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
58.3%
UTSA wins
Lean
Vegas Spread
Troy -2
O/U 55.5
Bovada
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 |
Troy 2022 Schedule
Troy's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Troy at Ole Miss | +21.5L10–28 | 56.5 | L10–28 | U | Y |
| Sat 9/10 | Troy vs Alabama A&M | -37.5W38–17 | 52.0 | W38–17 | O | N |
| Sat 9/17 | Troy at App State | +14.0L28–32 | 52.0 | L28–32 | O | Y |
| Sat 9/24 | Troy vs Marshall | +3.0W16–7 | 51.5 | W16–7 | U | Y |
| Sat 10/1 | Troy at Western Kentucky | +5.0W34–27 | 55.0 | W34–27 | O | Y |
| Sat 10/8 | Troy vs Southern Miss | -7.0W27–10 | 44.0 | W27–10 | U | Y |
| Sat 10/15 | Troy vs Texas State | -16.5W17–14 | 47.0 | W17–14 | U | N |
| Thu 10/20 | Troy at South Alabama | +3.0W10–6 | 47.0 | W10–6 | U | Y |
| — Bye Week — | ||||||
| Sat 11/5 | Troy at Louisiana | -3.5W23–17 | 42.5 | W23–17 | U | Y |
| Sat 11/12 | Troy vs Army | -8.5W10–9 | 45.5 | W10–9 | U | N |
| Sat 11/19 | Troy vs UL Monroe | -15.0W34–16 | 48.5 | W34–16 | O | Y |
| Sat 11/26 | Troy at Arkansas State | -13.5W48–19 | 43.5 | W48–19 | O | Y |
| Sat 12/3 | Troy vs Coastal Carolina | -7.0W45–26 | 49.0 | W45–26 | O | Y |
| Fri 12/16 | Troy vs UTSA | -2.0W18–12 | 55.5 | W18–12 | U | 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 +0.50
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 12 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UTSA Edge
UTSA +10.7
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 13 games this season
Actual Result
CSS Battle
Troy
1 — 0 sequences
✗ Predicted incorrectly
GC Battle
UTSA
30.7 — 51.1 GC score
✓ Predicted correctly
Game Result
Troy won by 6
✗ Model missed it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UTSA. 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
UTSA
Jeff Traylor #1
19–7 (73%)
· Yr 3 at school
OC
Will Stein
Yr 1
#1
DC
Jess Loepp
Yr 1
#1
Troy
Jon Sumrall #1
0–0 (0%)
· Yr 1 at school
OC
Joe Craddock
Yr 1
#1
DC
Shiel Wood
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: 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 ✓