Sat, Nov 12 2022
·
Week 11
·
🏟 Mountaineer Field at Milan Puskar Stadium
Morgantown, WV
·
Turf
·
60,000 cap
Oklahoma✈ 1,005 mi+1 hr TZ
Matchup Prediction
Oklahoma
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Oklahoma entering this game.
Momentum Control
61.3%
Oklahoma wins
Lean
Game Control
75.9%
Oklahoma wins
Solid
Vegas Spread
Oklahoma -8.5
O/U 68.5
teamrankings
Advanced Stats
PPA + Success Rate agree → Oklahoma
· 73.9% ATS historically
↓ See full breakdown
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 |
West Virginia 2022 Schedule
West Virginia's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Thu 9/1 | West Virginia at Pittsburgh | +7.5L31–38 | 50.0 | L31–38 | O | Y |
| Sat 9/10 | West Virginia vs Kansas | -14.0L42–55 | 59.5 | L42–55 | O | N |
| Sat 9/17 | West Virginia vs Towson | -41.0W65–7 | 58.5 | W65–7 | O | Y |
| Thu 9/22 | West Virginia at Virginia Tech | -2.0W33–10 | 49.5 | W33–10 | U | Y |
| Sat 10/1 | West Virginia at Texas | +7.5L20–38 | 61.0 | L20–38 | U | N |
| — Bye Week — | ||||||
| Thu 10/13 | West Virginia vs Baylor | +3.0W43–40 | 55.0 | W43–40 | O | Y |
| Sat 10/22 | West Virginia at Texas Tech | +5.0L10–48 | 65.5 | L10–48 | U | N |
| Sat 10/29 | West Virginia vs TCU | +7.0L31–41 | 70.0 | L31–41 | O | N |
| Sat 11/5 | West Virginia at Iowa State | +6.5L14–31 | 49.5 | L14–31 | U | N |
| Sat 11/12 | West Virginia vs Oklahoma | +8.5W23–20 | 68.5 | W23–20 | U | Y |
| Sat 11/19 | West Virginia vs Kansas State | +8.0L31–48 | 54.5 | L31–48 | O | N |
| Sat 11/26 | West Virginia at Oklahoma State | +5.5W24–19 | 59.5 | W24–19 | 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?
Oklahoma Edge
Oklahoma +0.50
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 8 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Oklahoma Edge
Oklahoma +21.3
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 9 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
0–0 (0%)
· Yr 1 at school
OC
Jeff Lebby
Yr 1
#1
DC
Todd Bates
Yr 1
#1
West Virginia
Neal Brown #1
17–18 (49%)
· Yr 4 at school
OC
Graham Harrell
Yr 1
#1
DC
ShaDon Brown
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 ✓