What Freakonomics Tells Us About College Football

Alabama excels in all three predictors. Alabama is the chicken and these three factors are the egg. Games are almost already decided.

 

In the 1980’s Bill James began writing Baseball Abstracts that detailed advanced statistics and ways of producing wins in an unfair game.  This prompted the success of Billy Beane with the Oakland A’s, causing Moneyball to be published, and thereby producing a huge boom in statistical analysis for other sports.  I’ve written about my affection for Football Outsiders and Football Study Hall.

Recently, there’s been more analysis on basic parts of soccer, such as the most effective way to take care of market inefficiencies.  The first example is that any player purchased after a big showing at an international tournament will always be overpaid.  The second example is that Mexicans are far underpaid and Brazilians are almost always overpaid, apparently because Europeans don’t get excited about watching Mexicans as much as Brazilians (the book’s argument, not mine).

One of the most fundamental and most successful arguments of Soccernomics by Simon Kuper and Stefan Szymanski is that England is a failing national power in soccer because of three main characteristics: the size of the working class, number of international games played, and the GDP of the country.  Not surprisingly, one could come up with similar prediction variables to determine who would win in a football game.

Simon Kuper and Stefan Szymanski said that they could predict who would win a soccer match, and by how many goals on average they should win by, by comparing the three predictors’ values.  There was a high correlation, shown by a high R2 value, that the three predictors are very accurate in predicting the outcome of future international soccer games.

Now there is a clear crossover between international soccer and college football.  Over time the same teams tend to dominate the playing field.  The majority of players will play in only one World Cup, two max (4 years apart), for their team, just like a player will only play 4 years for a college. Lastly, there is no spending limit on their teams.

The English believe it's their right to win every world cup. Their tragedy is they don't. A shrinking working class, stagnant GDP, and few international friendlies lower their chances.

The first predictor in international soccer is the size of the working class, and this relates to the recruiting classes college football teams bring in.  The middle and working classes always produce athletes (which show that race is not a determinant of athletic potential contrary to whatever pseudo-science you believe in.  But race and sport is a topic covered in books that I may delve in to later.) The larger the middle class in a country, the more athletes to pick from and the greater the likelihood of getting superstars like Wayne Rooney, Lionel Messi, and Cristiano Ronaldo.

In the case of college football, the better recruiting class you bring in, the more likely players will pan out.  Sure there are the occasional Patrick Chung, Jeff Maehl, and Jerry Rice’s who weren’t highly touted recruits, but they are few and far between.  The odds of a 5-star recruit living up to the hype is much much higher.  We are only aware of the 5-star busts because there are so few 5 star ratings given out, so of course people are going to track the best players out of high school.  A common saying in college football is that it is not so much about the x’s and o’s as it is about the Jimmies and Joes.  For college football the five-year average of recruiting rankings is the best predictor number because players at a school their fifth year are peaking and no one is in school for more than five years (except for the Deltas).

Florida, Alabama, Texas, Oklahoma, LSU, and USC would end up in spots 1-6, which also happen to be six of the best football programs in college football.  Schools like Ohio State, Florida State, Georgia, and Auburn would be in spots 7-10.  When these top teams matchup with teams that didn’t do as well in recruiting, the other teams are already at a disadvantage and you’ll hear analysts talk about how much more talented one team is over another.  The truth of the matter is, the schools with better recruits have better odds of their players panning and being stars.

The second predictor is a country’s GDP based off population, or in college football a

If you think Texas spends a lot already on sports, wait until their Longhorn Network funds come in.

schools’ athletic budget on football.  While the working and middle classes produce athletes, poor countries don’t have enough funding to support their national team.  World powers like Spain have high GDP’s, a sign of spending ability, and are able to get the best trainers, coaches, and facilities for the players to perform at their best.  The same concept applies to college football.  The more money a school has designated to football the more it can outbid other schools to get the best coaches, get the best facilities and trainers to train players, put more funds to recruiting and getting players to sign with the school, providing academic support to keep players eligible, and to pay assistants to cut up film and work with positions.

Let’s take a look at the top 10 spenders in college sports.  Texas is first with over $120,000,000 annually.  Ohio State is second, Florida third, Michigan fourth with just under $100,000,000, Wisconsin fifth, Penn State sixth, Auburn seventh, Alabama eighth, Tennessee ninth, and Oklahoma State tenth (Thanks T Boone Pickens!).  If you made a list of the top ten football programs, almost all of those teams would be on the list.  This isn’t a chicken or egg issue, money directly results in more wins, just like recruiting.

One of the determining factors of winning in international soccer as shown by regression models is the number of games a national team has played.  One of the reasons, as theorized by the authors, that Asian, South American, and North American teams are catching up to European teams (especially England) is because their club leagues aren’t as strenuous and allow for more international friendlies to take place.  This slowly closes the gap between the number of international games Germany and England have played compared to the United States and Japan.

We could apply this same concept to college football and measure teams based on the number of wins a school has in its history.  The more wins a team has, the longer it’s been around and is an established brand in the college football world, the more tradition it has, and the more likely it is to draw coaches and players to play there.  Schools like USF that started in 1996, while lacking wins, have the benefit of a large budget and player pool, but they will be at a disadvantage when compared to older and historically successful programs.  Studies by Football Outsiders show that teams at the top in college football have been there since the game first spread throughout the country and are likely to stay there, and total wins are a good showing of a programs ability to be successful.

If we look at the winningest teams in college football history we get Michigan (877), Texas (845), Notre Dame (837), Nebraska (827), Ohio State (819), Alabama (813), Penn State (812), Oklahoma (796), Tennessee (783), and USC (774).  This predictor could be modified to be winning percentage instead of total wins, but I’m assuming you’d get roughly similar numbers across the board.

Now if you took all these numbers and put them in to a regression model or some sort of formula, you’d get a list of favored teams and underdogs, and you could look at a matchup, look at the predictions given by these three factors, and pick the winner almost every time.  International soccer differs from college football in that there aren’t as many games (as teams will play single digit friendlies a year and a huge tournament every four years) for teams to play, so there won’t be as much variance or volatility in team success in international soccer compared to college football, but these three factors are the ultimate predictors in a program’s success.

I’m interested in any comments people have on this topic.

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Rusty Ryan

About Rusty Ryan

Rusty once robbed three casinos at the same time with a team formed by Danny Ocean. He's also stolen the Corronation egg and crashed the GRECO security system, effectively ruining a casino. Laying low for the time being he now follows sports, betting, and pop culture a little too closely.