Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Maryland,
while Game Control favors Rutgers.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
61.3%
Maryland wins
Lean
Game Control
50.6%
Rutgers wins
Toss-up
Vegas Spread
Maryland -2
O/U 45.5
DraftKings
Advanced Stats
PPA + Success Rate agree → Maryland
· 73.9% ATS historically
↓ See full breakdown
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 |
Rutgers 2023 Schedule
Rutgers's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sun 9/3 | Rutgers vs Northwestern | -5.0W24–7 | 39.0 | W24–7 | U | Y |
| Sat 9/9 | Rutgers vs Temple | -7.5W36–7 | 43.5 | W36–7 | U | Y |
| Sat 9/16 | Rutgers vs Virginia Tech | -6.5W35–16 | 37.5 | W35–16 | O | Y |
| Sat 9/23 | Rutgers at Michigan | +24.0L7–31 | 44.5 | L7–31 | U | Y |
| Sat 9/30 | Rutgers vs Wagner | -46.0W52–3 | 52.5 | W52–3 | O | Y |
| Sat 10/7 | Rutgers at Wisconsin | +12.5L13–24 | 44.0 | L13–24 | U | Y |
| Sat 10/14 | Rutgers vs Michigan State | -4.0W27–24 | 38.5 | W27–24 | O | N |
| Sat 10/21 | Rutgers at Indiana | -6.0W31–14 | 39.5 | W31–14 | O | Y |
| — Bye Week — | ||||||
| Sat 11/4 | Rutgers vs Ohio State | +19.0L16–35 | 42.5 | L16–35 | O | Y |
| Sat 11/11 | Rutgers at Iowa | -2.5L0–22 | 27.5 | L0–22 | U | N |
| Sat 11/18 | Rutgers at Penn State | +19.5L6–27 | 39.5 | L6–27 | U | N |
| Sat 11/25 | Rutgers vs Maryland | +2.0L24–42 | 45.5 | L24–42 | O | N |
| Thu 12/28 | Rutgers vs Miami | -3.0W31–24 | 41.0 | W31–24 | O | 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 +0.30
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 10 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Rutgers Edge
Rutgers +1.3
GC Edge (season-to-date)
Teams with this edge win 50.6% of games historically
Based on 11 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
CSS and GC disagree on this matchup. When the metrics split, historical cover rates are essentially random — treat this as 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
24–28 (46%)
· Yr 5 at school
OC
Josh Gattis
Yr 1
#1
DC
Brian Williams
Yr 3
#1
Rutgers
Greg Schiano #1
15–22 (41%)
· Yr 4 at school
OC
Kirk Ciarrocca
Yr 1
#1
DC
Joe Harasymiak
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 ✓