Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors UTSA,
while Game Control favors Temple.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
61.3%
UTSA wins
Lean
Game Control
58.6%
Temple wins
Lean
Vegas Spread
UT San Antonio -14
O/U 56.0
William Hill (New Jersey)
Advanced Stats
All 4 factors agree → UTSA
· 83.1% ATS historically when all four align
↓ See full breakdown
UTSA 2023 Schedule
UTSA's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | UTSA at Houston | -2.5L14–17 | 59.5 | L14–17 | U | N |
| Sat 9/9 | UTSA vs Texas State | -13.5W20–13 | 66.5 | W20–13 | U | N |
| Fri 9/15 | UTSA vs Army | -7.0L29–37 | 42.0 | L29–37 | O | N |
| Sat 9/23 | UTSA at Tennessee | +24.0L14–45 | 59.0 | L14–45 | U | N |
| — Bye Week — | ||||||
| Sat 10/7 | UTSA at Temple | -14.0W49–34 | 56.0 | W49–34 | O | Y |
| Sat 10/14 | UTSA vs UAB | -9.0W41–20 | 67.0 | W41–20 | U | Y |
| Sat 10/21 | UTSA at Florida Atlantic | -2.5W36–10 | 58.5 | W36–10 | U | Y |
| Sat 10/28 | UTSA vs East Carolina | -17.5W41–27 | 48.0 | W41–27 | O | N |
| Sat 11/4 | UTSA at North Texas | -7.5W37–29 | 71.0 | W37–29 | U | Y |
| Sat 11/11 | UTSA vs Rice | -13.5W34–14 | 53.5 | W34–14 | U | Y |
| Fri 11/17 | UTSA vs South Florida | -14.5W49–21 | 65.5 | W49–21 | O | Y |
| Fri 11/24 | UTSA at Tulane | +2.5L16–29 | 51.5 | L16–29 | U | N |
| Tue 12/19 | UTSA vs Marshall | -7.0W35–17 | 47.0 | W35–17 | O | Y |
Temple 2023 Schedule
Temple's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Temple vs Akron | -9.5W24–21 | 55.0 | W24–21 | U | N |
| Sat 9/9 | Temple at Rutgers | +7.5L7–36 | 43.5 | L7–36 | U | N |
| Sat 9/16 | Temple vs Norfolk State | -30.5W41–9 | 55.0 | W41–9 | U | Y |
| Sat 9/23 | Temple vs Miami | +23.0L7–41 | 46.5 | L7–41 | O | N |
| Thu 9/28 | Temple at Tulsa | +3.0L26–48 | 56.0 | L26–48 | O | N |
| Sat 10/7 | Temple vs UTSA | +14.0L34–49 | 56.0 | L34–49 | O | N |
| Sat 10/14 | Temple at North Texas | +8.0L14–45 | 65.5 | L14–45 | U | N |
| Fri 10/20 | Temple vs SMU | +24.0L0–55 | 53.0 | L0–55 | O | N |
| — Bye Week — | ||||||
| Sat 11/4 | Temple vs Navy | +7.0W32–18 | 46.0 | W32–18 | O | Y |
| Sat 11/11 | Temple at South Florida | +7.5L23–27 | 70.5 | L23–27 | U | Y |
| Sat 11/18 | Temple at UAB | +8.0L24–34 | 61.5 | L24–34 | U | N |
| Fri 11/24 | Temple vs Memphis | +13.5L21–45 | 63.5 | L21–45 | O | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2023 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 · 2023 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 4 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Temple Edge
Temple +6.0
GC Edge (season-to-date)
Teams with this edge win 58.6% of games historically
Based on 5 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
31–12 (72%)
· Yr 4 at school
OC
Justin Burke
Yr 1
#1
DC
Jess Loepp
Yr 2
#1
Temple
Stan Drayton #1
5–10 (33%)
· Yr 2 at school
OC
Danny Langsdorf
Yr 2
#1
DC
Everett Withers
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 ✓