Matchup Prediction
UTSA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UTSA entering this game.
Momentum Control
61.3%
UTSA wins
Lean
Game Control
75.9%
UTSA wins
Solid
Vegas Spread
UTSA -16.5
O/U 57.5
DraftKings
Advanced Stats
All 4 factors agree → UTSA
· 83.1% ATS historically when all four align
↓ See full breakdown
UTSA 2025 Schedule
UTSA's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | UTSA at Texas A&M | +21.5L24–42 | 56.5 | L24–42 | O | Y |
| Sat 9/6 | UTSA vs Texas State | -4.5L36–43 | 64.5 | L36–43 | O | N |
| Sat 9/13 | UTSA vs Incarnate Word | -21.0W48–20 | 62.5 | W48–20 | O | Y |
| Sat 9/20 | UTSA at Colorado State | -4.5W17–16 | 58.5 | W17–16 | U | N |
| — Bye Week — | ||||||
| Sat 10/4 | UTSA at Temple | -6.5L21–27 | 58.5 | L21–27 | U | N |
| Sat 10/11 | UTSA vs Rice | -8.5W61–13 | 48.5 | W61–13 | O | Y |
| Sat 10/18 | UTSA at North Texas | +4.0L17–55 | 64.5 | L17–55 | O | N |
| — Bye Week — | ||||||
| Thu 10/30 | UTSA vs Tulane | +5.5W48–26 | 54.5 | W48–26 | O | Y |
| Thu 11/6 | UTSA at South Florida | +14.0L23–55 | 66.5 | L23–55 | O | N |
| Sat 11/15 | UTSA at Charlotte | -16.5W28–7 | 57.5 | W28–7 | U | Y |
| Sat 11/22 | UTSA vs East Carolina | +2.0W58–24 | 62.5 | W58–24 | O | Y |
| Sat 11/29 | UTSA vs Army | -8.5L24–27 | 50.5 | L24–27 | O | N |
| Fri 12/26 | UTSA vs Florida International | -7.0W57–20 | 62.5 | W57–20 | O | Y |
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 |
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
→ UTSA
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ UTSA
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ UTSA
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?
UTSA Edge
UTSA +0.38
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 8 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UTSA Edge
UTSA +34.9
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 9 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UTSA 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
UTSA
Jeff Traylor #1
45–20 (69%)
· Yr 6 at school
OC
Justin Burke
Yr 3
#1
DC
Jess Loepp
Yr 3
#1
Charlotte
Tim Albin #1
0–0 (0%)
· Yr 1 at school
OC
Todd Fitch
Yr 1
#1
DC
Nate Faanes
Yr 1
#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 ✓