Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Temple,
while Game Control favors UTSA.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
58.4%
Temple wins
Lean
Game Control
58.3%
UTSA wins
Lean
Vegas Spread
UTSA -6.5
O/U 58.5
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 |
Temple 2025 Schedule
Temple's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | Temple at Massachusetts | -3.0W42–10 | 51.5 | W42–10 | O | Y |
| Sat 9/6 | Temple vs Howard | -28.0W55–7 | 47.0 | W55–7 | O | Y |
| Sat 9/13 | Temple vs Oklahoma | +23.5L3–42 | 50.5 | L3–42 | U | N |
| Sat 9/20 | Temple at Georgia Tech | +24.5L24–45 | 52.5 | L24–45 | O | Y |
| — Bye Week — | ||||||
| Sat 10/4 | Temple vs UTSA | +6.5W27–21 | 58.5 | W27–21 | U | Y |
| Sat 10/11 | Temple vs Navy | +10.0L31–32 | 52.5 | L31–32 | O | Y |
| Sat 10/18 | Temple at Charlotte | -10.0W49–14 | 47.5 | W49–14 | O | Y |
| Sat 10/25 | Temple at Tulsa | -4.5W38–37 | 52.5 | W38–37 | O | N |
| Sat 11/1 | Temple vs East Carolina | +5.5L14–45 | 58.5 | L14–45 | O | N |
| Sat 11/8 | Temple at Army | +7.5L13–14 | 45.5 | L13–14 | U | Y |
| — Bye Week — | ||||||
| Sat 11/22 | Temple vs Tulane | +7.5L13–37 | 54.5 | L13–37 | U | N |
| Fri 11/28 | Temple at North Texas | +20.0L25–52 | 65.5 | L25–52 | O | N |
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?
Temple Edge
Temple +0.67
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 3 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UTSA Edge
UTSA +5.2
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 4 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
CSS and GC disagree on this matchup. When the metrics split, historical cover rates are essentially random — treat this as 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
45–20 (69%)
· Yr 6 at school
OC
Justin Burke
Yr 3
#1
DC
Jess Loepp
Yr 3
#1
Temple
K. C. Keeler #1
0–0 (0%)
· Yr 1 at school
OC
Tyler Walker
Yr 1
#1
DC
Brian Smith
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 ✓