"Just Say No!" to preseason rankings. Maybe. Or not. Whatever.

[Insert insane sports commentator cliche statement here] That's right, it's August, training camps are in full swing, and college football is less than a month away. Numerous (what? one is a number!) people have been asking if I'm going to put up my own pre-season rankings. Initially, I said no. My ranking system is based entirely on on-field performance, and with no performance data, there can be no accurate rankings. However, after some thought, I came up with an idea that just might work. Unlike the actual rankings, which is meant to indicate who is the best team, the preseason rankings should indicate who is likely to be the best team. My plan is to use the final rankings from last season as a starting point and then adjust them for the following factors: number of returning starters and changes in coaching staff. Look at it like this: if you were the best team last year, but you lost your head coach and half your starters, there's no reason to believe you'll be the best team anymore (unless every other team endured the same losses).
Some of the necessary information is easy to come by, some is harder, so if, as you're reading this, you know of a good source for any of this information, please let me know. I'd really like to find a reliable source rather than having to go through all 119 teams' homepages one at a time and individually analyze depth charts and coaches. Here's the information I plan to use and how I plan to use it:

• Head coach - if a team has a new head coach, they incur a 15% penalty unless the coach was either OC or DC during the previous season, then then incur a 5% penalty.


• Offensive coordinator - if this position has changed, a 5% penalty is incurred.


• Defensive coordinator - if this position has changed, a 5% penalty is incurred.


• Offensive starters - For each of the 11 starters from last season who is not returning, a 1% penalty is incurred.


• Defensive starters - For each of the 11 starters from last season who is not returning, a 1% penalty is incurred.


• Punter - if this position has changed, a 1% penalty is incurred.


• Kicker - if this position has changed, a 1% penalty is incurred.


• Kick returner - if this position has changed, a 1% penalty is incurred.

Each penalty is cumulative. By which I mean that the percentage points for each penalty are added together before being applied. So for example, if a team has a new OC and 19 returning starters, they would take a 8% hit to the final ranking from last year. In a worst case scenario, a team would only take a 50% hit, effectively relegating the best team to the middle of the pack, which, considering that we (numerically speaking) know nothing about the current team, seems fair enough.
There are a couple problems with this plan however:

• It punishes bad teams who have fired lousy coaches or who are replacing bad players


• It fails to take into account especially deep teams who may rotate lots of players in and out, resulting in many experienced players despite the loss of starters


• Not everything can be simply quantified. For example, a lost starting DE on team A might have been more valuable than a lost starting SS on Team B.

The first issue is probably fixable by inverting the penalty to be a bonus if a team had a poor rating last year, at least for a coaching change, but then the question becomes at what point do you make that cut off? Furthermore, is anyone really interested in how bottom rungs teams are rated preseason? Here's what I propose, to keep everything consistent, I'm going to cut their score the same as everyone elses, then if they improve, they'll have exceeded expectations and have something to be proud of. The second issue I have no solution for, so suck it up and prove my rankings wrong. The third issue, well, you're right. But since my rankings are supposed to be as impartial as possible (ie, depending on numbers instead of feelings), that's just the way it's going to be. As I stated at the beginning of this project, yes, I'm aware that by virtue of the fact that I wrote the algorithm, it's subject to my own biases right from the get go. The goal here is to come up with a plan that makes sense, and then rate everyone using the same formula to arrive at results that seem to jive with popular opionion on most counts. So, enough blabbing, go find me my source of all this data and rankings will be forthcoming once I've acquired everything I need.