Sat, Oct 5 2024
·
Week 6
·
🏟 Stanford Stadium
Stanford, CA
·
Turf
·
50,424 cap
Virginia Tech✈ 2,274 mi-3 hr TZ
Matchup Prediction
Virginia Tech
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Virginia Tech entering this game.
Momentum Control
73.7%
Virginia Tech wins
Solid
Game Control
49.4%
Virginia Tech wins
Toss-up
Vegas Spread
Virginia Tech -9.5
O/U 50.0
DraftKings
Advanced Stats
All 4 factors agree → Virginia Tech
· 83.1% ATS historically when all four align
↓ See full breakdown
Virginia Tech 2024 Schedule
Virginia Tech's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | Virginia Tech at Vanderbilt | -13.5L27–34 | 53.5 | L27–34 | O | N |
| Sat 9/7 | Virginia Tech vs Marshall | -20.5W31–14 | 52.5 | W31–14 | U | N |
| Sat 9/14 | Virginia Tech at Old Dominion | -15.5W37–17 | 47.5 | W37–17 | O | Y |
| Sat 9/21 | Virginia Tech vs Rutgers | -3.0L23–26 | 45.0 | L23–26 | O | N |
| Fri 9/27 | Virginia Tech at Miami | +17.5L34–38 | 55.5 | L34–38 | O | Y |
| Sat 10/5 | Virginia Tech at Stanford | -9.5W31–7 | 50.0 | W31–7 | U | Y |
| — Bye Week — | ||||||
| Thu 10/17 | Virginia Tech vs Boston College | -8.5W42–21 | 47.5 | W42–21 | O | Y |
| Sat 10/26 | Virginia Tech vs Georgia Tech | -11.0W21–6 | 52.0 | W21–6 | U | Y |
| Sat 11/2 | Virginia Tech at Syracuse | -3.5L31–38 | 54.0 | L31–38 | O | N |
| Sat 11/9 | Virginia Tech vs Clemson | +6.5L14–24 | 54.5 | L14–24 | U | N |
| — Bye Week — | ||||||
| Sat 11/23 | Virginia Tech at Duke | -2.5L28–31 | 45.5 | L28–31 | O | N |
| Sat 11/30 | Virginia Tech vs Virginia | -4.5W37–17 | 44.5 | W37–17 | O | Y |
| Fri 1/3 | Virginia Tech vs Minnesota | +10.0L10–24 | 43.5 | L10–24 | U | N |
Stanford 2024 Schedule
Stanford's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 8/30 | Stanford vs TCU | +8.0L27–34 | 58.5 | L27–34 | O | Y |
| Sat 9/7 | Stanford vs Cal Poly | -33.5W41–7 | 59.5 | W41–7 | U | Y |
| — Bye Week — | ||||||
| Fri 9/20 | Stanford at Syracuse | +9.5W26–24 | 56.5 | W26–24 | U | Y |
| Sat 9/28 | Stanford at Clemson | +24.0L14–40 | 58.0 | L14–40 | U | N |
| Sat 10/5 | Stanford vs Virginia Tech | +9.5L7–31 | 50.0 | L7–31 | U | N |
| Sat 10/12 | Stanford at Notre Dame | +22.5L7–49 | 45.5 | L7–49 | O | N |
| Sat 10/19 | Stanford vs SMU | +16.5L10–40 | 52.5 | L10–40 | U | N |
| Sat 10/26 | Stanford vs Wake Forest | +3.0L24–27 | 53.0 | L24–27 | U | Y |
| Sat 11/2 | Stanford at NC State | +10.0L28–59 | 46.5 | L28–59 | O | N |
| — Bye Week — | ||||||
| Sat 11/16 | Stanford vs Louisville | +21.0W38–35 | 57.5 | W38–35 | O | Y |
| Sat 11/23 | Stanford at California | +15.0L21–24 | 53.5 | L21–24 | U | Y |
| Fri 11/29 | Stanford at San José State | +2.5L31–34 | 54.5 | L31–34 | O | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2024 season
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
All 4 Agree
→ Virginia Tech
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Virginia Tech
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Virginia 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 · 2024 season ·
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?
Virginia Tech Edge
Virginia Tech +1.67
CSS Edge (season-to-date)
Teams with this edge win 73.7% of games historically
Based on 3 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Virginia Tech Edge
Virginia Tech +0.3
GC Edge (season-to-date)
Teams with this edge win 49.4% of games historically
Based on 4 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Virginia Tech, but the GC edge is small. When metrics agree but GC is near-neutral, the agreed-upon team has covered only 46.7% of the time historically (n=224) — potentially a fade signal.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Virginia Tech
Brent Pry #1
10–14 (42%)
· Yr 3 at school
OC
Tyler Bowen
Yr 3
#1
DC
Chris Marve
Yr 3
#1
Stanford
Troy Taylor #1
3–9 (25%)
· Yr 2 at school
OC
Troy Taylor
Yr 2
#1
DC
Bobby April III
Yr 2
#1
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 ✓