teams rarely try for a first down on fourth down.  The primary question is why?  Romer’s explanation is myopia.  An alternative explanation is that a failed fourth down attempt will shift momentum in the game.

The key problem with observational data is that it is difficult to calculate the expected outcome from counterfactual decisions and policies.  Romer argues that teams are not optimizing because he believes they would be successful fairly often on fourth down and two yards to go.  He concludes this despite rarely seeing teams attempting this play.  So how does Romer “guesstimate” the likelihood of success on fourth down and two?  He looks at outcomes of third down plays with two yard to go in the first quarter of games.  He uses first quarter plays because once enough game time has elapsed and the score is uneven, both teams will adjust their game strategies.  He assumes that third down strategies and outcomes are very similar to what would happen on fourth down plays (if they actually were to occur) because he doesn’t have enough data on fourth down plays.  Even if he observed more fourth down plays, they would not be a random or representative sample of teams and/or game situations.  Romer’s assumptions are made out of necessity, not because they are realistic or accurate.

It is also important to note that Romer’s empirical model does not allow for momentum.  The value of having possession of the ball with first down and ten yards to go on a given yard line (say midfield) in his empirical model is completely independent of how one reached that position on the field.  In the language of dynamic programming, the state variables for the optimization problem include down and yardage, but not the sequence of plays leading up to that point.  He imposes this assumption, as good applied economists often do, to make the problem tractable.  He recognizes that momentum could matter and makes some supplemental calculations to show that teams don’t perform much differently immediately after very bad plays (fumbles, interceptions, blocked kicks, and long kickoff and punt returns by the opponent) and just after very good plays (touchdowns) than they do after typical plays.  This is, at best, a half-hearted attempt to determine whether there is momentum in NFL games.  The model simply doesn’t take the idea of momentum shifts seriously, but avoiding these shifts seems to be a key reason why coaches kick field goals rather than go for it on fourth down.

Rocky Long, at San Diego State, may follow the advice of economics and business school professors and forsake the punt.  But he should do so understanding how the policy advice was determined.  It is extremely difficult for economics professors to evaluate counterfactual policies- whether it is a forecast of what will happen if teams ran and passed on fourth down or a “guesstimate” of the 2012 unemployment rate had there been no stimulus package in 2009. 

Asking economics professors, the Congressional Budget Office or other forecasters to evaluate alternative policies and predict what might happen over the next decade also has limited value.  Many of these same professionals either didn’t forecast the recession or underestimated its severity.  Government economists and advisors didn’t know how deep the downturn was until a year or two later when the data came in.  Mis-estimation of the recession in the midst of the downturn is the explanation given for the woefully inaccurate prediction that the stimulus would keep the unemployment rate below 8%.

It is unwise to rely too heavily on economists as authorities on counterfactual policies.  Economists can’t easily determine what would have happened had there been no stimulus, or how the economy might perform if taxpayers earning more than $200,000 were to face higher marginal tax rates.  In fact they struggle to measure output and employment in real-time.  Predictions about hypothetical economic policies are as fraught with error as predictions about fourth down decisions that have rarely been tried in the past.

Alabama and Stanford Provided the Most Valuable Talent to the NFL in the 2012 Draft

Many NFL teams use the relative valuation of draft selections established by the Dallas Cowboys to evaluate possible trades for “moving up” or “moving down” in the draft.  According to the Cowboys’ chart, the top pick in the draft is worth more than 5 times as much as the first pick in the second round (the 33rd pick) and ten times as much as the first pick in the 3rd round (the 65th selection).  The value of players declines exponentially because there are more substitutes for less skilled players and the NFL imposes a minimum salary schedule.

These values can be used to determine which positions are expected to provide the NFL with the most valuable talent in the just completed 2012 Draft.  The following chart shows that quarterbacks, wide receivers, defensive ends and cornerbacks, despite representing less than one third of a team’s positions, are expected to provide almost half of the value in the draft.  This is a clear indication that the NFL has become a pass first league.

Position Share of Draft Value Share of Players Selected
Quarterback

14.12%

4.35%

Wide Receiver

13.14%

13.04%

Defensive End

10.26%

7.51%

Cornerback

10.17%

11.86%

Defensive Tackle

10.10%

9.88%

Offensive Tackle

8.19%

7.11%

Running Back

7.95%

7.51%

Offensive Guard

6.00%

8.30%

Outside Linebacker

5.83%

9.09%

Safety

5.46%

7.91%

Inside Linebacker

5.02%

3.56%

Tight End

2.20%

4.35%

Offensive Center

0.90%

1.98%

All Other Positions

0.66%

3.56%

The chart also shows that quarterbacks are selected much earlier in the draft (their share of draft value is more than three times their share of players selected) than other positions.  Safeties, inside linebackers, tight ends and centers are selected later, on average, than other positions.

The draft values used by NFL teams also can determine which college teams provided the NFL with the most valuable talent in expected value terms) in the 2012 draft.  Alabama had the most players selected (8 out of 253) and are expected to be the source of almost 10% of the value of the 2012 draft.

Position Share of Draft Value Share of Players Selected
Alabama

9.85%

3.16%

Stanford

7.86%

1.58%

Baylor

5.84%

1.98%

LSU

5.34%

1.98%

South Carolina

4.41%

2.37%

USC

4.12%

1.19%

Oklahoma State

4.11%

1.19%

Illinois

3.62%

1.58%

Notre Dame

3.05%

1.58%

Boise State

2.80%

2.37%

College football players were drafted out of 105 different colleges and universities.  The ten schools listed above accounted for 19% of the players and 51% of the value of the talent in the draft.  Only twenty schools accounted for 32.4% of the players and 71.5% of the value in the draft.

The pattern of players selected in the draft emphasizes the high value that NFL General Managers place on both passing offense and defense.  It is difficult to forecast the value of professional football players based on their performance in college and the NFL combine.  Many of the quarterbacks selected in the 2012 draft will underperform relative to expectations, while others will outperform quarterbacks selected ahead of them.  An NFL  team, such as the Miami Dolphins, is willing to take a big chance in the draft on a quarterback because the right player can elevate a team’s performance and profitability for a decade.

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