Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Duke,
while Game Control favors SMU.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
58.4%
Duke wins
Lean
Game Control
64.9%
SMU wins
Lean
Vegas Spread
SMU -11.5
O/U 49.5
Bovada
Advanced Stats
PPA + Success Rate agree → SMU
· 73.9% ATS historically
↓ See full breakdown
SMU 2024 Schedule
SMU's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/24 | SMU at Nevada | -28.0W29–24 | 55.5 | W29–24 | U | N |
| Sat 8/31 | SMU vs Houston Christian | -30 | — | — | — | — |
| Fri 9/6 | SMU vs BYU | -12.5L15–18 | 55.5 | L15–18 | U | N |
| — Bye Week — | ||||||
| Sat 9/21 | SMU vs TCU | +1.0W66–42 | 58.5 | W66–42 | O | Y |
| Sat 9/28 | SMU vs Florida State | -6.0W42–16 | 46.0 | W42–16 | O | Y |
| Sat 10/5 | SMU at Louisville | +6.5W34–27 | 55.0 | W34–27 | O | Y |
| — Bye Week — | ||||||
| Sat 10/19 | SMU at Stanford | -16.5W40–10 | 52.5 | W40–10 | U | Y |
| Sat 10/26 | SMU at Duke | -11.5W28–27 | 49.5 | W28–27 | O | N |
| Sat 11/2 | SMU vs Pittsburgh | -7.0W48–25 | 55.5 | W48–25 | O | Y |
| — Bye Week — | ||||||
| Sat 11/16 | SMU vs Boston College | -19.0W38–28 | 54.5 | W38–28 | O | N |
| Sat 11/23 | SMU at Virginia | -11.5W33–7 | 54.5 | W33–7 | U | Y |
| Sat 11/30 | SMU vs California | -11.5W38–6 | 54.5 | W38–6 | U | Y |
| Sat 12/7 | SMU vs Clemson | -2.5L31–34 | 56.5 | L31–34 | O | N |
| Sat 12/21 | SMU at Penn State | +9.0L10–38 | 52.5 | L10–38 | U | N |
Duke 2024 Schedule
Duke's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 8/30 | Duke vs Elon | -24.5W26–3 | 47.5 | W26–3 | U | N |
| Fri 9/6 | Duke vs Northwestern | +2.5W26–20 | 37.5 | W26–20 | O | Y |
| Sat 9/14 | Duke vs UConn | -17.5W26–21 | 46.5 | W26–21 | O | N |
| Sat 9/21 | Duke at Middle Tennessee | -14.5W45–17 | 51.0 | W45–17 | O | Y |
| Sat 9/28 | Duke vs North Carolina | +1.0W21–20 | 55.5 | W21–20 | U | Y |
| Sat 10/5 | Duke at Georgia Tech | +10.0L14–24 | 52.5 | L14–24 | U | Y |
| — Bye Week — | ||||||
| Fri 10/18 | Duke vs Florida State | -2.5W23–16 | 43.0 | W23–16 | U | Y |
| Sat 10/26 | Duke vs SMU | +11.5L27–28 | 49.5 | L27–28 | O | Y |
| Sat 11/2 | Duke at Miami | +21.0L31–53 | 55.5 | L31–53 | O | N |
| Sat 11/9 | Duke at NC State | +3.5W29–19 | 51.5 | W29–19 | U | Y |
| — Bye Week — | ||||||
| Sat 11/23 | Duke vs Virginia Tech | +2.5W31–28 | 45.5 | W31–28 | O | Y |
| Sat 11/30 | Duke at Wake Forest | -3.5W23–17 | 53.5 | W23–17 | U | Y |
| Thu 1/2 | Duke vs Ole Miss | +17.5L20–52 | 50.5 | L20–52 | 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
Both Agree
→ SMU
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?
Duke Edge
Duke +0.33
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 6 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +17.3
GC Edge (season-to-date)
Teams with this edge win 64.9% of games historically
Based on 7 games this season
Actual Result
CSS Battle
Tie
1 — 1 sequences
✗ Predicted incorrectly
GC Battle
SMU
19.0 — 46.7 GC score
✓ Predicted correctly
Game Result
SMU won by 1
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
SMU
Rhett Lashlee #1
18–10 (64%)
· Yr 3 at school
OC
Casey Woods
Yr 3
#1
DC
Scott Symons
Yr 3
#1
Duke
Manny Diaz #1
0–0 (0%)
· Yr 1 at school
OC
Jonathan Brewer
Yr 1
#1
DC
Jonathan Patke
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 ✓