Sat, Sep 26 2026
·
Week 4
·
🏟 Stanford Stadium
Stanford, CA
·
Turf
·
50,424 cap
Georgia Tech✈ 2,122 mi-3 hr 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
Georgia Tech
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Georgia Tech entering this game.
Momentum Control
61.3%
Georgia Tech wins
Lean
Game Control
75.9%
Georgia Tech wins
Solid
Advanced Stats
PPA + Success Rate agree → Georgia Tech
· 73.9% ATS historically
↓ 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 | — | — | — | — |
Stanford 2026 Schedule
Stanford's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/29 | Stanford vs Hawai'i | +5.5 | — | — | — | — |
| Fri 9/4 | Stanford vs Miami | +28 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 9/19 | Stanford at Duke | +16 | — | — | — | — |
| Sat 9/26 | Stanford vs Georgia Tech | +11 | — | — | — | — |
| Sat 10/3 | Stanford at Wake Forest | +16 | — | — | — | — |
| Sat 10/10 | Stanford at Notre Dame | +31.5 | — | — | — | — |
| Sat 10/17 | Stanford vs Elon | -15 | — | — | — | — |
| Fri 10/23 | Stanford vs NC State | +11.5 | — | — | — | — |
| Sat 10/31 | Stanford at Louisville | +22.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 11/14 | Stanford at Virginia Tech | +9 | — | — | — | — |
| Sat 11/21 | Stanford at California | +15 | — | — | — | — |
| Sat 11/28 | Stanford vs SMU | +20.5 | — | — | — | — |
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
Both Agree
→ Georgia Tech
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?
Georgia Tech Edge
Georgia Tech +0.92
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Georgia Tech Edge
Georgia Tech +20.3
GC Edge (season-to-date)
Teams with this edge win 75.9% 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
Stanford
Tavita Pritchard #77
0–0 (0%)
· Yr 1 at school
OC
Terry Heffernan
Yr 1
#67
DC
Kris Richard
Yr 1
#68
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: Momentum Control is a great measure for predicting game outcome but NOT an 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: Game Control is another great measure for predicting game outcome but NOT an 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: Momentum Control is a great measure for predicting game outcome but NOT an 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: Game Control is another great measure for predicting game outcome but NOT an 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 ✓