Matchup Prediction
SMU
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
SMU entering this game.
Momentum Control
61.3%
SMU wins
Lean
Game Control
75.9%
SMU wins
Solid
Vegas Spread
SMU -16.5
O/U 52.5
DraftKings
Advanced Stats
All 4 factors agree → SMU
· 83.1% ATS historically when all four align
↓ 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 |
Stanford 2024 Schedule
Stanford's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 8/30 | Stanford vs TCU | +8.0L27–34 | 58.5 | L27–34 | O | Y |
| Sat 9/7 | Stanford vs Cal Poly | -33.5W41–7 | 59.5 | W41–7 | U | Y |
| — Bye Week — | ||||||
| Fri 9/20 | Stanford at Syracuse | +9.5W26–24 | 56.5 | W26–24 | U | Y |
| Sat 9/28 | Stanford at Clemson | +24.0L14–40 | 58.0 | L14–40 | U | N |
| Sat 10/5 | Stanford vs Virginia Tech | +9.5L7–31 | 50.0 | L7–31 | U | N |
| Sat 10/12 | Stanford at Notre Dame | +22.5L7–49 | 45.5 | L7–49 | O | N |
| Sat 10/19 | Stanford vs SMU | +16.5L10–40 | 52.5 | L10–40 | U | N |
| Sat 10/26 | Stanford vs Wake Forest | +3.0L24–27 | 53.0 | L24–27 | U | Y |
| Sat 11/2 | Stanford at NC State | +10.0L28–59 | 46.5 | L28–59 | O | N |
| — Bye Week — | ||||||
| Sat 11/16 | Stanford vs Louisville | +21.0W38–35 | 57.5 | W38–35 | O | Y |
| Sat 11/23 | Stanford at California | +15.0L21–24 | 53.5 | L21–24 | U | Y |
| Fri 11/29 | Stanford at San José State | +2.5L31–34 | 54.5 | L31–34 | 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
All 4 Agree
→ SMU
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ SMU
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?
SMU Edge
SMU +0.40
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 5 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +24.7
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 6 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on SMU with a large edge. Historically, dominant teams like this are fully priced into the spread — the agreed-upon team covers just 50.2% of the time. The metrics predict game control better than they beat the number.
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
Stanford
Troy Taylor #1
3–9 (25%)
· Yr 2 at school
OC
Troy Taylor
Yr 2
#1
DC
Bobby April III
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 ✓