Sat, Sep 10 2022
·
Week 2
·
🏟 Notre Dame Stadium
Notre Dame, IN
·
Turf
·
80,795 cap
Marshall✈ 302 miSame TZ
Matchup Prediction
Toss-up — no clear edge
Neither metric shows a meaningful pre-game edge in this matchup.
Momentum Control
58.4%
—
Lean
Game Control
75.9%
Marshall wins
Solid
Vegas Spread
Notre Dame -20.5
O/U 48.0
teamrankings
Advanced Stats
All 4 factors agree → Marshall
· 83.1% ATS historically when all four align
↓ See full breakdown
Marshall 2022 Schedule
Marshall's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Marshall vs Norfolk State | -40.5W55–3 | 59.0 | W55–3 | U | Y |
| Sat 9/10 | Marshall at Notre Dame | +20.5W26–21 | 48.0 | W26–21 | U | Y |
| Sat 9/17 | Marshall at Bowling Green | -17.0L31–34 | 50.0 | L31–34 | O | N |
| Sat 9/24 | Marshall at Troy | -3.0L7–16 | 51.5 | L7–16 | U | N |
| Sat 10/1 | Marshall vs Gardner-Webb | -31.0W28–7 | 56.0 | W28–7 | U | N |
| — Bye Week — | ||||||
| Wed 10/12 | Marshall vs Louisiana | -10.5L13–23 | 45.5 | L13–23 | U | N |
| Sat 10/22 | Marshall at James Madison | +9.5W26–12 | 48.5 | W26–12 | U | Y |
| Sat 10/29 | Marshall vs Coastal Carolina | -2.5L13–24 | 54.0 | L13–24 | U | N |
| Sat 11/5 | Marshall at Old Dominion | -3.5W12–0 | 46.5 | W12–0 | U | Y |
| Sat 11/12 | Marshall vs App State | +2.0W28–21 | 47.5 | W28–21 | O | Y |
| Sat 11/19 | Marshall at Georgia Southern | -6.0W23–10 | 52.5 | W23–10 | U | Y |
| Sat 11/26 | Marshall vs Georgia State | -6.5W28–23 | 45.5 | W28–23 | O | N |
| Mon 12/19 | Marshall vs UConn | -11.5W28–14 | 42.0 | W28–14 | U | Y |
Notre Dame 2022 Schedule
Notre Dame's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Notre Dame at Ohio State | +17.0L10–21 | 58.5 | L10–21 | U | Y |
| Sat 9/10 | Notre Dame vs Marshall | -20.5L21–26 | 48.0 | L21–26 | U | N |
| Sat 9/17 | Notre Dame vs California | -13.5W24–17 | 41.5 | W24–17 | U | N |
| Sat 9/24 | Notre Dame at North Carolina | +2.5W45–32 | 55.0 | W45–32 | O | Y |
| — Bye Week — | ||||||
| Sat 10/8 | Notre Dame vs BYU | -4.0W28–20 | 51.0 | W28–20 | U | Y |
| Sat 10/15 | Notre Dame vs Stanford | -16.5L14–16 | 53.5 | L14–16 | U | N |
| Sat 10/22 | Notre Dame vs UNLV | -26.0W44–21 | 46.5 | W44–21 | O | N |
| Sat 10/29 | Notre Dame at Syracuse | +1.0W41–24 | 48.0 | W41–24 | O | Y |
| Sat 11/5 | Notre Dame vs Clemson | +3.5W35–14 | 43.5 | W35–14 | O | Y |
| Sat 11/12 | Notre Dame vs Navy | -17.0W35–32 | 40.5 | W35–32 | O | N |
| Sat 11/19 | Notre Dame vs Boston College | -20.0W44–0 | 42.0 | W44–0 | O | Y |
| Sat 11/26 | Notre Dame at USC | +4.0L27–38 | 63.5 | L27–38 | O | N |
| Fri 12/30 | Notre Dame vs South Carolina | -5.0W45–38 | 50.5 | W45–38 | O | 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
All 4 Agree
→ Marshall
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Marshall
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Marshall
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?
Marshall Edge
Marshall +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Marshall Edge
Marshall +77.3
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 1 game 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
Marshall
Charles Huff #1
7–6 (54%)
· Yr 2 at school
OC
Clint Trickett
Yr 1
#1
DC
Lance Guidry
Yr 2
#1
Notre Dame
Marcus Freeman #1
0–1 (0%)
· Yr 1 at school
OC
Tommy Rees
Yr 2
#1
DC
Al Golden
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: 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 ✓