Sat, Sep 11 2021
·
Week 2
·
🏟 Amon G. Carter Stadium
Fort Worth, TX
·
Turf
·
45,000 cap
California✈ 1,443 mi+2 hr TZ
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
76%
TCU wins
Strong
Vegas Spread
TCU -11.5
O/U 46.5
teamrankings
Advanced Stats
All 4 factors agree → California
· 83.1% ATS historically when all four align
↓ See full breakdown
California 2021 Schedule
California's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | California vs Nevada | -2.5L17–22 | 52.5 | L17–22 | U | N |
| Sat 9/11 | California at TCU | +11.5L32–34 | 46.5 | L32–34 | O | Y |
| Sat 9/18 | California vs Sacramento State | -24.5W42–30 | 49.0 | W42–30 | O | N |
| Sat 9/25 | California at Washington | +7.5L24–31 | 47.5 | L24–31 | O | Y |
| Sat 10/2 | California vs Washington State | -7.5L6–21 | 52.5 | L6–21 | U | N |
| — Bye Week — | ||||||
| Fri 10/15 | California at Oregon | +13.5L17–24 | 53.5 | L17–24 | U | Y |
| Sat 10/23 | California vs Colorado | -8.0W26–3 | 44.0 | W26–3 | U | Y |
| Sat 10/30 | California vs Oregon State | +2.5W39–25 | 56.5 | W39–25 | O | Y |
| Sat 11/6 | California at Arizona | -7.0L3–10 | 47.0 | L3–10 | U | N |
| Sat 11/13 | California vs USC | +2.0 | 52.5 | — | — | — |
| Sat 11/20 | California at Stanford | -2.5W41–11 | 46.0 | W41–11 | O | Y |
| Sat 11/27 | California at UCLA | +6.5L14–42 | 58.5 | L14–42 | U | N |
| Sat 12/4 | California vs USC | -4.5W24–14 | 57.5 | W24–14 | U | Y |
TCU 2021 Schedule
TCU's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | TCU vs Duquesne | -42.0W45–3 | 54.5 | W45–3 | U | N |
| Sat 9/11 | TCU vs California | -11.5W34–32 | 46.5 | W34–32 | O | N |
| — Bye Week — | ||||||
| Sat 9/25 | TCU vs SMU | -8.0L34–42 | 66.0 | L34–42 | O | N |
| Sat 10/2 | TCU vs Texas | +3.5L27–32 | 65.5 | L27–32 | U | N |
| Sat 10/9 | TCU at Texas Tech | -2.5W52–31 | 60.0 | W52–31 | O | Y |
| Sat 10/16 | TCU at Oklahoma | +12.5L31–52 | 64.5 | L31–52 | O | N |
| Sat 10/23 | TCU vs West Virginia | -5.0L17–29 | 58.0 | L17–29 | U | N |
| Sat 10/30 | TCU at Kansas State | +3.5L12–31 | 58.5 | L12–31 | U | N |
| Sat 11/6 | TCU vs Baylor | +7.5W30–28 | 57.0 | W30–28 | O | Y |
| Sat 11/13 | TCU at Oklahoma State | +11.0L17–63 | 53.5 | L17–63 | O | N |
| Sat 11/20 | TCU vs Kansas | -21.0W31–28 | 64.0 | W31–28 | U | N |
| Fri 11/26 | TCU at Iowa State | +16.0L14–48 | 61.5 | L14–48 | O | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2021 season
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
All 4 Agree
→ California
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ California
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ California
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 · 2021 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?
California Edge
California +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 0 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
TCU Edge
TCU +59.0
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 1 game this season
Actual Result
CSS Battle
TCU
2 — 0 sequences
✗ Predicted incorrectly
GC Battle
TCU
49.6 — 29.2 GC score
✓ Predicted correctly
Game Result
TCU won by 2
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
California
Justin Wilcox #1
22–23 (49%)
· Yr 5 at school
OC
Bill Musgrave
Yr 1
#1
DC
Peter Sirmon
Yr 1
#1
TCU
Gary Patterson #1
180–74 (71%)
· Yr 22 at school
OC
Doug Meacham
Yr 1
#1
DC
Chad Glasgow
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 ✓