Matchup Prediction
UCLA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UCLA entering this game.
Momentum Control
61.3%
UCLA wins
Lean
Game Control
58.3%
UCLA wins
Lean
Vegas Spread
UCLA -4
O/U 60.5
teamrankings
Advanced Stats
All 4 factors agree → UCLA
· 83.1% ATS historically when all four align
↓ See full breakdown
UCLA 2021 Schedule
UCLA's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/28 | UCLA vs Hawai'i | -17.5W44–10 | 67.0 | W44–10 | U | Y |
| Sat 9/4 | UCLA vs LSU | +2.0W38–27 | 64.0 | W38–27 | O | Y |
| — Bye Week — | ||||||
| Sat 9/18 | UCLA vs Fresno State | -11.0L37–40 | 64.0 | L37–40 | O | N |
| Sat 9/25 | UCLA at Stanford | -4.0W35–24 | 60.5 | W35–24 | U | Y |
| Sat 10/2 | UCLA vs Arizona State | -3.0L23–42 | 56.5 | L23–42 | O | N |
| Sat 10/9 | UCLA at Arizona | -16.0W34–16 | 60.0 | W34–16 | U | Y |
| Sat 10/16 | UCLA at Washington | +1.5W24–17 | 55.5 | W24–17 | U | Y |
| Sat 10/23 | UCLA vs Oregon | -1.0L31–34 | 62.5 | L31–34 | O | N |
| Sat 10/30 | UCLA at Utah | +6.0L24–44 | 60.5 | L24–44 | O | N |
| — Bye Week — | ||||||
| Sat 11/13 | UCLA vs Colorado | -18.0W44–20 | 57.5 | W44–20 | O | Y |
| Sat 11/20 | UCLA at USC | -4.5W62–33 | 66.5 | W62–33 | O | Y |
| Sat 11/27 | UCLA vs California | -6.5W42–14 | 58.5 | W42–14 | U | Y |
| Tue 12/28 | UCLA vs NC State | +2.0 | 60.0 | — | — | — |
Stanford 2021 Schedule
Stanford's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | Stanford vs Kansas State | +3.0L7–24 | 54.5 | L7–24 | U | N |
| Sat 9/11 | Stanford at USC | +17.0W42–28 | 53.0 | W42–28 | O | Y |
| Sat 9/18 | Stanford at Vanderbilt | -13.0W41–23 | 49.0 | W41–23 | O | Y |
| Sat 9/25 | Stanford vs UCLA | +4.0L24–35 | 60.5 | L24–35 | U | N |
| Sat 10/2 | Stanford vs Oregon | +8.5W31–24 | 57.5 | W31–24 | U | Y |
| Fri 10/8 | Stanford at Arizona State | +13.5L10–28 | 53.5 | L10–28 | U | N |
| Sat 10/16 | Stanford at Washington State | +1.0L31–34 | 53.0 | L31–34 | O | N |
| — Bye Week — | ||||||
| Sat 10/30 | Stanford vs Washington | -2.5L13–20 | 45.5 | L13–20 | U | N |
| Fri 11/5 | Stanford vs Utah | +10.0L7–52 | 52.0 | L7–52 | O | N |
| Sat 11/13 | Stanford at Oregon State | +12.5L14–35 | 56.5 | L14–35 | U | N |
| Sat 11/20 | Stanford vs California | +2.5L11–41 | 46.0 | L11–41 | O | N |
| Sat 11/27 | Stanford vs Notre Dame | +20.5L14–45 | 53.0 | L14–45 | 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
→ 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 · 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?
UCLA Edge
UCLA +0.67
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 3 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UCLA Edge
UCLA +8.1
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 3 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
UCLA
Chip Kelly #1
12–22 (35%)
· Yr 4 at school
OC
Justin Frye
Yr 1
#1
DC
Jerry Azzinaro
Yr 1
#1
Stanford
David Shaw #1
92–37 (71%)
· Yr 11 at school
OC
Tavita Pritchard
Yr 1
#1
DC
Lance Anderson
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 ✓