Matchup Prediction
Maryland
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Maryland entering this game.
Momentum Control
78.1%
Maryland wins
Strong
Game Control
76%
Maryland wins
Strong
Vegas Spread
Maryland -16.5
O/U 48.5
William Hill (New Jersey)
Advanced Stats
PPA + Success Rate agree → Maryland
· 73.9% ATS historically
↓ See full breakdown
Virginia 2023 Schedule
Virginia's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Virginia vs Tennessee | +27.5L13–49 | 56.0 | L13–49 | O | N |
| Sat 9/9 | Virginia vs James Madison | +6.0L35–36 | 40.0 | L35–36 | O | Y |
| Fri 9/15 | Virginia at Maryland | +16.5L14–42 | 48.5 | L14–42 | O | N |
| Fri 9/22 | Virginia vs NC State | +8.5L21–24 | 47.5 | L21–24 | U | Y |
| Sat 9/30 | Virginia at Boston College | +5.0L24–27 | 52.5 | L24–27 | U | Y |
| Sat 10/7 | Virginia vs William & Mary | -10.0W27–13 | 42.0 | W27–13 | U | Y |
| — Bye Week — | ||||||
| Sat 10/21 | Virginia at North Carolina | +24.0W31–27 | 58.0 | W31–27 | U | Y |
| Sat 10/28 | Virginia at Miami | +18.5L26–29 | 48.0 | L26–29 | O | Y |
| Sat 11/4 | Virginia vs Georgia Tech | -2.0L17–45 | 57.5 | L17–45 | O | N |
| Thu 11/9 | Virginia at Louisville | +20.5L24–31 | 49.5 | L24–31 | O | Y |
| Sat 11/18 | Virginia vs Duke | +4.0W30–27 | 48.5 | W30–27 | O | Y |
| Sat 11/25 | Virginia vs Virginia Tech | +2.5L17–55 | 52.5 | L17–55 | O | N |
Maryland 2023 Schedule
Maryland's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Maryland vs Towson | -38.5W38–6 | 55.5 | W38–6 | U | N |
| Sat 9/9 | Maryland vs Charlotte | -24.5W38–20 | 50.0 | W38–20 | O | N |
| Fri 9/15 | Maryland vs Virginia | -16.5W42–14 | 48.5 | W42–14 | O | Y |
| Sat 9/23 | Maryland at Michigan State | -7.0W31–9 | 52.5 | W31–9 | U | Y |
| Sat 9/30 | Maryland vs Indiana | -14.5W44–17 | 50.0 | W44–17 | O | Y |
| Sat 10/7 | Maryland at Ohio State | +17.0L17–37 | 56.5 | L17–37 | U | N |
| Sat 10/14 | Maryland vs Illinois | -13.5L24–27 | 52.0 | L24–27 | U | N |
| — Bye Week — | ||||||
| Sat 10/28 | Maryland at Northwestern | -14.0L27–33 | 48.5 | L27–33 | O | N |
| Sat 11/4 | Maryland vs Penn State | +8.5L15–51 | 50.5 | L15–51 | O | N |
| Sat 11/11 | Maryland at Nebraska | -1.5W13–10 | 41.5 | W13–10 | U | Y |
| Sat 11/18 | Maryland vs Michigan | +17.5L24–31 | 50.5 | L24–31 | O | Y |
| Sat 11/25 | Maryland at Rutgers | -2.0W42–24 | 45.5 | W42–24 | O | Y |
| Sat 12/30 | Maryland vs Auburn | +4.0W31–13 | 47.5 | W31–13 | U | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2023 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 · 2023 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 +3.00
CSS Edge (season-to-date)
Teams with this edge win 78.1% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Maryland Edge
Maryland +41.3
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 2 games this season
Actual Result
CSS Battle
Maryland
2 — 1 sequences
✓ Predicted correctly
GC Battle
Maryland
48.4 — 36.0 GC score
✓ Predicted correctly
Game Result
Maryland won by 28
✓ Model called it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Maryland with a large edge. Historically, dominant teams like this are fully priced into the spread — the agreed-upon team covers just 50.2% of the time. The metrics predict game control better than they beat the number.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Virginia
Tony Elliott #1
3–10 (23%)
· Yr 2 at school
OC
Des Kitchings
Yr 2
#1
DC
John Rudzinski
Yr 2
#1
Maryland
Mike Locksley #1
24–28 (46%)
· Yr 5 at school
OC
Josh Gattis
Yr 1
#1
DC
Brian Williams
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 ✓