Preseason projection — This game has not yet been played and 2026 in-season data is not yet available.
Edges are based on 2025 full-season performance.
Confidence will increase once in-season games are logged.
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
Advanced Stats
All 4 factors agree → SMU
· 83.1% ATS historically when all four align
↓ See full breakdown
SMU 2026 Schedule
SMU's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Mon 9/7 | SMU at Florida State | -2.5 | — | — | — | — |
| Sat 9/12 | SMU vs UC Davis | -31.5 | — | — | — | — |
| Sat 9/19 | SMU at Louisville | +0.5 | — | — | — | — |
| Sat 9/26 | SMU vs Missouri State | -28.5 | — | — | — | — |
| Sat 10/3 | SMU vs Boston College | -19.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/17 | SMU vs Virginia | -9 | — | — | — | — |
| Fri 10/23 | SMU vs California | -11 | — | — | — | — |
| Fri 10/30 | SMU at Syracuse | -12 | — | — | — | — |
| Fri 11/6 | SMU vs Virginia Tech | -8 | — | — | — | — |
| Sat 11/14 | SMU vs Wake Forest | -11.5 | — | — | — | — |
| Sat 11/21 | SMU at Notre Dame | +16.5 | — | — | — | — |
| Sat 11/28 | SMU at Stanford | -13.5 | — | — | — | — |
Stanford 2026 Schedule
Stanford's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/29 | Stanford vs Hawai'i | -2.5 | — | — | — | — |
| Fri 9/4 | Stanford vs Miami | +23 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 9/19 | Stanford at Duke | +10 | — | — | — | — |
| Sat 9/26 | Stanford vs Georgia Tech | +4 | — | — | — | — |
| Sat 10/3 | Stanford at Wake Forest | +9.5 | — | — | — | — |
| Sat 10/10 | Stanford at Notre Dame | +30 | — | — | — | — |
| Sat 10/17 | Stanford vs Elon | -20.5 | — | — | — | — |
| Fri 10/23 | Stanford vs NC State | +6 | — | — | — | — |
| Sat 10/31 | Stanford at Louisville | +16.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 11/14 | Stanford at Virginia Tech | +13 | — | — | — | — |
| Sat 11/21 | Stanford at California | +10 | — | — | — | — |
| Sat 11/28 | Stanford vs SMU | +13.5 | — | — | — | — |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2025 season (prior year)
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 · 2025 season (prior year — 2026 data not yet available) ·
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 +0.83
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +34.7
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
SMU
Rhett Lashlee #12
38–17 (69%)
· Yr 5 at school
OC
Rob Likens
Yr 1
#67
DC
Maurice Crum Jr
Yr 1
#52
Stanford
Tavita Pritchard #77
0–0 (0%)
· Yr 1 at school
OC
Terry Heffernan
Yr 1
#67
DC
Kris Richard
Yr 1
#68
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 ✓