Sat, Oct 31 2026
·
Week 9
·
🏟 Acrisure Stadium
Pittsburgh, PA
·
Turf
·
68,400 cap
Georgia Tech✈ 520 miSame TZ
Preseason projection — This game has not yet been played and 2026 in-season data is not yet available.
Edges are based on 2025 full-season performance.
Confidence will increase once in-season games are logged.
Matchup Prediction
Pittsburgh
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Pittsburgh entering this game.
Momentum Control
58.4%
Pittsburgh wins
Lean
Game Control
67.1%
Pittsburgh wins
Solid
Advanced Stats
Advanced factors are split · No strong agreement signal
↓ See full breakdown
Georgia Tech 2026 Schedule
Georgia Tech's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Thu 9/3 | Georgia Tech vs Colorado | -12 | — | — | — | — |
| Sat 9/12 | Georgia Tech vs Tennessee | +7 | — | — | — | — |
| Sat 9/19 | Georgia Tech vs Mercer | -28 | — | — | — | — |
| Sat 9/26 | Georgia Tech at Stanford | -11 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/10 | Georgia Tech vs Duke | -2.5 | — | — | — | — |
| Sat 10/17 | Georgia Tech at Virginia Tech | -5 | — | — | — | — |
| Sat 10/24 | Georgia Tech vs Boston College | -15 | — | — | — | — |
| Sat 10/31 | Georgia Tech at Pittsburgh | +6 | — | — | — | — |
| Fri 11/6 | Georgia Tech vs Louisville | +4 | — | — | — | — |
| Sat 11/14 | Georgia Tech at Clemson | +6 | — | — | — | — |
| Sat 11/21 | Georgia Tech vs Wake Forest | -2.5 | — | — | — | — |
| Sat 11/28 | Georgia Tech at Georgia | +23.5 | — | — | — | — |
Pittsburgh 2026 Schedule
Pittsburgh's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Pittsburgh vs Miami (OH) | -12.5 | — | — | — | — |
| Sat 9/12 | Pittsburgh vs UCF | -10 | — | — | — | — |
| Thu 9/17 | Pittsburgh vs Syracuse | -19 | — | — | — | — |
| Sat 9/26 | Pittsburgh vs Bucknell | -30 | — | — | — | — |
| Fri 10/2 | Pittsburgh at Virginia Tech | -8.5 | — | — | — | — |
| Sat 10/10 | Pittsburgh vs North Carolina | -14.5 | — | — | — | — |
| Sat 10/17 | Pittsburgh at Boston College | -13.5 | — | — | — | — |
| Sat 10/24 | Pittsburgh at Miami | +16 | — | — | — | — |
| Sat 10/31 | Pittsburgh vs Georgia Tech | -6 | — | — | — | — |
| — Bye Week — | ||||||
| Fri 11/13 | Pittsburgh vs Florida State | -4 | — | — | — | — |
| Sat 11/21 | Pittsburgh at Louisville | +5.5 | — | — | — | — |
| Sat 11/28 | Pittsburgh at California | -2 | — | — | — | — |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2025 season (prior year)
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
Split
Metrics disagree
Elite · 82.4% ATS
PPA + PPO + Havoc
Split
Metrics disagree
Elite · 73.9% ATS
PPA + Success Rate
Split
Metrics disagree
Individual Factors — Ranked by Predictive Strength
PPA Overall
Points added per play · Elite predictor
PPA Passing
Pass efficiency edge · Strong predictor
Havoc Total
Def. disruption rate · Strong predictor
TFLs, sacks, PBUs, forced fumbles — higher is better
Points Per Opp
Drive-finishing edge · Strong predictor
Success Rate
Play consistency edge · Solid predictor
Field Position
Avg start (lower=better) · Solid predictor
Avg yards from own endzone to average start — lower is better · longer bar = better field position
Advanced stats sourced from CFBD · 2025 season (prior year — 2026 data not yet available) ·
Edges are matchup-adjusted (offense vs opponent defense)
Power Ratings
Team Power Ratings
Overall · Offense · Defense ratings · Updated as season progresses
Power ratings updated throughout the season as results accumulate
Momentum Control (CSS)
Consecutive Scoring Sequences
Who builds scoring momentum?
Pittsburgh Edge
Pittsburgh +0.17
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Pittsburgh Edge
Pittsburgh +13.8
GC Edge (season-to-date)
Teams with this edge win 67.1% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
Georgia Tech
Brent Key #62
27–20 (57%)
· Yr 5 at school
OC
George Godsey
Yr 1
#67
DC
Jason Semore
Yr 1
#42
Pittsburgh
Pat Narduzzi #40
80–61 (57%)
· Yr 12 at school
OC
Kade Bell
Yr 3
#20
DC
Randy Bates
Yr 3
#100
About these metrics
Advanced Stats shows matchup-adjusted factor edges (offense vs opponent defense). Combination signals — when PPA, PPO, Success Rate, and Havoc all point the same direction — have historically predicted the SU winner in 95–97% of games and the ATS winner in 82–83% of games (2021–2025, FBS vs FBS, regular season).
Impact: Advanced Stats are the best performance based metric used to predict the outcome of games. ✓
Momentum Control (CSS) measures consecutive scoring sequences — when a team scores, holds the opponent scoreless, then scores again. Teams entering a game with a CSS edge of +1.0 or more have won 71–78% of games historically (2021–2025, FBS vs FBS).
Impact: CSS is not a predictive ATS advantage, data shows this is already considered when lines are set. ✗
Game Control (GC) measures win probability dominance — how thoroughly a team controlled the game from start to finish. Teams with a GC edge of +12 or more have won 67–76% of games historically. When both metrics agree, combined confidence is higher. When they split, treat as a lean at best.
Impact: GS is not a predictive ATS advantage, data shows this is already considered when lines are set. ✗
Power Ratings are a custom-built composite of a Teams Talent, Experience & Production, Coaching & Performance Metrics. These are updated constantly with roster changes, performance once the games start for the 2026 season, injuries the team is dealing with and scheduling situations.
Impact: There are a wide range of power ratings available, we think ours is the best, you can decide for yourself ✓
Advanced Stats shows matchup-adjusted factor edges (offense vs opponent defense). Combination signals — when PPA, PPO, Success Rate, and Havoc all point the same direction — have historically predicted the SU winner in 95–97% of games and the ATS winner in 82–83% of games (2021–2025, FBS vs FBS, regular season).
Impact: Advanced Stats are the best performance based metric used to predict the outcome of games. ✓
Momentum Control (CSS) measures consecutive scoring sequences — when a team scores, holds the opponent scoreless, then scores again. Teams entering a game with a CSS edge of +1.0 or more have won 71–78% of games historically (2021–2025, FBS vs FBS).
Impact: CSS is not a predictive ATS advantage, data shows this is already considered when lines are set. ✗
Game Control (GC) measures win probability dominance — how thoroughly a team controlled the game from start to finish. Teams with a GC edge of +12 or more have won 67–76% of games historically. When both metrics agree, combined confidence is higher. When they split, treat as a lean at best.
Impact: GS is not a predictive ATS advantage, data shows this is already considered when lines are set. ✗
Power Ratings are a custom-built composite of a Teams Talent, Experience & Production, Coaching & Performance Metrics. These are updated constantly with roster changes, performance once the games start for the 2026 season, injuries the team is dealing with and scheduling situations.
Impact: There are a wide range of power ratings available, we think ours is the best, you can decide for yourself ✓