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
67.1%
SMU wins
Solid
Vegas Spread
SMU -22.5
O/U 53.0
William Hill (New Jersey)
Advanced Stats
All 4 factors agree → SMU
· 83.1% ATS historically when all four align
↓ See full breakdown
Charlotte 2023 Schedule
Charlotte's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Charlotte vs South Carolina State | -24.0W24–3 | 44.5 | W24–3 | U | N |
| Sat 9/9 | Charlotte at Maryland | +24.5L20–38 | 50.0 | L20–38 | O | Y |
| Sat 9/16 | Charlotte vs Georgia State | +6.5L25–41 | 52.0 | L25–41 | O | N |
| Sat 9/23 | Charlotte at Florida | +28.0L7–22 | 48.5 | L7–22 | U | Y |
| Sat 9/30 | Charlotte at SMU | +22.5L16–34 | 53.0 | L16–34 | U | Y |
| — Bye Week — | ||||||
| Sat 10/14 | Charlotte vs Navy | +3.5L0–14 | 44.0 | L0–14 | U | N |
| Sat 10/21 | Charlotte at East Carolina | +6.0W10–7 | 39.5 | W10–7 | U | Y |
| Fri 10/27 | Charlotte vs Florida Atlantic | +3.5L16–38 | 45.5 | L16–38 | O | N |
| Sat 11/4 | Charlotte at Tulsa | +4.5W33–26 | 47.5 | W33–26 | O | Y |
| Sat 11/11 | Charlotte vs Memphis | +9.5L38–44 | 51.5 | L38–44 | O | Y |
| Sat 11/18 | Charlotte vs Rice | -0.5L7–28 | 46.5 | L7–28 | U | N |
| Sat 11/25 | Charlotte at South Florida | +7.5L14–48 | 56.5 | L14–48 | O | N |
SMU 2023 Schedule
SMU's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | SMU vs Louisiana Tech | -21.0W38–14 | 66.0 | W38–14 | U | Y |
| Sat 9/9 | SMU at Oklahoma | +16.5L11–28 | 68.5 | L11–28 | U | N |
| Sat 9/16 | SMU vs Prairie View A&M | -42.5W69–0 | 63.5 | W69–0 | O | Y |
| Sat 9/23 | SMU at TCU | +7.0L17–34 | 63.5 | L17–34 | U | N |
| Sat 9/30 | SMU vs Charlotte | -22.5W34–16 | 53.0 | W34–16 | U | N |
| — Bye Week — | ||||||
| Thu 10/12 | SMU at East Carolina | -11.5W31–10 | 48.5 | W31–10 | U | Y |
| Fri 10/20 | SMU at Temple | -24.0W55–0 | 53.0 | W55–0 | O | Y |
| Sat 10/28 | SMU vs Tulsa | -20.5W69–10 | 55.0 | W69–10 | O | Y |
| Sat 11/4 | SMU at Rice | -12.0W36–31 | 59.5 | W36–31 | O | N |
| Fri 11/10 | SMU vs North Texas | -21.5W45–21 | 67.5 | W45–21 | U | Y |
| Sat 11/18 | SMU at Memphis | -9.5W38–34 | 64.5 | W38–34 | O | N |
| Sat 11/25 | SMU vs Navy | -20.0W59–14 | 46.0 | W59–14 | O | Y |
| Sat 12/2 | SMU at Tulane | +4.0W26–14 | 50.5 | W26–14 | U | Y |
| Thu 12/28 | SMU vs Boston College | -13.5L14–23 | 49.0 | L14–23 | U | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2023 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 · 2023 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?
Charlotte +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 3 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +19.9
GC Edge (season-to-date)
Teams with this edge win 67.1% of games historically
Based on 4 games this season
Actual Result
CSS Battle
SMU
2 — 1 sequences
GC Battle
SMU
90.9 — 4.9 GC score
✓ Predicted correctly
Game Result
SMU won by 18
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on SMU with a solid GC edge. Teams with this profile have covered 53.0% of the time historically (n=330) — a mild lean.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Charlotte
Biff Poggi #1
1–2 (33%)
· Yr 1 at school
OC
Mike Miller
Yr 1
#1
DC
Ryan Osborn
Yr 1
#1
SMU
Rhett Lashlee #1
9–7 (56%)
· Yr 2 at school
OC
Casey Woods
Yr 2
#1
DC
Scott Symons
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: 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 ✓