Matchup Prediction
Army
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Army entering this game.
Momentum Control
58.4%
Army wins
Lean
Game Control
58.6%
Army wins
Lean
Vegas Spread
Army -17.5
O/U 45.5
DraftKings
Advanced Stats
All 4 factors agree → Army
· 83.1% ATS historically when all four align
↓ See full breakdown
Charlotte 2025 Schedule
Charlotte's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 8/29 | Charlotte vs App State | +8.5L11–34 | 53.5 | L11–34 | U | N |
| Sat 9/6 | Charlotte vs North Carolina | +16.5L3–20 | 49.5 | L3–20 | U | N |
| Sat 9/13 | Charlotte vs Monmouth | -3.0W42–35 | 66.5 | W42–35 | O | Y |
| Thu 9/18 | Charlotte vs Rice | +1.5L17–28 | 41.5 | L17–28 | O | N |
| — Bye Week — | ||||||
| Fri 10/3 | Charlotte at South Florida | +28.5L26–54 | 54.5 | L26–54 | O | Y |
| Sat 10/11 | Charlotte at Army | +17.5L7–24 | 45.5 | L7–24 | U | Y |
| Sat 10/18 | Charlotte vs Temple | +10.0L14–49 | 47.5 | L14–49 | O | N |
| Fri 10/24 | Charlotte vs North Texas | +25.5L20–54 | 60.5 | L20–54 | O | N |
| — Bye Week — | ||||||
| Sat 11/8 | Charlotte at East Carolina | +29.5L22–48 | 56.5 | L22–48 | O | Y |
| Sat 11/15 | Charlotte vs UTSA | +16.5L7–28 | 57.5 | L7–28 | U | N |
| Sat 11/22 | Charlotte at Georgia | +42.5L3–35 | 53.5 | L3–35 | U | Y |
| Sat 11/29 | Charlotte at Tulane | +31.5L0–27 | 52.5 | L0–27 | U | Y |
Army 2025 Schedule
Army's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 8/29 | Army vs Tarleton State | -14.5L27–30 | 47.5 | L27–30 | O | N |
| Sat 9/6 | Army at Kansas State | +17.0W24–21 | 48.5 | W24–21 | U | Y |
| — Bye Week — | ||||||
| Sat 9/20 | Army vs North Texas | +2.5L38–45 | 50.5 | L38–45 | O | N |
| Thu 9/25 | Army at East Carolina | +3.5L6–28 | 52.5 | L6–28 | U | N |
| Sat 10/4 | Army at UAB | -6.5W31–13 | 55.5 | W31–13 | U | Y |
| Sat 10/11 | Army vs Charlotte | -17.5W24–7 | 45.5 | W24–7 | U | N |
| Sat 10/18 | Army at Tulane | +10.0L17–24 | 44.5 | L17–24 | U | Y |
| — Bye Week — | ||||||
| Sat 11/1 | Army at Air Force | -1.5W20–17 | 48.5 | W20–17 | U | Y |
| Sat 11/8 | Army vs Temple | -7.5W14–13 | 45.5 | W14–13 | U | N |
| — Bye Week — | ||||||
| Sat 11/22 | Army vs Tulsa | -10.0L25–26 | 43.5 | L25–26 | O | N |
| Sat 11/29 | Army at UTSA | +8.5W27–24 | 50.5 | W27–24 | O | Y |
| — Bye Week — | ||||||
| Sat 12/13 | Army vs Navy | +6.0L16–17 | 38.0 | L16–17 | U | Y |
| Sat 12/27 | Army vs UConn | -5.5W41–16 | 41.5 | W41–16 | O | 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
→ Army
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Army
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Army
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?
Army Edge
Army +0.25
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 4 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Army Edge
Army +10.6
GC Edge (season-to-date)
Teams with this edge win 58.6% of games historically
Based on 4 games this season
Actual Result
CSS Battle
Army
1 — 0 sequences
✓ Predicted correctly
GC Battle
Army
85.7 — 5.7 GC score
✓ Predicted correctly
Game Result
Army won by 17
✓ Model called it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Army. 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
Charlotte
Tim Albin #1
0–0 (0%)
· Yr 1 at school
OC
Todd Fitch
Yr 1
#1
DC
Nate Faanes
Yr 1
#1
Army
Jeff Monken #1
81–57 (59%)
· Yr 12 at school
OC
Cody Worley
Yr 2
#1
DC
Nate Woody
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 ✓