Matchup Prediction
Toss-up — no clear edge
Neither metric shows a meaningful pre-game edge in this matchup.
Momentum Control
58.4%
—
Lean
Game Control
58.3%
Baylor wins
Lean
Vegas Spread
Oklahoma -3
O/U 61.5
teamrankings
Advanced Stats
PPA + Success Rate agree → Oklahoma
· 73.9% ATS historically
↓ See full breakdown
Baylor 2022 Schedule
Baylor's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Baylor vs UAlbany | -42.5W69–10 | 46.5 | W69–10 | O | Y |
| Sat 9/10 | Baylor at BYU | +2.5L20–26 | 54.5 | L20–26 | U | N |
| Sat 9/17 | Baylor vs Texas State | -30.0W42–7 | 53.0 | W42–7 | U | Y |
| Sat 9/24 | Baylor at Iowa State | +2.5W31–24 | 45.0 | W31–24 | O | Y |
| Sat 10/1 | Baylor vs Oklahoma State | -2.5L25–36 | 56.0 | L25–36 | O | N |
| — Bye Week — | ||||||
| Thu 10/13 | Baylor at West Virginia | -3.0L40–43 | 55.0 | L40–43 | O | N |
| Sat 10/22 | Baylor vs Kansas | -10.5W35–23 | 56.5 | W35–23 | O | Y |
| Sat 10/29 | Baylor at Texas Tech | +1.5W45–17 | 61.0 | W45–17 | O | Y |
| Sat 11/5 | Baylor at Oklahoma | +3.0W38–35 | 61.5 | W38–35 | O | Y |
| Sat 11/12 | Baylor vs Kansas State | -2.5L3–31 | 52.0 | L3–31 | U | N |
| Sat 11/19 | Baylor vs TCU | +2.0L28–29 | 58.0 | L28–29 | U | Y |
| Fri 11/25 | Baylor at Texas | +10.0L27–38 | 55.0 | L27–38 | O | N |
| Thu 12/22 | Baylor vs Air Force | -3.5L15–30 | 42.0 | L15–30 | O | N |
Oklahoma 2022 Schedule
Oklahoma's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Oklahoma vs UTEP | -31.0W45–13 | 58.0 | W45–13 | U | Y |
| Sat 9/10 | Oklahoma vs Kent State | -33.5W33–3 | 73.0 | W33–3 | U | N |
| Sat 9/17 | Oklahoma at Nebraska | -10.5W49–14 | 65.5 | W49–14 | U | Y |
| Sat 9/24 | Oklahoma vs Kansas State | -13.5L34–41 | 53.0 | L34–41 | O | N |
| Sat 10/1 | Oklahoma at TCU | -5.0L24–55 | 69.5 | L24–55 | O | N |
| Sat 10/8 | Oklahoma vs Texas | +7.5L0–49 | 65.0 | L0–49 | U | N |
| Sat 10/15 | Oklahoma vs Kansas | -10.5W52–42 | 66.0 | W52–42 | O | N |
| — Bye Week — | ||||||
| Sat 10/29 | Oklahoma at Iowa State | -1.5W27–13 | 58.0 | W27–13 | U | Y |
| Sat 11/5 | Oklahoma vs Baylor | -3.0L35–38 | 61.5 | L35–38 | O | N |
| Sat 11/12 | Oklahoma at West Virginia | -8.5L20–23 | 68.5 | L20–23 | U | N |
| Sat 11/19 | Oklahoma vs Oklahoma State | -7.0W28–13 | 67.5 | W28–13 | U | Y |
| Sat 11/26 | Oklahoma at Texas Tech | -2.0L48–51 | 65.5 | L48–51 | O | N |
| Thu 12/29 | Oklahoma vs Florida State | +10.5L32–35 | 67.0 | L32–35 | U | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2022 season
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
→ 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 · 2022 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?
Baylor +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 8 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Baylor Edge
Baylor +9.3
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 8 games this season
Actual Result
CSS Battle
Baylor
1 — 2 sequences
GC Battle
Baylor
14.4 — 61.7 GC score
✓ Predicted correctly
Game Result
Baylor won by 3
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
Baylor
Dave Aranda #1
14–9 (61%)
· Yr 3 at school
OC
Jeff Grimes
Yr 2
#1
DC
Ron Roberts
Yr 2
#1
Oklahoma
Brent Venables #1
0–0 (0%)
· Yr 1 at school
OC
Jeff Lebby
Yr 1
#1
DC
Todd Bates
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: 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 ✓