Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Rice,
while Game Control favors UTSA.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
61.3%
Rice wins
Lean
Game Control
58.6%
UTSA wins
Lean
Vegas Spread
UTSA -8.5
O/U 48.5
DraftKings
Advanced Stats
All 4 factors agree → UTSA
· 83.1% ATS historically when all four align
↓ See full breakdown
Rice 2025 Schedule
Rice's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | Rice at Louisiana | +14.5W14–12 | 49.5 | W14–12 | U | Y |
| Sat 9/6 | Rice vs Houston | +13.5L9–35 | 38.5 | L9–35 | O | N |
| Sat 9/13 | Rice vs Prairie View A&M | -29.5W38–17 | 48.5 | W38–17 | O | N |
| Thu 9/18 | Rice at Charlotte | -1.5W28–17 | 41.5 | W28–17 | O | Y |
| Sat 9/27 | Rice at Navy | +14.0L13–21 | 45.5 | L13–21 | U | Y |
| Sat 10/4 | Rice vs Florida Atlantic | -4.5L21–27 | 54.5 | L21–27 | U | N |
| Sat 10/11 | Rice at UTSA | +8.5L13–61 | 48.5 | L13–61 | O | N |
| — Bye Week — | ||||||
| Sat 10/25 | Rice vs UConn | +10.5W37–34 | 48.5 | W37–34 | O | Y |
| Fri 10/31 | Rice vs Memphis | +13.5L14–38 | 48.5 | L14–38 | O | N |
| Sat 11/8 | Rice vs UAB | +1.5W24–17 | 51.5 | W24–17 | U | Y |
| — Bye Week — | ||||||
| Sat 11/22 | Rice vs North Texas | +18.5L24–56 | 57.0 | L24–56 | O | N |
| Sat 11/29 | Rice at South Florida | +28.5L3–52 | 57.5 | L3–52 | U | N |
| Fri 1/2 | Rice vs Texas State | +19.5L10–41 | 55.5 | L10–41 | U | N |
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?
Rice Edge
Rice +0.40
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?
UTSA Edge
UTSA +8.7
GC Edge (season-to-date)
Teams with this edge win 58.6% of games historically
Based on 5 games this season
Actual Result
CSS Battle
UTSA
3 — 1 sequences
✗ Predicted incorrectly
GC Battle
UTSA
96.1 — 2.7 GC score
✓ Predicted correctly
Game Result
UTSA won by 48
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
Rice
Scott Abell #1
0–0 (0%)
· Yr 1 at school
OC
Vince Munch
Yr 1
#1
DC
Jon Kay
Yr 1
#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 ✓