Matchup Prediction
Maryland
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Maryland entering this game.
Momentum Control
73.7%
Maryland wins
Solid
Game Control
58.3%
Maryland wins
Lean
Vegas Spread
Virginia -2.5
O/U 55.5
ESPN Bet
Advanced Stats
PPA + Success Rate agree → Maryland
· 73.9% ATS historically
↓ See full breakdown
Maryland 2024 Schedule
Maryland's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | Maryland vs UConn | -19.0W50–7 | 45.5 | W50–7 | O | Y |
| Sat 9/7 | Maryland vs Michigan State | -7.5L24–27 | 44.5 | L24–27 | O | N |
| Sat 9/14 | Maryland at Virginia | +2.5W27–13 | 55.5 | W27–13 | U | Y |
| Sat 9/21 | Maryland vs Villanova | -22.5W38–20 | 46.5 | W38–20 | O | N |
| Sat 9/28 | Maryland at Indiana | +7.5L28–42 | 50.0 | L28–42 | O | N |
| — Bye Week — | ||||||
| Fri 10/11 | Maryland vs Northwestern | -11.0L10–37 | 45.0 | L10–37 | O | N |
| Sat 10/19 | Maryland vs USC | +6.5W29–28 | 56.5 | W29–28 | O | Y |
| Sat 10/26 | Maryland at Minnesota | +6.0L23–48 | 45.0 | L23–48 | O | N |
| — Bye Week — | ||||||
| Sat 11/9 | Maryland at Oregon | +24.0L18–39 | 58.0 | L18–39 | U | Y |
| Sat 11/16 | Maryland vs Rutgers | -4.5L17–31 | 54.5 | L17–31 | U | N |
| Sat 11/23 | Maryland vs Iowa | +4.0L13–29 | 40.5 | L13–29 | O | N |
| Sat 11/30 | Maryland at Penn State | +26.5L7–44 | 50.5 | L7–44 | O | N |
Virginia 2024 Schedule
Virginia's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | Virginia vs Richmond | -21 | — | — | — | — |
| Sat 9/7 | Virginia at Wake Forest | -1.5W31–30 | 55.5 | W31–30 | O | N |
| Sat 9/14 | Virginia vs Maryland | -2.5L13–27 | 55.5 | L13–27 | U | N |
| Sat 9/21 | Virginia at Coastal Carolina | -3.5W43–24 | 52.0 | W43–24 | O | Y |
| — Bye Week — | ||||||
| Sat 10/5 | Virginia vs Boston College | -2.0W24–14 | 52.5 | W24–14 | U | Y |
| Sat 10/12 | Virginia vs Louisville | +7.0L20–24 | 54.0 | L20–24 | U | Y |
| Sat 10/19 | Virginia at Clemson | +20.0L31–48 | 57.5 | L31–48 | O | Y |
| Sat 10/26 | Virginia vs North Carolina | -3.5L14–41 | 58.5 | L14–41 | U | N |
| — Bye Week — | ||||||
| Sat 11/9 | Virginia at Pittsburgh | +7.5W24–19 | 56.5 | W24–19 | U | Y |
| Sat 11/16 | Virginia at Notre Dame | +20.5L14–35 | 51.0 | L14–35 | U | N |
| Sat 11/23 | Virginia vs SMU | +11.5L7–33 | 54.5 | L7–33 | U | N |
| Sat 11/30 | Virginia at Virginia Tech | +4.5L17–37 | 44.5 | L17–37 | 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
Split
Metrics disagree
Elite · 82.4% ATS
PPA + PPO + Havoc
Split
Metrics disagree
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Maryland
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?
Maryland Edge
Maryland +1.50
CSS Edge (season-to-date)
Teams with this edge win 73.7% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Maryland Edge
Maryland +5.7
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 2 games this season
Actual Result
CSS Battle
Maryland
1 — 2 sequences
✓ Predicted correctly
GC Battle
Maryland
20.4 — 53.2 GC score
✓ Predicted correctly
Game Result
Maryland won by 14
✓ Model called it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Maryland. Teams with this edge profile have covered 50.3% historically — essentially a coin flip against the spread.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Maryland
Mike Locksley #1
29–33 (47%)
· Yr 6 at school
OC
Josh Gattis
Yr 2
#1
DC
Aazaar Abdul-Rahim
Yr 1
#1
Virginia
Tony Elliott #1
6–16 (27%)
· Yr 3 at school
OC
Des Kitchings
Yr 3
#1
DC
John Rudzinski
Yr 3
#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 ✓