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
Rutgers -2
O/U 53.0
teamrankings
Advanced Stats
PPA + Success Rate agree → Maryland
· 73.9% ATS historically
↓ See full breakdown
Maryland 2021 Schedule
Maryland's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | Maryland vs West Virginia | +2.5W30–24 | 56.0 | W30–24 | U | Y |
| Sat 9/11 | Maryland vs Howard | -48.5W62–0 | 56.0 | W62–0 | O | Y |
| Fri 9/17 | Maryland at Illinois | -7.0W20–17 | 61.5 | W20–17 | U | N |
| Sat 9/25 | Maryland vs Kent State | -13.0W37–16 | 71.5 | W37–16 | U | Y |
| Fri 10/1 | Maryland vs Iowa | +3.0L14–51 | 47.5 | L14–51 | O | N |
| Sat 10/9 | Maryland at Ohio State | +22.0L17–66 | 71.5 | L17–66 | O | N |
| — Bye Week — | ||||||
| Sat 10/23 | Maryland at Minnesota | +4.0L16–34 | 53.0 | L16–34 | U | N |
| Sat 10/30 | Maryland vs Indiana | -3.5W38–35 | 48.0 | W38–35 | O | N |
| Sat 11/6 | Maryland vs Penn State | +10.0L14–31 | 56.5 | L14–31 | U | N |
| Sat 11/13 | Maryland at Michigan State | +11.5L21–40 | 60.0 | L21–40 | O | N |
| Sat 11/20 | Maryland vs Michigan | +16.0L18–59 | 58.5 | L18–59 | O | N |
| Sat 11/27 | Maryland at Rutgers | +2.0W40–16 | 53.0 | W40–16 | O | Y |
| Wed 12/29 | Maryland vs Virginia Tech | -4.0W54–10 | 55.0 | W54–10 | O | Y |
Rutgers 2021 Schedule
Rutgers's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | Rutgers vs Temple | -14.0W61–14 | 52.0 | W61–14 | O | Y |
| Sat 9/11 | Rutgers at Syracuse | -2.5W17–7 | 50.5 | W17–7 | U | Y |
| Sat 9/18 | Rutgers vs Delaware | -20.0W45–13 | 45.0 | W45–13 | O | Y |
| Sat 9/25 | Rutgers at Michigan | +20.0L13–20 | 50.0 | L13–20 | U | Y |
| Sat 10/2 | Rutgers vs Ohio State | +15.0L13–52 | 58.0 | L13–52 | O | N |
| Sat 10/9 | Rutgers vs Michigan State | +4.5L13–31 | 50.0 | L13–31 | U | N |
| Sat 10/16 | Rutgers at Northwestern | -2.5L7–21 | 45.0 | L7–21 | U | N |
| — Bye Week — | ||||||
| Sat 10/30 | Rutgers at Illinois | -1.5W20–14 | 41.5 | W20–14 | U | Y |
| Sat 11/6 | Rutgers vs Wisconsin | +13.0L3–52 | 37.5 | L3–52 | O | N |
| Sat 11/13 | Rutgers at Indiana | +6.5W38–3 | 42.5 | W38–3 | U | Y |
| Sat 11/20 | Rutgers at Penn State | +14.0L0–28 | 44.5 | L0–28 | U | N |
| Sat 11/27 | Rutgers vs Maryland | -2.0L16–40 | 53.0 | L16–40 | O | N |
| Fri 12/31 | Rutgers vs Wake Forest | +17.0L10–38 | 63.0 | L10–38 | U | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2021 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 · 2021 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 +0.10
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 +2.8
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
9–17 (35%)
· Yr 3 at school
OC
Dan Enos
Yr 1
#1
DC
Brian Williams
Yr 1
#1
Rutgers
Greg Schiano #1
6–6 (50%)
· Yr 2 at school
OC
Sean Gleeson
Yr 1
#1
DC
Robb Smith
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 ✓