Sun, Nov 26 2023
·
Week 13
·
🏟 Rose Bowl
Pasadena, CA
·
Turf
·
92,542 cap
California✈ 343 miSame TZ
Matchup Prediction
UCLA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UCLA entering this game.
Momentum Control
58.4%
UCLA wins
Lean
Game Control
58.6%
UCLA wins
Lean
Vegas Spread
UCLA -9.5
O/U 50.5
Bovada
Advanced Stats
All 4 factors agree → UCLA
· 83.1% ATS historically when all four align
↓ See full breakdown
California 2023 Schedule
California's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | California at North Texas | -5.0W58–21 | 53.5 | W58–21 | O | Y |
| Sat 9/9 | California vs Auburn | +5.0L10–14 | 55.5 | L10–14 | U | Y |
| Sat 9/16 | California vs Idaho | -14.5W31–17 | 52.5 | W31–17 | U | N |
| Sat 9/23 | California at Washington | +21.0L32–59 | 55.5 | L32–59 | O | N |
| Sat 9/30 | California vs Arizona State | -13.0W24–21 | 47.5 | W24–21 | U | N |
| Sat 10/7 | California vs Oregon State | +7.5L40–52 | 51.0 | L40–52 | O | N |
| Sat 10/14 | California at Utah | +9.0L14–34 | 42.5 | L14–34 | O | N |
| — Bye Week — | ||||||
| Sat 10/28 | California vs USC | +10.5L49–50 | 67.5 | L49–50 | O | Y |
| Sat 11/4 | California at Oregon | +26.5L19–63 | 61.5 | L19–63 | O | N |
| Sat 11/11 | California vs Washington State | -1.5W42–39 | 58.5 | W42–39 | O | Y |
| Sat 11/18 | California at Stanford | -6.5W27–15 | 52.5 | W27–15 | U | Y |
| Sat 11/25 | California at UCLA | +9.5W33–7 | 50.5 | W33–7 | U | Y |
| Sat 12/16 | California vs Texas Tech | +3.5L14–34 | 54.5 | L14–34 | U | N |
UCLA 2023 Schedule
UCLA's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | UCLA vs Coastal Carolina | -15.5W27–13 | 66.5 | W27–13 | U | N |
| Sat 9/9 | UCLA at San Diego State | -13.0W35–10 | 49.0 | W35–10 | U | Y |
| Sat 9/16 | UCLA vs North Carolina Central | -35.0W59–7 | 60.5 | W59–7 | O | Y |
| Sat 9/23 | UCLA at Utah | +3.0L7–14 | 50.5 | L7–14 | U | N |
| — Bye Week — | ||||||
| Sat 10/7 | UCLA vs Washington State | -3.0W25–17 | 60.0 | W25–17 | U | Y |
| Sat 10/14 | UCLA at Oregon State | +3.5L24–36 | 53.5 | L24–36 | O | N |
| Sat 10/21 | UCLA at Stanford | -17.0W42–7 | 52.0 | W42–7 | U | Y |
| Sat 10/28 | UCLA vs Colorado | -14.0W28–16 | 60.0 | W28–16 | U | N |
| Sat 11/4 | UCLA at Arizona | -2.5L10–27 | 50.0 | L10–27 | U | N |
| Sat 11/11 | UCLA vs Arizona State | -14.0L7–17 | 45.5 | L7–17 | U | N |
| Sat 11/18 | UCLA at USC | +6.0W38–20 | 65.5 | W38–20 | U | Y |
| Sat 11/25 | UCLA vs California | -9.5L7–33 | 50.5 | L7–33 | U | N |
| Sat 12/16 | UCLA vs Boise State | -6.5W35–22 | 46.0 | W35–22 | O | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2023 season
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
All 4 Agree
→ UCLA
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ UCLA
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ UCLA
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 · 2023 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?
UCLA Edge
UCLA +0.20
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 10 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UCLA Edge
UCLA +8.8
GC Edge (season-to-date)
Teams with this edge win 58.6% of games historically
Based on 11 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UCLA. Teams with this edge profile have covered 50.3% historically — essentially 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
UCLA
Chip Kelly #1
30–29 (51%)
· Yr 6 at school
OC
Chip Kelly
Yr 2
#1
DC
D'Anton Lynn
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: 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 ✓
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 ✓