Blake Munroe

Start your week off with the OTF crew by joining us for Coffee and Football at 8:10 AM. Bobby, Gerry and I will be discussing the Texas news of the day and taking your questions.

This. Let’s hear it, Gerry. Bobby said on C&F that TAMU takes it, if I recall correctly. I’m the exact opposite. Florida wins in one of the ugliest games of the year.

Texas is now 2-0, with both a dominating home win over a G5 and a good ol’ fashioned butt whipping of a win against a (then ranked) No. 10 Michigan team on the road. In those two games the Longhorns have: - Outscored their opponents 83-12. - Averaged 301 passing yards per game - Averaged 197 rushing yards per game. - Allowed 139 yards through the air, on average, per game. - Held opponents to 99 rushing yards, on average, per game. So, now that we are two games in, have your expectations for this team changed? Why or why not? And, if so, how have they changed?

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.

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.

The fact Michigan only converted 25% of their 3rd downs, while Texas converted nearly 63% is crazy.

Scoring Summary

Special Team Stats

The Longhorns defeated Michigan on Saturday 31-12. Texas, who is now 2-0, simply controlled the game from the start, posting 389 yards on offense while holding Michigan to 284. Offensive Stats Defensive Stats Team Stats

Bobby is in Ann Arbor, Gerry is in Florida and I'm in Texas. Does that help? lol

I scheduled this post haha…. But was up at 4:55!

Join us for Coffee and Football to gear up for Texas-Michigan weekend! Show starts at 8:10 AM and we will be taking your questions plus discussing the big game! We will also be joined by Aaron McMann of MLive. Aaron knows a thing or two about Michigan and will give you his analysis of the game.