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Economists Like Taking The Fun Out Of It

Economists at Mercer University are putting their skills to good use.  Never mind the market, I'm talking about predicting recruiting commitments:

The authors used statistical software developed by SAS along with data provided by (a national website dedicated to college football and recruiting) in the development of this model. The model was built on a database capturing characteristics and decisions of 3,395 recruited athletes for the three "recruiting seasons" between 2002 and 2004. On average, each player was choosing from among a group of 4 schools. A wide array of player and team level data were gathered for this task. Then, a special form of a probit model was developed to capture, to the best extent possible a statistical equation to capture the decision making process.

They go on to detail some of the things that appear to matter to recruits, and of course one's that don't.

[F]actors like the school’s graduation rate, the number of Bowl Championship Series (BCS) bowl appearances, the current roster depth at the recruited player’s position, the number of players from a specific college drafted by the NFL, and even the number of national championships won by a particular program don’t systematically influence the decisions of high school athletes.

This is (as they later suggest) somewhat surprising.  Graduation rate and BCS appearances are easy enough to discount.  Most top recruits are interested in going pro in something other than sports. Grad rates aren't a big part of that.  As for BCS appearances, they do seem to have lost their luster.  The Capital One Bowl actually posted better rating than the Orange Bowl, and with the addition of the 5th game, getting into one of these contests isn't a de facto reason to think a team is elite.

The others are a bit more interesting.  An MNC is a reason to think a team (and coaching staff) are very good.  There is talk about creating "NFL pipelines" and using that as a recruiting sell, but the guys at Mercer aren't buying it.

The biggest surprise to me is the idea that "current roster depth" isn't an issue, especially when so many top recruits talk specifically about how they want to "get in there and play".  This seems to be a more recent trend, though, and maybe part of the problem is the age of the data.  Additionally, this may not be quite as important to the 150-250 range of recruits, which could balance things out a bit.

So, attributes that do matter (according to the report, that is):

  • Whether the athlete made an "official visit" to a specific college                      
  • Whether the school is in a BCS conference                             
  • The distance from the high school athlete’s hometown to a specific school          
  • Whether the recruit is in the same state as a specific school
  • The final AP Ranking of a specific school in the previous year of competition            
  • The number of conference titles a school has recorded in recent years
  • Whether the school is currently under a "bowl ban" for violating NCAA rules                    
  • The current number of scholarship reductions a school faces for violating NCAA rules             
  • The size of the team’s stadium (measured in terms of seating capacity)
  • Whether the school has an on-campus stadium                       
  • The current age of the team’s stadium   
  • Location, school status and stadium are all tangible, common sense things.  Conference titles indicates how successful a team has been playing recently and it makes sense that recruits are drawn to programs that appear at the top of their game.  One thing that I think is worth a look is the impact of a new coach, which seems to very often carry a 1-3 year spike in recruiting.

    They report that the model predicts with a 70-75% accuracy rate.  Not bad, I think.

    So the fun part: what does Penn State's run up to signing day look like?

    Rank - Player Confidence (0 to 1) Rank  Points Ahead (Behind)
    3 - Jelani Jenkins 0.451 1st 17.9
    86 - Tajh Boyd 0.064 6th (22.8 )
    129 - Peter White 0.102 3rd (18.3)
    194 - Justin Brown 0.362 1st 14.5
    211 - Isaac Holmes 0.068 5th (20.1)

    And more fun with charts; schools who have "lost" players they were predicted to get, and the number of players each of those schools has "poached", or obtained verbals from who were predicted to go to another school.

    School Lost Poached
    Penn State 2 0
    Pitt 0 0
    Rutgers 0 0
    Ohio State 1 3
    Michigan 1 3
    USC 3 3
    Florida 6 0
    Notre Dame 0 2
    Alabama 3 3

    Keep in mind this is a double edged sward.  If you are performing at a very high level in a very rich recruiting state (Florida, for example), you are going to be predicted to take home an awful lot of talent; more than is probably reasonable.

    Penn State's two losses are to Ohio State, who are pretty much getting whoever they want at this point.  Also, notice the big goose eggs next to Pitt and Rutgers.  Penn State who?