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
UTSA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UTSA entering this game.
Momentum Control
58.4%
UTSA wins
Lean
Game Control
76%
UTSA wins
Strong
Advanced Stats
All 4 factors agree → UTSA
· 83.1% ATS historically when all four align
↓ See full breakdown
Tulsa 2026 Schedule
Tulsa's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Tulsa vs Oklahoma State | +6.5 | — | — | — | — |
| Sat 9/12 | Tulsa at Sam Houston | -17 | — | — | — | — |
| Sat 9/19 | Tulsa vs East Texas A&M | -20 | — | — | — | — |
| Sat 9/26 | Tulsa at Arkansas | +7.5 | — | — | — | — |
| Thu 10/1 | Tulsa vs North Texas | -6.5 | — | — | — | — |
| Sat 10/10 | Tulsa at Navy | +6 | — | — | — | — |
| Sat 10/17 | Tulsa at Rice | -10 | — | — | — | — |
| Fri 10/23 | Tulsa vs Army | -1 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 11/7 | Tulsa at Tulane | +3 | — | — | — | — |
| Sat 11/14 | Tulsa vs Florida Atlantic | -6 | — | — | — | — |
| Sat 11/21 | Tulsa vs Charlotte | -25 | — | — | — | — |
| Sat 11/28 | Tulsa at UTSA | +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 | -23 | — | — | — | — |
| Sat 9/12 | UTSA at Texas State | +1 | — | — | — | — |
| Sat 9/19 | UTSA at Texas | +28 | — | — | — | — |
| Sat 9/26 | UTSA vs Colorado State | -10.5 | — | — | — | — |
| Sat 10/3 | UTSA at Rice | -13 | — | — | — | — |
| Thu 10/8 | UTSA vs South Florida | -2 | — | — | — | — |
| Sat 10/17 | UTSA vs Navy | -1.5 | — | — | — | — |
| Sat 10/24 | UTSA at Tulane | +0.5 | — | — | — | — |
| — Bye Week — | ||||||
| Thu 11/5 | UTSA at Florida Atlantic | -3.5 | — | — | — | — |
| Sat 11/14 | UTSA vs North Texas | -9 | — | — | — | — |
| Sat 11/21 | UTSA at UAB | -14 | — | — | — | — |
| Sat 11/28 | UTSA vs Tulsa | -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
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 (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?
UTSA Edge
UTSA +0.67
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UTSA Edge
UTSA +20.7
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
Tulsa
Tre Lamb #113
4–8 (33%)
· Yr 2 at school
OC
Ty Darlington
Yr 2
#127
DC
Mike Gray
Yr 2
#97
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 ✓