Behind the Recruiting Rankings

These recruiting class rankings are an attempt to create as unbiased a rankings system as possible, while also providing a sort of live look at what’s going on in the college football recruiting landscape. The system is based on a formula that assigns a value to each player based on how Tom Lemming has him ranked and a few other factors. It is powered by a QlikView database that includes every player who is either rated in Tom Lemming’s magazine or is committed to an FBS program, while also allowing for easy sorting and dissecting of information.

On the most basic level, the formula assigns a value to each player, then determines the average value of a player in a given class, then multiplies that value by the number of commitments. In doing so it rewards both quality and quantity.

There are a few other factors regarding a player’s value in the system, beyond the number of stars he was awarded. These factors include:

  • Player’s ranking in Tom’s Top 100 (if applicable)
  • Player’s home state relative to the school’s expected recruiting zone
  • Player’s fit in the college’s offensive/defensive system

The first factor is pretty self-explanatory. If a recruit is ranked in Tom’s Top 100, he is worth more than the typical 4-5 star player. The No. 1 player is worth the most, followed by No. 2, etc. The point of this is to boost the value of the players who Tom has selected as his elite prospects, since not all 4-star recruits are created equal.

The latter two factors play a relatively small role, but they do have an impact. As far as the home state goes, the system applies a recruiting zone to each school, thereby applying a bonus for any player who is recruited from outside the typical zone. For example, Texas would have received a bonus for grabbing Jordan Hicks from the state of Ohio last year, since that’s generally not their territory. Every school has a unique zone, since Ohio State and Ohio have very different recruiting expectations. While it can’t necessarily be said that a 3-star player from New York is any better than a 3-star player from California, it’s, generally speaking, more impressive for a school like Cal to land a guy from New York since that isn’t really their territory. It’s more a statement of the college/coach doing something impressive than the player’s individual value.

Lastly, the formula takes into account how well a player fits into the team’s offensive and defensive systems. The logic here is that a dual-threat quarterback is more valuable to a spread offense like Michigan’s than a pro-style offense like USC’s. Likewise, linebackers are more valuable to a 3-4 defense than a 4-3 defense for obvious reasons.

The important thing to remember with these rankings is that they are a statement on how the class would rank if today were Signing Day and these were the final classes. So leading up to Signing Day, the rankings may omit some schools with high quality and low quantity. For example, at the time of the writing, Auburn is sitting just outside the Top 25 classes, despite getting commitments from some very good players. To account for this, I have created a related formula that projects a class, based on the quality of players they have so far. This formula assumes that every college will recruit players of a similar caliber and will finish with a similar number of recruits (while also accounting for a school like USC, which is subject to scholarship limits this season and thus will finish with fewer commitments than most schools). According to this formula, Auburn will sit in the top 15 by Signing Day.

Finally, both these formulas are linked to every high school commitment in the FBS. A 3-star cornerback committing to Fresno State can shake up both the current rankings and the projected rankings. Obviously a no-name player will have a smaller impact, but a commitment from a top 10 player can shoot a team up the charts, while a notable decommitment can cause a team’s stock to plummet. This system allows for up-to-the-minute assessment of recruiting classes, while also limiting subjectivity.

Formula created by CBS Sports Network Researcher Brian Raab.