ESPN computers are never wrong! 

Really happy for Dabo that he’s still in the picture 

If your formula spits out Notre Dame still in the top 10, it might be time to make some adjustments. 

I don't want anymore rat poison, but I know its coming. Kirby Smart and Georgia is going to look to have a statement game in Austin on October 19th.

Unless bama gets A LOT better i see a couple of losses on their schedule because they looked putrid on offense and couldn’t stop the run against south florida. 

Nice, but rankings mean diddly 2 games in.  But...these rankings are better than if we were sucking, for sure.

18 minutes ago, qaertyisthatdude said:

Rat poison.

Yes sir it is ! 

Ditto

But it's such tasty rat poison.

ESPN FPI--the thing that had Penn State in the top three before the bowl games last year.

ESPN's mysterious and flawed FPI should be ignored by OTF. FPI isn't football science or an impartial computer formula; it is ESPN's secret proprietary hype ranking. UNI went on the road as a 28.5 pt dog and beat ND.  1-1 ND ain't #10.

41 minutes ago, Jerky said:

ESPN's mysterious and flawed FPI should be ignored by OTF. FPI isn't football science or an impartial computer formula; it is ESPN's secret proprietary hype ranking. UNI went on the road as a 28.5 pt dog and beat ND.  1-1 ND ain't #10.

Here’s a little more in depth explanation on FPI:

The model uses a Bayesian framework, using priors around the EPA rate of each team unit, derived from preseason expectations.

EPA is the foundation for FPI. Each game play has an adjusted EPA based on historical data. EPA breaks down points added in every way, thus having different factors for the team's offense, defense, and special teams units.

Generally, the offense and defense factors are independent.

FPI is applied to football both at the NFL level and at the college level, but their models are slightly different.

In college football, each team unit has its own prior. Four of the main inputs for each prior includes data on the last 4 seasons (with an emphasis on the previous season), the number of returning starters on the offense and defense (with the QB counting as more), a binary input on the returning coach, and the strength of the team's recruiting class (with an input for transfers). College FPI is more reliant on the priors in the model due to the regular occurrences of mismatches each week. The priors are based on the adjusted EPA so that no team dominates.

A little more info:

FPI accounts for team strength, opponent strength, and home-field advantage. There are a number of unique inputs into each game prediction, such as the following:

On-field performance in previous games: measured by adjusted expected points added by unit

Rest: Extra days of rest has shown to make a difference, particularly when facing a team coming off short rest.

Distance traveled: Extreme cases of long travel (e.g. Seattle to Miami) is worth about a half point per game, with all other factors equal.

These factors are combined to create a single-game prediction, but other factors are included based upon the type of league (college football vs the NFL). Each team's season is simulated 10,000 times to produce its chance to win its division, win its conference, make the playoffs, win the Super Bowl (NFL), pick any slot in the NFL draft, and more.

In 2016, FPI favorites won 73 percent of games in the regular season, which was a higher success rate than the Las Vegas closing lines. 

The models are pretty much meaningless after only 2 weeks of input.

Having said that, Sagarin's model has Texas at #2 and would be a 2pt favorite vs UGA in Austin (again, after only 2 weeks of data).

http://sagarin.com/sports/cfsend.htm

I used Sagarin's model to win a few college football pools against the line with a winning percentage of barely 50%.

He's been doing this for almost 30 years.  I've been following since 1998 when his model favored us against A&M.

Again, the models are pretty much meaningless after 2 weeks. 😎🤘

 

 

 

 

Pate State JP Poll is my go to!!   Anticipating my Chalice anytime now.   Hook 'em