California at Stanford Week 12 College Football Matchup California at Stanford Matchup - Week 12
Sat, Nov 18 2023 · Week 12 · 🏟 Stanford Stadium Stanford, CA · Turf · 50,424 cap
27 15
Final
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📊 Punt & Rally Projection
California
33
Stanford
23
P&R Line California -10
P&R Total O/U 56.5
Confidence 90 High
Vegas California -6.5 · O/U 52.5
Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Stanford, while Game Control favors California. Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
58.4%
Stanford wins
Lean
Game Control
64.9%
California wins
Lean
Vegas Spread
California -6.5
O/U 52.5
ESPN Bet
Advanced Stats
All 4 factors agree → California · 83.1% ATS historically when all four align
↓ See full breakdown
California 2023 Schedule
California's 2023 Schedule
DateMatchupSpreadTotalResultO/UCover
Sat 9/2California at North Texas-5.0W58–2153.5W58–21OY
Sat 9/9California vs Auburn+5.0L10–1455.5L10–14UY
Sat 9/16California vs Idaho-14.5W31–1752.5W31–17UN
Sat 9/23California at Washington+21.0L32–5955.5L32–59ON
Sat 9/30California vs Arizona State-13.0W24–2147.5W24–21UN
Sat 10/7California vs Oregon State+7.5L40–5251.0L40–52ON
Sat 10/14California at Utah+9.0L14–3442.5L14–34ON
— Bye Week —
Sat 10/28California vs USC+10.5L49–5067.5L49–50OY
Sat 11/4California at Oregon+26.5L19–6361.5L19–63ON
Sat 11/11California vs Washington State-1.5W42–3958.5W42–39OY
Sat 11/18California at Stanford-6.5W27–1552.5W27–15UY
Sat 11/25California at UCLA+9.5W33–750.5W33–7UY
Sat 12/16California vs Texas Tech+3.5L14–3454.5L14–34UN
Stanford 2023 Schedule
Stanford's 2023 Schedule
DateMatchupSpreadTotalResultO/UCover
Fri 9/1Stanford at Hawai'i-2.0W37–2454.0W37–24OY
Sat 9/9Stanford at USC+28.5L10–5670.5L10–56UN
Sat 9/16Stanford vs Sacramento State-7.0L23–3060.5L23–30UN
Sat 9/23Stanford vs Arizona+13.0L20–2160.0L20–21UY
Sat 9/30Stanford vs Oregon+27.0L6–4259.5L6–42UN
— Bye Week —
Fri 10/13Stanford at Colorado+13.0W46–4359.0W46–43OY
Sat 10/21Stanford vs UCLA+17.0L7–4252.0L7–42UN
Sat 10/28Stanford vs Washington+27.5L33–4262.0L33–42OY
Sat 11/4Stanford at Washington State+13.0W10–759.5W10–7UY
Sat 11/11Stanford at Oregon State+21.5L17–6251.5L17–62ON
Sat 11/18Stanford vs California+6.5L15–2752.5L15–27UN
Sat 11/25Stanford vs Notre Dame+26.0L23–5650.5L23–56ON
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) · 2023 season
California PPA Edge
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
California #61
+0.556
Stanford #114
+0.309
California Edge
PPA Passing
Pass efficiency edge · Strong predictor
California #92
+0.662
Stanford #106
+0.459
California Edge
Havoc Total
Def. disruption rate · Strong predictor
California #75
0.160
Stanford #133
0.111
TFLs, sacks, PBUs, forced fumbles — higher is better
California Edge
Points Per Opp
Drive-finishing edge · Strong predictor
California #46
+9.341
Stanford #101
+7.804
California Edge
Success Rate
Play consistency edge · Solid predictor
California #88
+0.920
Stanford #114
+0.826
California Edge
Field Position
Avg start (lower=better) · Solid predictor
California #18
68.8
Stanford #119
72.8
Avg yards from own endzone to average start — lower is better · longer bar = better field position
California Edge
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
California Rated Higher
Overall Power Rating
California
5.3
Stanford
-5.0
Offense Rating
California
19.2
Stanford
11.1
Defense Rating (lower = better defense)
California
13.9
Stanford
16.0
Power ratings updated throughout the season as results accumulate
Momentum Control (CSS)
Consecutive Scoring Sequences Who builds scoring momentum? Stanford Edge
Avg sequences created per game
California #72
0.78
Stanford #67
1.11
Avg sequences allowed per game (lower is better)
California #116
1.89
Stanford #123
1.89
Stanford +0.33
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 9 games this season
Game Control (GC)
Win Probability Dominance Who controls games start to finish? California Edge
Avg GC score per game (offense)
California #1
39.2
Stanford #1
22.5
Avg GC score allowed per game (lower is better)
California #74
44.1
Stanford #128
59.7
California +16.7
GC Edge (season-to-date)
Teams with this edge win 64.9% of games historically
Based on 10 games this season
Actual Result
CSS Battle
Stanford
1 — 0 sequences
✓ Predicted correctly
GC Battle
California
9.5 — 77.9 GC score
✓ Predicted correctly
Game Result
California won by 12
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
California
Justin Wilcox #1
32–37 (46%) · Yr 7 at school
OC Jake Spavital Yr 1 #1
DC Peter Sirmon Yr 3 #1
Staff Rating
0.00 #1
Stanford
Troy Taylor #1
1–2 (33%) · Yr 1 at school
OC Troy Taylor Yr 1 #1
DC Bobby April III Yr 1 #1
Staff Rating
0.00 #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