Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Temple,
while Game Control favors Oklahoma.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
78.1%
Temple wins
Strong
Game Control
58.3%
Oklahoma wins
Lean
Vegas Spread
Oklahoma -23.5
O/U 50.5
DraftKings
Advanced Stats
All 4 factors agree → Oklahoma
· 83.1% ATS historically when all four align
↓ See full breakdown
Oklahoma 2025 Schedule
Oklahoma's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | Oklahoma vs Illinois State | -39.5W35–3 | 61.0 | W35–3 | U | N |
| Sat 9/6 | Oklahoma vs Michigan | -3.0W24–13 | 47.5 | W24–13 | U | Y |
| Sat 9/13 | Oklahoma at Temple | -23.5W42–3 | 50.5 | W42–3 | U | Y |
| Sat 9/20 | Oklahoma vs Auburn | -6.5W24–17 | 47.5 | W24–17 | U | Y |
| — Bye Week — | ||||||
| Sat 10/4 | Oklahoma vs Kent State | -46.5W44–0 | 53.5 | W44–0 | U | N |
| Sat 10/11 | Oklahoma vs Texas | +2.5L6–23 | 44.5 | L6–23 | U | N |
| Sat 10/18 | Oklahoma at South Carolina | -4.5W26–7 | 42.5 | W26–7 | U | Y |
| Sat 10/25 | Oklahoma vs Ole Miss | -5.5L26–34 | 52.5 | L26–34 | O | N |
| Sat 11/1 | Oklahoma at Tennessee | +3.0W33–27 | 55.5 | W33–27 | O | Y |
| — Bye Week — | ||||||
| Sat 11/15 | Oklahoma at Alabama | +6.5W23–21 | 45.5 | W23–21 | U | Y |
| Sat 11/22 | Oklahoma vs Missouri | -4.5W17–6 | 42.5 | W17–6 | U | Y |
| Sat 11/29 | Oklahoma vs LSU | -11.5W17–13 | 36.5 | W17–13 | U | N |
| Fri 12/19 | Oklahoma vs Alabama | -1.5L24–34 | 42.0 | L24–34 | O | N |
Temple 2025 Schedule
Temple's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | Temple at Massachusetts | -3.0W42–10 | 51.5 | W42–10 | O | Y |
| Sat 9/6 | Temple vs Howard | -28.0W55–7 | 47.0 | W55–7 | O | Y |
| Sat 9/13 | Temple vs Oklahoma | +23.5L3–42 | 50.5 | L3–42 | U | N |
| Sat 9/20 | Temple at Georgia Tech | +24.5L24–45 | 52.5 | L24–45 | O | Y |
| — Bye Week — | ||||||
| Sat 10/4 | Temple vs UTSA | +6.5W27–21 | 58.5 | W27–21 | U | Y |
| Sat 10/11 | Temple vs Navy | +10.0L31–32 | 52.5 | L31–32 | O | Y |
| Sat 10/18 | Temple at Charlotte | -10.0W49–14 | 47.5 | W49–14 | O | Y |
| Sat 10/25 | Temple at Tulsa | -4.5W38–37 | 52.5 | W38–37 | O | N |
| Sat 11/1 | Temple vs East Carolina | +5.5L14–45 | 58.5 | L14–45 | O | N |
| Sat 11/8 | Temple at Army | +7.5L13–14 | 45.5 | L13–14 | U | Y |
| — Bye Week — | ||||||
| Sat 11/22 | Temple vs Tulane | +7.5L13–37 | 54.5 | L13–37 | U | N |
| Fri 11/28 | Temple at North Texas | +20.0L25–52 | 65.5 | L25–52 | O | N |
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
→ Oklahoma
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Oklahoma
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Oklahoma
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?
Temple Edge
Temple +2.00
CSS Edge (season-to-date)
Teams with this edge win 78.1% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Oklahoma Edge
Oklahoma +7.0
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 2 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
Oklahoma
Brent Venables #1
22–16 (58%)
· Yr 4 at school
OC
Ben Arbuckle
Yr 1
#1
DC
Zac Alley
Yr 2
#1
Temple
K. C. Keeler #1
0–0 (0%)
· Yr 1 at school
OC
Tyler Walker
Yr 1
#1
DC
Brian Smith
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 ✓