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
58.4%
Maryland wins
Lean
Game Control
58.3%
Rutgers wins
Lean
Vegas Spread
Maryland -4.5
O/U 54.5
Bovada
Advanced Stats
Advanced factors are split · No strong agreement signal
↓ See full breakdown
Rutgers 2024 Schedule
Rutgers's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Thu 8/29 | Rutgers vs Howard | -38.5W44–7 | 51.5 | W44–7 | U | N |
| Sat 9/7 | Rutgers vs Akron | -24.5W49–17 | 41.5 | W49–17 | O | Y |
| — Bye Week — | ||||||
| Sat 9/21 | Rutgers at Virginia Tech | +3.0W26–23 | 45.0 | W26–23 | O | Y |
| Fri 9/27 | Rutgers vs Washington | -1.5W21–18 | 44.5 | W21–18 | U | Y |
| Sat 10/5 | Rutgers at Nebraska | +7.0L7–14 | 39.5 | L7–14 | U | Y |
| Sat 10/12 | Rutgers vs Wisconsin | -1.0L7–42 | 43.5 | L7–42 | O | N |
| Sat 10/19 | Rutgers vs UCLA | -4.0L32–35 | 42.5 | L32–35 | O | N |
| Fri 10/25 | Rutgers at USC | +14.0L20–42 | 57.0 | L20–42 | O | N |
| — Bye Week — | ||||||
| Sat 11/9 | Rutgers vs Minnesota | +6.5W26–19 | 44.5 | W26–19 | O | Y |
| Sat 11/16 | Rutgers at Maryland | +4.5W31–17 | 54.5 | W31–17 | U | Y |
| Sat 11/23 | Rutgers vs Illinois | -2.0L31–38 | 48.0 | L31–38 | O | N |
| Sat 11/30 | Rutgers at Michigan State | +1.5W41–14 | 46.5 | W41–14 | O | Y |
| Thu 12/26 | Rutgers vs Kansas State | +7.5L41–44 | 51.5 | L41–44 | O | Y |
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 |
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
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 · 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 +0.38
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 8 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Rutgers Edge
Rutgers +4.2
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 9 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
Rutgers
Greg Schiano #1
19–28 (40%)
· Yr 5 at school
OC
Kirk Ciarrocca
Yr 2
#1
DC
Joe Harasymiak
Yr 3
#1
Maryland
Mike Locksley #1
29–33 (47%)
· Yr 6 at school
OC
Josh Gattis
Yr 2
#1
DC
Aazaar Abdul-Rahim
Yr 1
#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: 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 ✓