Matchup Prediction
UTSA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UTSA entering this game.
Momentum Control
73.7%
UTSA wins
Solid
Game Control
75.9%
UTSA wins
Solid
Vegas Spread
UT San Antonio -2
O/U 54.0
teamrankings
Advanced Stats
All 4 factors agree → UTSA
· 83.1% ATS historically when all four align
↓ See full breakdown
UTSA 2022 Schedule
UTSA's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | UTSA vs Houston | +3.5L35–37 | 61.5 | L35–37 | O | Y |
| Sat 9/10 | UTSA at Army | -2.0W41–38 | 54.0 | W41–38 | O | Y |
| Sat 9/17 | UTSA at Texas | +13.0L20–41 | 57.5 | L20–41 | O | N |
| Sat 9/24 | UTSA vs Texas Southern | -42.0W52–24 | 65.5 | W52–24 | O | N |
| Fri 9/30 | UTSA at Middle Tennessee | -4.5W45–30 | 64.0 | W45–30 | O | Y |
| Sat 10/8 | UTSA vs Western Kentucky | -6.5W31–28 | 72.5 | W31–28 | U | N |
| Fri 10/14 | UTSA at Florida International | -33.0W30–10 | 64.0 | W30–10 | U | N |
| Sat 10/22 | UTSA vs North Texas | -10.0W31–27 | 73.0 | W31–27 | U | N |
| — Bye Week — | ||||||
| Sat 11/5 | UTSA at UAB | -2.5W44–38 | 53.5 | W44–38 | O | Y |
| Sat 11/12 | UTSA vs Louisiana Tech | -17.0W51–7 | 68.5 | W51–7 | U | Y |
| Sat 11/19 | UTSA at Rice | -14.0W41–7 | 56.0 | W41–7 | U | Y |
| Sat 11/26 | UTSA vs UTEP | -16.5W34–31 | 56.5 | W34–31 | O | N |
| Fri 12/2 | UTSA vs North Texas | -8.5W48–27 | 70.0 | W48–27 | O | Y |
| Fri 12/16 | UTSA vs Troy | +2.0L12–18 | 55.5 | L12–18 | U | N |
Army 2022 Schedule
Army's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Army at Coastal Carolina | +1.5L28–38 | 54.0 | L28–38 | O | N |
| Sat 9/10 | Army vs UTSA | +2.0L38–41 | 54.0 | L38–41 | O | N |
| Sat 9/17 | Army vs Villanova | -14.0W49–10 | 56.0 | W49–10 | O | Y |
| — Bye Week — | ||||||
| Sat 10/1 | Army vs Georgia State | -8.5L14–31 | 54.0 | L14–31 | U | N |
| Sat 10/8 | Army at Wake Forest | +16.0L10–45 | 65.5 | L10–45 | U | N |
| Sat 10/15 | Army vs Colgate | -31.0W42–17 | 51.0 | W42–17 | O | N |
| Sat 10/22 | Army vs UL Monroe | -6.5W48–24 | 55.5 | W48–24 | O | Y |
| — Bye Week — | ||||||
| Sat 11/5 | Army vs Air Force | +7.0L7–13 | 40.5 | L7–13 | U | Y |
| Sat 11/12 | Army at Troy | +8.5L9–10 | 45.5 | L9–10 | U | Y |
| Sat 11/19 | Army vs UConn | -10.5W34–17 | 45.0 | W34–17 | O | Y |
| Sat 11/26 | Army at Massachusetts | -20.0W44–7 | 45.5 | W44–7 | O | Y |
| — Bye Week — | ||||||
| Sat 12/10 | Army vs Navy | +2.5W20–17 | 32.0 | W20–17 | O | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2022 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 · 2022 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 +1.00
CSS Edge (season-to-date)
Teams with this edge win 73.7% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UTSA Edge
UTSA +23.2
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 1 game this season
Actual Result
CSS Battle
Tie
1 — 1 sequences
✗ Predicted incorrectly
GC Battle
Army
54.6 — 19.0 GC score
✗ Predicted incorrectly
Game Result
UTSA won by 3
✓ Model called it
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
19–7 (73%)
· Yr 3 at school
OC
Will Stein
Yr 1
#1
DC
Jess Loepp
Yr 1
#1
Army
Jeff Monken #1
58–42 (58%)
· Yr 9 at school
OC
Brent Davis
Yr 2
#1
DC
Nate Woody
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: 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 ✓