Matchup Prediction
UTSA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UTSA entering this game.
Momentum Control
61.3%
UTSA wins
Lean
Game Control
64.9%
UTSA wins
Lean
Vegas Spread
UTSA -9.5
O/U 52.5
ESPN Bet
Advanced Stats
All 4 factors agree → UTSA
· 83.1% ATS historically when all four align
↓ See full breakdown
UTSA 2024 Schedule
UTSA's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | UTSA vs Kennesaw State | -24.0W28–16 | 49.5 | W28–16 | U | N |
| Sat 9/7 | UTSA at Texas State | +2.5L10–49 | 58.5 | L10–49 | O | N |
| Sat 9/14 | UTSA at Texas | +36.5L7–56 | 56.5 | L7–56 | O | N |
| Sat 9/21 | UTSA vs Houston Christian | -35.5W45–7 | 54.5 | W45–7 | U | Y |
| Sat 9/28 | UTSA at East Carolina | +2.0L20–30 | 53.5 | L20–30 | U | N |
| — Bye Week — | ||||||
| Sat 10/12 | UTSA at Rice | -3.5L27–29 | 51.0 | L27–29 | O | N |
| Sat 10/19 | UTSA vs Florida Atlantic | -4.0W38–24 | 52.5 | W38–24 | O | Y |
| Sat 10/26 | UTSA at Tulsa | -9.5L45–46 | 52.5 | L45–46 | O | N |
| Sat 11/2 | UTSA vs Memphis | +7.0W44–36 | 62.0 | W44–36 | O | Y |
| — Bye Week — | ||||||
| Fri 11/15 | UTSA vs North Texas | -1.0W48–27 | 73.0 | W48–27 | O | Y |
| Fri 11/22 | UTSA vs Temple | -16.5W51–27 | 56.0 | W51–27 | O | Y |
| Sat 11/30 | UTSA at Army | +6.5L24–29 | 53.5 | L24–29 | U | Y |
| Mon 12/23 | UTSA vs Coastal Carolina | -12.5W44–15 | 56.5 | W44–15 | O | Y |
Tulsa 2024 Schedule
Tulsa's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Thu 8/29 | Tulsa vs Northwestern State | -37.5W62–28 | 55.5 | W62–28 | O | N |
| Sat 9/7 | Tulsa at Arkansas State | +9.5L24–28 | 65.5 | L24–28 | U | Y |
| Sat 9/14 | Tulsa vs Oklahoma State | +17.5L10–45 | 62.5 | L10–45 | U | N |
| Sat 9/21 | Tulsa at Louisiana Tech | +3.0W23–20 | 56.5 | W23–20 | U | Y |
| Sat 9/28 | Tulsa at North Texas | +7.0L20–52 | 65.5 | L20–52 | O | N |
| Sat 10/5 | Tulsa vs Army | +13.5L7–49 | 50.5 | L7–49 | O | N |
| — Bye Week — | ||||||
| Sat 10/19 | Tulsa at Temple | +3.5L10–20 | 51.5 | L10–20 | U | N |
| Sat 10/26 | Tulsa vs UTSA | +9.5W46–45 | 52.5 | W46–45 | O | Y |
| Sat 11/2 | Tulsa at UAB | +2.5L21–59 | 57.5 | L21–59 | O | N |
| — Bye Week — | ||||||
| Thu 11/14 | Tulsa vs East Carolina | +16.0L31–38 | 63.5 | L31–38 | O | Y |
| Sat 11/23 | Tulsa at South Florida | +17.5L30–63 | 60.0 | L30–63 | O | N |
| Sat 11/30 | Tulsa vs Florida Atlantic | +2.5L16–63 | 57.5 | L16–63 | O | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2024 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 · 2024 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 6 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UTSA Edge
UTSA +11.4
GC Edge (season-to-date)
Teams with this edge win 64.9% of games historically
Based on 7 games this season
Actual Result
CSS Battle
Tulsa
2 — 1 sequences
✗ Predicted incorrectly
GC Battle
UTSA
6.3 — 84.2 GC score
✓ Predicted correctly
Game Result
Tulsa won by 1
✗ Model missed it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UTSA. Teams with this edge profile have covered 50.3% historically — essentially 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
39–14 (74%)
· Yr 5 at school
OC
Justin Burke
Yr 2
#1
DC
Jess Loepp
Yr 3
#1
Tulsa
Kevin Wilson #1
4–8 (33%)
· Yr 2 at school
OC
Steve Spurrier Jr.
Yr 2
#1
DC
Chris Polizzi
Yr 2
#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 ✓