Sat, Sep 12 2026
·
Week 2
·
🏟 Rose Bowl
Pasadena, CA
·
Turf
·
92,542 cap
San Diego State✈ 112 miSame TZ
Preseason projection — This game has not yet been played and 2026 in-season data is not yet available.
Edges are based on 2025 full-season performance.
Confidence will increase once in-season games are logged.
Matchup Prediction
San Diego State
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
San Diego State entering this game.
Momentum Control
61.3%
San Diego State wins
Lean
Game Control
75.9%
San Diego State wins
Solid
Advanced Stats
All 4 factors agree → San Diego State
· 83.1% ATS historically when all four align
↓ See full breakdown
San Diego State 2026 Schedule
San Diego State's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | San Diego State vs Portland State | -28 | — | — | — | — |
| Sat 9/12 | San Diego State at UCLA | +3.5 | — | — | — | — |
| Sat 9/19 | San Diego State vs James Madison | -2 | — | — | — | — |
| Sat 9/26 | San Diego State at Toledo | +1.5 | — | — | — | — |
| Sat 10/3 | San Diego State vs Texas State | -4.5 | — | — | — | — |
| Sat 10/10 | San Diego State at Oregon State | -14 | — | — | — | — |
| Sat 10/17 | San Diego State vs Fresno State | -5.5 | — | — | — | — |
| Sat 10/24 | San Diego State at Colorado State | -15 | — | — | — | — |
| Sat 10/31 | San Diego State vs Washington State | -8.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 11/14 | San Diego State vs Utah State | -13.5 | — | — | — | — |
| Sat 11/21 | San Diego State at Boise State | +0.5 | — | — | — | — |
UCLA 2026 Schedule
UCLA's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | UCLA at California | +3.5 | 53.5 | — | — | — |
| Sat 9/12 | UCLA vs San Diego State | -3.5 | — | — | — | — |
| Sat 9/19 | UCLA vs Purdue | -13.5 | — | — | — | — |
| Sat 9/26 | UCLA at Maryland | -1 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/10 | UCLA at Oregon | +21.5 | — | — | — | — |
| Sat 10/17 | UCLA vs Wisconsin | -10.5 | — | — | — | — |
| Sat 10/24 | UCLA vs Michigan State | -11 | — | — | — | — |
| Sat 10/31 | UCLA vs Nevada | -26 | — | — | — | — |
| Sat 11/7 | UCLA at Minnesota | +0 | — | — | — | — |
| Sat 11/14 | UCLA vs Illinois | +2 | — | — | — | — |
| Sat 11/21 | UCLA at Michigan | +11.5 | — | — | — | — |
| Sat 11/28 | UCLA vs USC | +8.5 | — | — | — | — |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2025 season (prior year)
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
All 4 Agree
→ San Diego State
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ San Diego State
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ San Diego State
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 · 2025 season (prior year — 2026 data not yet available) ·
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?
San Diego State Edge
San Diego State +0.25
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
San Diego State Edge
San Diego State +33.5
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
San Diego State
Sean Lewis #67
12–13 (48%)
· Yr 3 at school
OC
Sean Lewis
Yr 3
#121
DC
Demetrius Sumler
Yr 1
#68
UCLA
Bob Chesney #20
0–0 (0%)
· Yr 1 at school
OC
Dean Kennedy
Yr 1
#21
DC
Colin Hitschler
Yr 1
#36
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 ✓