Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Stanford,
while Game Control favors UCLA.
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
75.9%
UCLA wins
Solid
Vegas Spread
UCLA -17
O/U 52.0
William Hill (New Jersey)
Advanced Stats
All 4 factors agree → UCLA
· 83.1% ATS historically when all four align
↓ See full breakdown
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 |
Stanford 2023 Schedule
Stanford's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 9/1 | Stanford at Hawai'i | -2.0W37–24 | 54.0 | W37–24 | O | Y |
| Sat 9/9 | Stanford at USC | +28.5L10–56 | 70.5 | L10–56 | U | N |
| Sat 9/16 | Stanford vs Sacramento State | -7.0L23–30 | 60.5 | L23–30 | U | N |
| Sat 9/23 | Stanford vs Arizona | +13.0L20–21 | 60.0 | L20–21 | U | Y |
| Sat 9/30 | Stanford vs Oregon | +27.0L6–42 | 59.5 | L6–42 | U | N |
| — Bye Week — | ||||||
| Fri 10/13 | Stanford at Colorado | +13.0W46–43 | 59.0 | W46–43 | O | Y |
| Sat 10/21 | Stanford vs UCLA | +17.0L7–42 | 52.0 | L7–42 | U | N |
| Sat 10/28 | Stanford vs Washington | +27.5L33–42 | 62.0 | L33–42 | O | Y |
| Sat 11/4 | Stanford at Washington State | +13.0W10–7 | 59.5 | W10–7 | U | Y |
| Sat 11/11 | Stanford at Oregon State | +21.5L17–62 | 51.5 | L17–62 | O | N |
| Sat 11/18 | Stanford vs California | +6.5L15–27 | 52.5 | L15–27 | U | N |
| Sat 11/25 | Stanford vs Notre Dame | +26.0L23–56 | 50.5 | L23–56 | O | N |
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?
Stanford Edge
Stanford +0.60
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 5 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UCLA Edge
UCLA +20.8
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 6 games this season
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
UCLA
Chip Kelly #1
30–29 (51%)
· Yr 6 at school
OC
Chip Kelly
Yr 2
#1
DC
D'Anton Lynn
Yr 1
#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
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 ✓