Matchup Prediction
UTSA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UTSA entering this game.
Momentum Control
58.4%
UTSA wins
Lean
Game Control
67.1%
UTSA wins
Solid
Vegas Spread
UTSA -8.5
O/U 50.5
ESPN Bet
Advanced Stats
All 4 factors agree → UTSA
· 83.1% ATS historically when all four align
↓ See full breakdown
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 |
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 |
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 +0.76
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 10 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UTSA Edge
UTSA +15.0
GC Edge (season-to-date)
Teams with this edge win 67.1% of games historically
Based on 11 games this season
Actual Result
CSS Battle
UTSA
2 — 1 sequences
✓ Predicted correctly
GC Battle
Army
37.2 — 42.2 GC score
✗ Predicted incorrectly
Game Result
Army won by 3
✗ Model missed it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UTSA 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
Army
Jeff Monken #1
81–57 (59%)
· Yr 12 at school
OC
Cody Worley
Yr 2
#1
DC
Nate Woody
Yr 3
#1
UTSA
Jeff Traylor #1
45–20 (69%)
· Yr 6 at school
OC
Justin Burke
Yr 3
#1
DC
Jess Loepp
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 ✓