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

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GT-Pitt is the definition of a toss-up on paper. Where can GT find a winning edge?

COLLEGE FOOTBALL: NOV 02 Pitt at Georgia Tech Photo by Rich von Biberstein/Icon Sportswire via Getty Images

I wrote on Tuesday that Georgia Tech’s performance against UNC constituted proof of concept for Coach Collins and his staff. This week is the opportunity to consolidate those gains against another solid conference opponent. Though GT comes in at 2-2, the underlying stats show the profile of a 3-1 team.

The defense has been excellent three straight weeks. The offense was efficient with Jordan Yates but added the missing explosiveness component with the return of Jeff Sims. Pittsburgh has the original Super Senior back at quarterback in Kenny Pickett and its usual stout defensive front. This will be a great test for the Jackets.

Let’s dig in to understand the matchup more deeply and look for some points of relative advantage and disadvantage for GT.

When GT Has the Ball

GT Offense vs. Pitt

Metric GT Offense 2021 Pitt Defense 2021 Advantage National Average
Metric GT Offense 2021 Pitt Defense 2021 Advantage National Average
Success Rate 44% 35% Pitt 42%
YPP 5.1 4.9 Pitt 5.7
EPA/Play 0.13 -0.07 GT -0.01
EPA/pass 0.07 -0.06 GT 0
EPA/run 0.2 -0.08 GT -0.01
YPA (including sacks and scrambles) 5.8 7.5 Pitt 7.4
3rd Down Success 34% 37% Pitt 42%
Red Zone Success 35% 35% Pitt 46%
Run Stuff Rate 13% 18% GT 19%
Havoc Rate 13% 21% GT 21%
*GT numbers come from my play by play charting. Opponent numbers come from @CFB_Data and teamrankings.com

Last year, Pitt’s defense was overwhelming for the GT offense. They got pressure on 36% of Sims’s drop backs, put up a 23% havoc rate, and held GT to a 35% success rate. The Panthers defense is still no slouch but probably isn’t quite the dominant force of last year. Most importantly will be whether GT can block Calijah Yancey and Habakkuk Baldonado up front. Consistent pressure has not been a friend to Jeff Sims. GT must be able to help him out by throwing in non-obvious passing situations and prevent pressure when doing so.

The running game looks to be a significant advantage for GT. Run stuffs will set this offense up for some dangerous situations, but especially with the threat Sims adds running the ball, the offense could find some chunk plays on the ground. I’m going to keep repeating myself until it happens, but we really need to see Dontae Smith in the second running back role and to share the field with Gibbs. Those combinations have electric potential, and Coach Patenaude seemed to realize that in the second half against UNC. GT should be able to move the ball consistently, but one issue will remain: can they finish drives?

We’re debuting a new metric this week; red zone success simply captures success rate when inside the opponent 20. As you may have guessed, GT is significantly worse than the national average so far this season. This could be another difficult week to produce in the red zone, so look for the offensive coaches to try and create some more explosive touchdown plays after crossing the Pitt 40.

When Pitt Has the Ball

GT Defense vs. Pitt

Metric GT Defense 2021 Pitt Offense 2021 Advantage National Average
Metric GT Defense 2021 Pitt Offense 2021 Advantage National Average
Success Rate 41% 51% Pitt 42%
YPP 4.2 6.4 GT 5.7
EPA/Play -0.1 0.22 Pitt -0.01
EPA/pass 0.04 0.4 Pitt 0
EPA/rush -0.24 0 GT -0.01
YPA 5.9 9.4 Pitt 7.4
3rd Down Success 49% 41% Pitt 42%
Red Zone Success 43% 47% GT 46%
Stuff Rate 19% 26% GT 19%
Havoc Rate 13% 18% Pitt 21%

Seeing the balance in matchup advantages is getting me really excited for this game. We could be in for a special one.

Again, GT looks to have the significant advantage in the running game, so we’re likely to see Kenny Pickett do his thing and air it out. I’d expect somewhere in the neighborhood of 40 pass attempts. GT has done a wonderful job limiting explosive passing plays the last two weeks, and the challenge will continue on Saturday. A passing EPA of 0.40/play is magnificent, but we do need to provide context. According to ESPN’s FPI metric, Pitt has faced the 102nd most difficult schedule to date (GT’s schedule ranks 18th). Against Pitt’s one P5 opponent, Tennessee, Pickett was fine but put up much more reasonable numbers. I don’t expect him to be lights out, but the question remains: is Pickett able to find Jordan Addison deep, or does GT’s 3-3-5 continue to cause confusion and disruption for opposing quarterbacks?

After seeing the creativity that Coach Thacker has shown in deploying his personnel against KSU, Clemson, and UNC, I like GT’s chances to throw some difficult stuff at Pickett. Pitt is susceptible to havoc plays, so Charlie Thomas and Jordan Domineck could find some opportunities.

Prediction

The consensus Vegas line opened with Pitt favored by 3 and has moved up to 3.5, which translates to a 60% win probability for the Panthers.

The Binion Index, which is the college football projection model that I created and that we are hosting on FTRS, likes GT’s chances a bit better. Because of its strong play by play performance relative to the competition, TBI has GT rated higher than some other outlets, and Pitt’s numbers come in just ahead of GT. With that being said, the model projects a 0.26 point Pitt victory; that’s the definition of a tossup.

Pitt’s statistical profile is slightly misleading because of their extremely weak schedule, and the Binion Index accounts for that. Jeff Sims also gives GT an offensive upside that would far surpass the total season to date offensive metrics for the Jackets. I like GT to cover and win straight up in a nail biter.

Vegas: Pitt by 3.5

My Pick: GT 31-28

The Binion Index: Pitt by 0.3

Year to Date Against the Spread: 109-89-5 (Goal: >=55%)

Average Absolute Error: 13.7 points per game (Goal <= 12.5 points per game)