Georgia Tech Football: Advanced Stats Preview - GT vs. NIU

We’re two days from kickoff, and it’s time to dust off the spreadsheets. We’ve had a busy offseason here at FTRS getting ready to bring you advanced stats data and analysis in 2021. Before we jump into the preview of the opening game, I want to draw your attention to a few places:

• Our own Akshay Easwaran is one of the principal designers and builders of a brand new website for college football advanced stats: gameonpaper.com/cfb. They’re producing excellent advanced box scores for every college football game, and it’s a great resource for you to follow CFB.
• I spent time this offseason putting together a predictive model for all of FBS. Each team has a rating that is equivalent to a point spread against an average FBS team. For the preseason version, the inputs include EPA/play, yards per play, and success rate for the past four years, team talent (according to the 247 Team Talent Index), and returning production. Each week during the season, the model will update based on season to date EPA/play, yards per play, and success rate, along with a conference/schedule strength adjustment. For now, you can follow me on twitter to see the model’s output each week, and hopefully we will have it hosted somewhere more permanent sometime soon. Here’s the preseason version: The Binion Index. I made a couple of small tweaks after posting that, and in the current version, GT checks in at #57, with a rating just above average of 0.78
• We’ll be producing similar statistics as last year exclusively for GT games using my game charting. Additionally though, I’ll have more individual player stats to share for the quarterback, the offensive line, and the defense. I’m excited to dig in even deeper to the team’s performance throughout this fall.
• We’ll be talking a lot about EPA - Expected Points Added - throughout the year, as we did last year. Check out this great primer from Jason DeLoach if you want a deeper understanding of what we’re talking about.

Without further delay, let’s jump into Saturday night’s action between Georgia Tech and Northern Illinois University. This is the first ever meeting between the two programs, and the amount of roster turnover that both teams have experienced in recent years makes it more difficult than normal to project. Still, let’s see what we can learn from a comparison of the teams’ advanced stat profiles from last year. Georgia Tech was a slightly below average team statistically in 2020. Northern Illinois was bad. UNI shows a slight advantage in a couple of the defensive disruption metrics. Otherwise, GT appears as the overwhelmingly superior team.

When GT Has the Ball

*GT numbers come from my play by play charting. Opponent numbers come from @CFB_Data and teamrankings.com

Air it out! Almost every GT analyst I’ve heard from has been saying the opposite: we’ve got four great running backs, so let’s run it down their throats! NIU looks about average at stopping the run, checking in right at the national median in run stuff rate, and they were just two tenths of a yard below the national median in yards per rush allowed. But they were shredded through the air. The Huskies allowed nearly first down yardage every single time an opposing quarterback threw the ball last year. Their EPA/play on defense was a ghastly 0.14, but that doesn’t tell the whole story. Against runs, they gave up 0.00 EPA play; against passes, that number was 0.29 EPA/play. That was 7th from the bottom in all of FBS over last year’s abbreviated season. For comparison, FSU was the worst ACC team in this metric last year at 0.24 EPA/play, and Jeff Sims was able to post a 61% success rate on pass plays against the Seminoles in his first ever college game.

NIU can’t defend the pass, and GT’s improvement this year rests heavily on taking steps forward in the passing game. This is an ideal opening matchup for Sims and company, especially considering GT’s best offensive player is an elite weapon catching passes out of the backfield.

My message to Coach Patenaude: pass to set up the run on Saturday night. Throw the ball on 55% or more of 1st down plays. Get Sims some confidence. Get the receivers in rhythm. Put points on the board in bunches.

When Northern Illinois Has the Ball

Once again, the rush/pass splits for the Husky offense are remarkable. On rushes, they put up -0.26 EPA/play. On average, they lost a quarter point every time they ran the ball. On pass plays, they were just below average, at -0.03 EPA/play. Against a team with every intention to throw the ball all game, this is a chance for the EDGE players to pin their ears back and for the secondary to play solid coverage, limiting the explosive plays that plagued GT last year.

If NIU runs the ball effectively, it simply does not bode well for the defense’s outlook this season. The strongest element of their rushing attack may well be scramble type plays from Rocky Lombardi. The Michigan State transfer has not been a very effective thrower of the football, but he was occasionally able to make plays with his legs. He carried 10 times for 65 yards against Northwestern in 2020 and 9 times for 53 yards against Nebraska in 2018, and he had a career-long carry of 47 yards against Ohio State in 2018. Can the pass rushers maintain gap integrity and the linebacking corps maintain eye discipline on potential scramble plays? If so, NIU should not be able to run the ball efficiently, and GT can bend its defense heavily towards stopping the pass. This will be a great test before matching up later in the season with teams who play a similar style but with vastly superior talent.

Prediction

The consensus Vegas line is GT by 18 as of this writing. That works out to about an 89% win probability. For comparison, GT was never favored by more than 8 points in any game last season.

As I mentioned earlier, I’ve built a model that will project every game featuring FBS teams this college football season. The Binion Index doesn’t just look at last year’s poor performance but gives some weight to success that Northern Illinois had enjoyed in previous years. Because of that, GT checks in as a 12 point favorite.

Subjectively, that feels low to me. I’m going against my model this week and picking GT to cover 18, 42-17.

Vegas: GT by 18

My Pick: GT by 25

The Binion Index: GT by 12

Year to Date Against the Spread: 3-2

Average Absolute Error: 15.0 points per game