Matchup Prediction
Oklahoma
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Oklahoma entering this game.
Momentum Control
80.6%
Oklahoma wins
Strong
Game Control
75.9%
Oklahoma wins
Solid
Vegas Spread
Oklahoma -28
O/U 58.5
William Hill (New Jersey)
Advanced Stats
All 4 factors agree → Oklahoma
· 83.1% ATS historically when all four align
↓ See full breakdown
Oklahoma 2023 Schedule
Oklahoma's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Oklahoma vs Arkansas State | -36.0W73–0 | 57.5 | W73–0 | O | Y |
| Sat 9/9 | Oklahoma vs SMU | -16.5W28–11 | 68.5 | W28–11 | U | Y |
| Sat 9/16 | Oklahoma at Tulsa | -28.0W66–17 | 58.5 | W66–17 | O | Y |
| Sat 9/23 | Oklahoma at Cincinnati | -13.0W20–6 | 58.0 | W20–6 | U | Y |
| Sat 9/30 | Oklahoma vs Iowa State | -19.5W50–20 | 48.5 | W50–20 | O | Y |
| Sat 10/7 | Oklahoma vs Texas | +4.0W34–30 | 62.0 | W34–30 | O | Y |
| — Bye Week — | ||||||
| Sat 10/21 | Oklahoma vs UCF | -17.0W31–29 | 67.5 | W31–29 | U | N |
| Sat 10/28 | Oklahoma at Kansas | -7.0L33–38 | 66.5 | L33–38 | O | N |
| Sat 11/4 | Oklahoma at Oklahoma State | -6.0L24–27 | 61.5 | L24–27 | U | N |
| Sat 11/11 | Oklahoma vs West Virginia | -13.5W59–20 | 59.5 | W59–20 | O | Y |
| Sat 11/18 | Oklahoma at BYU | -24.5W31–24 | 58.5 | W31–24 | U | N |
| Fri 11/24 | Oklahoma vs TCU | -12.5W69–45 | 66.5 | W69–45 | O | Y |
| Thu 12/28 | Oklahoma vs Arizona | +2.5L24–38 | 59.5 | L24–38 | O | N |
Tulsa 2023 Schedule
Tulsa's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Thu 8/31 | Tulsa vs Arkansas-Pine Bluff | -41.0W42–7 | 52.5 | W42–7 | U | N |
| Sat 9/9 | Tulsa at Washington | +34.0L10–43 | 66.5 | L10–43 | U | Y |
| Sat 9/16 | Tulsa vs Oklahoma | +28.0L17–66 | 58.5 | L17–66 | O | N |
| Sat 9/23 | Tulsa at Northern Illinois | +3.5W22–14 | 54.5 | W22–14 | U | Y |
| Thu 9/28 | Tulsa vs Temple | -3.0W48–26 | 56.0 | W48–26 | O | Y |
| Sat 10/7 | Tulsa at Florida Atlantic | +3.0L17–20 | 54.5 | L17–20 | U | Y |
| — Bye Week — | ||||||
| Thu 10/19 | Tulsa vs Rice | -3.0L10–42 | 56.5 | L10–42 | U | N |
| Sat 10/28 | Tulsa at SMU | +20.5L10–69 | 55.0 | L10–69 | O | N |
| Sat 11/4 | Tulsa vs Charlotte | -4.5L26–33 | 47.5 | L26–33 | O | N |
| Sat 11/11 | Tulsa at Tulane | +24.5L22–24 | 52.5 | L22–24 | U | Y |
| Sat 11/18 | Tulsa vs North Texas | +1.5L28–35 | 69.5 | L28–35 | U | N |
| Sat 11/25 | Tulsa at East Carolina | +4.5W29–27 | 44.5 | W29–27 | O | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2023 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 · 2023 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?
Oklahoma Edge
Oklahoma +4.00
CSS Edge (season-to-date)
Teams with this edge win 80.6% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Oklahoma Edge
Oklahoma +53.0
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 2 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Oklahoma with a large edge. Historically, dominant teams like this are fully priced into the spread — the agreed-upon team covers just 50.2% of the time. The metrics predict game control better than they beat the number.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Oklahoma
Brent Venables #1
9–7 (56%)
· Yr 2 at school
OC
Jeff Lebby
Yr 2
#1
DC
Todd Bates
Yr 2
#1
Tulsa
Kevin Wilson #1
1–2 (33%)
· Yr 1 at school
OC
Steve Spurrier Jr.
Yr 1
#1
DC
Chris Polizzi
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 ✓