Sat, Sep 19 2026
·
Week 3
·
🏟 Maryland Stadium
College Park, MD
·
Turf
·
51,802 cap
Virginia Tech✈ 224 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
Maryland
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Maryland entering this game.
Momentum Control
58.4%
Maryland wins
Lean
Game Control
67.1%
Maryland wins
Solid
Advanced Stats
3 factors agree (PPA + PPO + Havoc) → Maryland
· 82.4% ATS historically
↓ See full breakdown
Virginia Tech 2026 Schedule
Virginia Tech's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Virginia Tech vs VMI | -21.5 | — | — | — | — |
| Sat 9/12 | Virginia Tech vs Old Dominion | +2.5 | — | — | — | — |
| Sat 9/19 | Virginia Tech at Maryland | +7 | — | — | — | — |
| Sat 9/26 | Virginia Tech at Boston College | -2.5 | — | — | — | — |
| Fri 10/2 | Virginia Tech vs Pittsburgh | +8.5 | — | — | — | — |
| Sat 10/10 | Virginia Tech at California | +9 | — | — | — | — |
| Sat 10/17 | Virginia Tech vs Georgia Tech | +5 | — | — | — | — |
| Sat 10/24 | Virginia Tech at Clemson | +13.5 | — | — | — | — |
| — Bye Week — | ||||||
| Fri 11/6 | Virginia Tech at SMU | +19.5 | — | — | — | — |
| Sat 11/14 | Virginia Tech vs Stanford | -9 | — | — | — | — |
| Fri 11/20 | Virginia Tech at Miami | +27 | — | — | — | — |
| Sat 11/28 | Virginia Tech vs Virginia | +6 | — | — | — | — |
Maryland 2026 Schedule
Maryland's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Maryland vs Howard | -26 | — | — | — | — |
| Sat 9/12 | Maryland at UConn | +1 | — | — | — | — |
| Sat 9/19 | Maryland vs Virginia Tech | -7 | — | — | — | — |
| Sat 9/26 | Maryland vs UCLA | +1 | — | — | — | — |
| Sat 10/3 | Maryland at Nebraska | +5.5 | — | — | — | — |
| Sat 10/10 | Maryland at Ohio State | +28 | — | — | — | — |
| Sat 10/17 | Maryland vs Rutgers | -5.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/31 | Maryland vs Illinois | +5.5 | — | — | — | — |
| Sat 11/7 | Maryland at Purdue | -5 | — | — | — | — |
| Sat 11/14 | Maryland vs Wisconsin | -7 | — | — | — | — |
| Sat 11/21 | Maryland at USC | +17 | — | — | — | — |
| Sat 11/28 | Maryland vs Penn State | +8.5 | — | — | — | — |
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
3 Agree
→ Maryland
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?
Maryland Edge
Maryland +0.36
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?
Maryland Edge
Maryland +16.8
GC Edge (season-to-date)
Teams with this edge win 67.1% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
Virginia Tech
James Franklin #6
0–0 (0%)
· Yr 1 at school
OC
Ty Howle
Yr 1
#67
DC
Brent Pry
Yr 1
#68
Maryland
Mike Locksley #109
37–49 (43%)
· Yr 8 at school
OC
Clint Trickett
Yr 1
#127
DC
Ted Monachino
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 ✓