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
76%
SMU wins
Strong
Vegas Spread
SMU -3.0
O/U 65.5
Bovada
Advanced Stats
All 4 factors agree → SMU
· 83.1% ATS historically when all four align
↓ See full breakdown
Baylor 2025 Schedule
Baylor's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 8/29 | Baylor vs Auburn | +1.5L24–38 | 57.5 | L24–38 | O | N |
| Sat 9/6 | Baylor at SMU | +3.0W48–45 | 65.5 | W48–45 | O | Y |
| Sat 9/13 | Baylor vs Samford | -51.5W42–7 | 65.5 | W42–7 | U | N |
| Sat 9/20 | Baylor vs Arizona State | -3.0L24–27 | 60.5 | L24–27 | U | N |
| Sat 9/27 | Baylor at Oklahoma State | -21.0W45–27 | 58.5 | W45–27 | O | N |
| Sat 10/4 | Baylor vs Kansas State | -4.5W35–34 | 59.5 | W35–34 | O | N |
| — Bye Week — | ||||||
| Sat 10/18 | Baylor at TCU | +3.5L36–42 | 66.5 | L36–42 | O | N |
| Sat 10/25 | Baylor at Cincinnati | +3.5L20–41 | 68.5 | L20–41 | U | N |
| Sat 11/1 | Baylor vs UCF | -3.0W30–3 | 58.5 | W30–3 | U | Y |
| — Bye Week — | ||||||
| Sat 11/15 | Baylor vs Utah | +9.5L28–55 | 60.5 | L28–55 | O | N |
| Sat 11/22 | Baylor at Arizona | +6.5L17–41 | 61.5 | L17–41 | U | N |
| Sat 11/29 | Baylor vs Houston | -2.5L24–31 | 57.5 | L24–31 | U | 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
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 ·
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?
Baylor Edge
Baylor +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 0 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +81.1
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 1 game this season
Actual Result
CSS Battle
SMU
1 — 0 sequences
✗ Predicted incorrectly
GC Battle
SMU
64.6 — 12.9 GC score
✓ Predicted correctly
Game Result
Baylor won by 3
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
Baylor
Dave Aranda #1
31–29 (52%)
· Yr 6 at school
OC
Jake Spavital
Yr 2
#1
DC
Matt Powledge
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: 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 ✓