Matchup Prediction
Maryland
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Maryland entering this game.
Momentum Control
58.4%
Maryland wins
Lean
Game Control
67.1%
Maryland wins
Solid
Vegas Spread
Maryland -14.5
O/U 48.5
teamrankings
Advanced Stats
PPA + Success Rate agree → Maryland
· 73.9% ATS historically
↓ See full breakdown
Rutgers 2022 Schedule
Rutgers's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Rutgers at Boston College | +8.5W22–21 | 47.0 | W22–21 | U | Y |
| Sat 9/10 | Rutgers vs Wagner | -48.5W66–7 | 55.5 | W66–7 | O | Y |
| Sat 9/17 | Rutgers at Temple | -18.0W16–14 | 42.5 | W16–14 | U | N |
| Sat 9/24 | Rutgers vs Iowa | +7.5L10–27 | 34.5 | L10–27 | O | N |
| Sat 10/1 | Rutgers at Ohio State | +39.0L10–49 | 58.0 | L10–49 | O | Y |
| Fri 10/7 | Rutgers vs Nebraska | +3.0L13–14 | 50.5 | L13–14 | U | Y |
| — Bye Week — | ||||||
| Sat 10/22 | Rutgers vs Indiana | -3.0W24–17 | 48.0 | W24–17 | U | Y |
| Sat 10/29 | Rutgers at Minnesota | +14.0L0–31 | 40.5 | L0–31 | U | N |
| Sat 11/5 | Rutgers vs Michigan | +26.0L17–52 | 45.0 | L17–52 | O | N |
| Sat 11/12 | Rutgers at Michigan State | +10.0L21–27 | 41.0 | L21–27 | O | Y |
| Sat 11/19 | Rutgers vs Penn State | +18.5L10–55 | 45.0 | L10–55 | O | N |
| Sat 11/26 | Rutgers at Maryland | +14.5L0–37 | 48.5 | L0–37 | U | N |
Maryland 2022 Schedule
Maryland's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Maryland vs Buffalo | -24.0W31–10 | 66.0 | W31–10 | U | N |
| Sat 9/10 | Maryland at Charlotte | -28.0W56–21 | 65.0 | W56–21 | O | Y |
| Sat 9/17 | Maryland vs SMU | -3.0W34–27 | 74.0 | W34–27 | U | Y |
| Sat 9/24 | Maryland at Michigan | +17.0L27–34 | 66.0 | L27–34 | U | Y |
| Sat 10/1 | Maryland vs Michigan State | -7.5W27–13 | 58.5 | W27–13 | U | Y |
| Sat 10/8 | Maryland vs Purdue | -3.0L29–31 | 59.5 | L29–31 | O | N |
| Sat 10/15 | Maryland at Indiana | -11.0W38–33 | 63.0 | W38–33 | O | N |
| Sat 10/22 | Maryland vs Northwestern | -14.0W31–24 | 51.0 | W31–24 | O | N |
| — Bye Week — | ||||||
| Sat 11/5 | Maryland at Wisconsin | +5.0L10–23 | 47.5 | L10–23 | U | N |
| Sat 11/12 | Maryland at Penn State | +10.5L0–30 | 56.5 | L0–30 | U | N |
| Sat 11/19 | Maryland vs Ohio State | +26.5L30–43 | 62.5 | L30–43 | O | Y |
| Sat 11/26 | Maryland vs Rutgers | -14.5W37–0 | 48.5 | W37–0 | U | Y |
| Fri 12/30 | Maryland vs NC State | +2.5W16–12 | 45.0 | W16–12 | U | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2022 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 · 2022 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 +0.79
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 11 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Maryland Edge
Maryland +15.8
GC Edge (season-to-date)
Teams with this edge win 67.1% of games historically
Based on 11 games this season
Actual Result
CSS Battle
Maryland
5 — 0 sequences
✓ Predicted correctly
GC Battle
Maryland
92.5 — 2.7 GC score
✓ Predicted correctly
Game Result
Maryland won by 37
✓ 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 solid GC edge. Teams with this profile have covered 53.0% of the time historically (n=330) — a mild lean.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Rutgers
Greg Schiano #1
8–14 (36%)
· Yr 3 at school
OC
Sean Gleeson
Yr 2
#1
DC
Joe Harasymiak
Yr 1
#1
Maryland
Mike Locksley #1
12–23 (34%)
· Yr 4 at school
OC
Dan Enos
Yr 2
#1
DC
Brian Williams
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 ✓