Sat, Nov 14 2026
·
Week 11
·
🏟 Alamodome
San Antonio, TX
·
Turf
·
65,000 cap
North Texas✈ 273 miSame TZ
Preseason projection — This game has not yet been played and 2026 in-season data is not yet available.
Edges are based on 2025 full-season performance.
Confidence will increase once in-season games are logged.
Matchup Prediction
North Texas
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
North Texas entering this game.
Momentum Control
61.3%
North Texas wins
Lean
Game Control
64.9%
North Texas wins
Lean
Advanced Stats
PPA + Success Rate agree → North Texas
· 73.9% ATS historically
↓ See full breakdown
North Texas 2026 Schedule
North Texas's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | North Texas at Indiana | +25.5 | — | — | — | — |
| Sat 9/12 | North Texas vs UNLV | -5.5 | — | — | — | — |
| Sat 9/19 | North Texas at Texas State | -0.5 | — | — | — | — |
| Sat 9/26 | North Texas vs Houston Christian | -28.5 | — | — | — | — |
| Thu 10/1 | North Texas at Tulsa | -8.5 | — | — | — | — |
| Sat 10/10 | North Texas vs Charlotte | -29 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/24 | North Texas at Navy | -1.5 | — | — | — | — |
| Thu 10/29 | North Texas vs Florida Atlantic | -16.5 | — | — | — | — |
| Sat 11/7 | North Texas vs Rice | -24.5 | — | — | — | — |
| Sat 11/14 | North Texas at UTSA | -1.5 | — | — | — | — |
| Sat 11/21 | North Texas at Tulane | -0 | — | — | — | — |
| Sat 11/28 | North Texas vs UAB | -24.5 | — | — | — | — |
UTSA 2026 Schedule
UTSA's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | UTSA vs UT Rio Grande Valley | -25.5 | — | — | — | — |
| Sat 9/12 | UTSA at Texas State | +3.5 | — | — | — | — |
| Sat 9/19 | UTSA at Texas | +27 | — | — | — | — |
| Sat 9/26 | UTSA vs Colorado State | -17 | — | — | — | — |
| Sat 10/3 | UTSA at Rice | -15.5 | — | — | — | — |
| Thu 10/8 | UTSA vs South Florida | +2 | — | — | — | — |
| Sat 10/17 | UTSA vs Navy | -2.5 | — | — | — | — |
| Sat 10/24 | UTSA at Tulane | +4 | — | — | — | — |
| — Bye Week — | ||||||
| Thu 11/5 | UTSA at Florida Atlantic | -7.5 | — | — | — | — |
| Sat 11/14 | UTSA vs North Texas | +1.5 | — | — | — | — |
| Sat 11/21 | UTSA at UAB | -15.5 | — | — | — | — |
| Sat 11/28 | UTSA vs Tulsa | -9.5 | — | — | — | — |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2025 season (prior year)
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
Split
Metrics disagree
Elite · 82.4% ATS
PPA + PPO + Havoc
Split
Metrics disagree
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ North Texas
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 (prior year — 2026 data not yet available) ·
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?
North Texas Edge
North Texas +0.96
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
North Texas Edge
North Texas +12.8
GC Edge (season-to-date)
Teams with this edge win 64.9% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
North Texas
Neal Brown #117
0–0 (0%)
· Yr 1 at school
OC
Neal Brown
Yr 1
#67
DC
Matt Powledge
Yr 1
#136
UTSA
Jeff Traylor #55
53–26 (67%)
· Yr 7 at school
OC
Rick Bowie
Yr 1
#67
DC
Jess Loepp
Yr 3
#122
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 ✓