Sat, Oct 29 2022
·
Week 9
·
🏟 Mountaineer Field at Milan Puskar Stadium
Morgantown, WV
·
Turf
·
60,000 cap
TCU✈ 1,080 mi+1 hr TZ
Matchup Prediction
TCU
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
TCU entering this game.
Momentum Control
73.7%
TCU wins
Solid
Game Control
75.9%
TCU wins
Solid
Vegas Spread
TCU -7
O/U 70.0
teamrankings
Advanced Stats
PPA + Success Rate agree → TCU
· 73.9% ATS historically
↓ See full breakdown
TCU 2022 Schedule
TCU's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 9/2 | TCU at Colorado | -13.5W38–13 | 59.0 | W38–13 | U | Y |
| Sat 9/10 | TCU vs Tarleton State | -40.0W59–17 | 66.5 | W59–17 | O | Y |
| — Bye Week — | ||||||
| Sat 9/24 | TCU at SMU | -2.5W42–34 | 72.0 | W42–34 | O | Y |
| Sat 10/1 | TCU vs Oklahoma | +5.0W55–24 | 69.5 | W55–24 | O | Y |
| Sat 10/8 | TCU at Kansas | -7.0W38–31 | 70.0 | W38–31 | U | N |
| Sat 10/15 | TCU vs Oklahoma State | -5.0W43–40 | 69.5 | W43–40 | O | N |
| Sat 10/22 | TCU vs Kansas State | -3.5W38–28 | 54.5 | W38–28 | O | Y |
| Sat 10/29 | TCU at West Virginia | -7.0W41–31 | 70.0 | W41–31 | O | Y |
| Sat 11/5 | TCU vs Texas Tech | -8.5W34–24 | 69.0 | W34–24 | U | Y |
| Sat 11/12 | TCU at Texas | +7.5W17–10 | 65.0 | W17–10 | U | Y |
| Sat 11/19 | TCU at Baylor | -2.0W29–28 | 58.0 | W29–28 | U | N |
| Sat 11/26 | TCU vs Iowa State | -9.5W62–14 | 46.0 | W62–14 | O | Y |
| Sat 12/3 | TCU vs Kansas State | -1.0L28–31 | 60.5 | L28–31 | U | N |
| Sat 12/31 | TCU vs Michigan | +8.0W51–45 | 56.0 | W51–45 | O | Y |
| Mon 1/9 | TCU vs Georgia | +13.5L7–65 | 62.0 | L7–65 | O | N |
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
→ TCU
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?
TCU Edge
TCU +1.33
CSS Edge (season-to-date)
Teams with this edge win 73.7% of games historically
Based on 6 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
TCU Edge
TCU +33.5
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 7 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on TCU 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
TCU
Sonny Dykes #1
0–0 (0%)
· Yr 1 at school
OC
Garrett Riley
Yr 1
#1
DC
Joseph Gillespie
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 ✓