Sat, Nov 22 2025
·
Week 13
·
🏟 Gerald J. Ford Stadium
University Park, TX
·
Turf
·
32,000 cap
Louisville✈ 721 mi-1 hr 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
58.6%
SMU wins
Lean
Vegas Spread
SMU -4
O/U 49.5
DraftKings
Advanced Stats
3 factors agree (PPA + PPO + Havoc) → SMU
· 82.4% ATS historically
↓ See full breakdown
Louisville 2025 Schedule
Louisville's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | Louisville vs Eastern Kentucky | -37.5W51–17 | 57.5 | W51–17 | O | N |
| Fri 9/5 | Louisville vs James Madison | -15.0W28–14 | 57.0 | W28–14 | U | N |
| — Bye Week — | ||||||
| Sat 9/20 | Louisville vs Bowling Green | -26.5W40–17 | 50.5 | W40–17 | O | N |
| Sat 9/27 | Louisville at Pittsburgh | -3.0W34–27 | 53.5 | W34–27 | O | Y |
| Sat 10/4 | Louisville vs Virginia | -6.5L27–30 | 59.5 | L27–30 | U | N |
| — Bye Week — | ||||||
| Fri 10/17 | Louisville at Miami | +10.5W24–21 | 49.5 | W24–21 | U | Y |
| Sat 10/25 | Louisville vs Boston College | -25.5W38–24 | 54.5 | W38–24 | O | N |
| Sat 11/1 | Louisville at Virginia Tech | -10.5W28–16 | 52.5 | W28–16 | U | Y |
| Sat 11/8 | Louisville vs California | -18.5L26–29 | 48.5 | L26–29 | O | N |
| Fri 11/14 | Louisville vs Clemson | -1.5L19–20 | 50.5 | L19–20 | U | N |
| Sat 11/22 | Louisville at SMU | +4.0L6–38 | 49.5 | L6–38 | U | N |
| Sat 11/29 | Louisville vs Kentucky | -1.0W41–0 | 45.5 | W41–0 | U | Y |
| Tue 12/23 | Louisville vs Toledo | -12.5W27–22 | 44.5 | W27–22 | O | N |
SMU 2025 Schedule
SMU's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | SMU vs East Texas A&M | -51.0W42–13 | 65.0 | W42–13 | U | N |
| Sat 9/6 | SMU vs Baylor | -3.0L45–48 | 65.5 | L45–48 | O | N |
| Sat 9/13 | SMU at Missouri State | -29.5W28–10 | 60.5 | W28–10 | U | N |
| Sat 9/20 | SMU at TCU | +6.5L24–35 | 63.5 | L24–35 | U | N |
| — Bye Week — | ||||||
| Sat 10/4 | SMU vs Syracuse | -17.5W31–18 | 56.5 | W31–18 | U | N |
| Sat 10/11 | SMU vs Stanford | -19.5W34–10 | 55.5 | W34–10 | U | Y |
| Sat 10/18 | SMU at Clemson | +3.5W35–24 | 49.5 | W35–24 | O | Y |
| Sat 10/25 | SMU at Wake Forest | -6.5L12–13 | 53.5 | L12–13 | U | N |
| Sat 11/1 | SMU vs Miami | +8.5W26–20 | 50.5 | W26–20 | U | Y |
| Sat 11/8 | SMU at Boston College | -10.5W45–13 | 54.5 | W45–13 | O | Y |
| — Bye Week — | ||||||
| Sat 11/22 | SMU vs Louisville | -4.0W38–6 | 49.5 | W38–6 | U | Y |
| Sat 11/29 | SMU at California | -13.5L35–38 | 53.5 | L35–38 | O | N |
| Fri 1/2 | SMU vs Arizona | -2.5W24–19 | 55.5 | W24–19 | U | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2025 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
3 Agree
→ SMU
Elite · 73.9% ATS
PPA + Success Rate
Split
Metrics disagree
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 ·
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?
Louisville +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 9 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +8.9
GC Edge (season-to-date)
Teams with this edge win 58.6% of games historically
Based on 10 games this season
Actual Result
CSS Battle
SMU
3 — 0 sequences
GC Battle
SMU
91.7 — 4.9 GC score
✓ Predicted correctly
Game Result
SMU won by 32
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on SMU. Teams with this edge profile have covered 50.3% historically — essentially a coin flip against the spread.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Louisville
Jeff Brohm #1
18–8 (69%)
· Yr 3 at school
OC
Brian Brohm
Yr 3
#1
DC
Ron English
Yr 3
#1
SMU
Rhett Lashlee #1
29–12 (71%)
· Yr 4 at school
OC
Casey Woods
Yr 3
#1
DC
Scott Symons
Yr 3
#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 ✓