Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors USC,
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%
USC wins
Lean
Game Control
58.3%
UCLA wins
Lean
Vegas Spread
UCLA -4.5
O/U 66.5
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 | — | — | — |
USC 2021 Schedule
USC's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | USC vs San José State | -13.5W30–7 | 61.0 | W30–7 | U | Y |
| Sat 9/11 | USC vs Stanford | -17.0L28–42 | 53.0 | L28–42 | O | N |
| Sat 9/18 | USC at Washington State | -7.0W45–14 | 61.0 | W45–14 | U | Y |
| Sat 9/25 | USC vs Oregon State | -11.0L27–45 | 62.5 | L27–45 | O | N |
| Sat 10/2 | USC at Colorado | -9.0W37–14 | 50.5 | W37–14 | O | Y |
| Sat 10/9 | USC vs Utah | -3.0L26–42 | 52.5 | L26–42 | O | N |
| — Bye Week — | ||||||
| Sat 10/23 | USC at Notre Dame | +8.0L16–31 | 59.5 | L16–31 | U | N |
| Sat 10/30 | USC vs Arizona | -22.0W41–34 | 55.5 | W41–34 | O | N |
| Sat 11/6 | USC at Arizona State | +10.0L16–31 | 61.0 | L16–31 | U | N |
| Sat 11/13 | USC at California | -2.0 | 52.5 | — | — | — |
| Sat 11/20 | USC vs UCLA | +4.5L33–62 | 66.5 | L33–62 | O | N |
| Sat 11/27 | USC vs BYU | +8.5L31–35 | 65.5 | L31–35 | O | Y |
| Sat 12/4 | USC at California | +4.5L14–24 | 57.5 | L14–24 | U | 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?
USC Edge
USC +0.59
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?
UCLA Edge
UCLA +6.2
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 9 games this season
Actual Result
CSS Battle
UCLA
1 — 3 sequences
✗ Predicted incorrectly
GC Battle
UCLA
12.9 — 69.7 GC score
✓ Predicted correctly
Game Result
UCLA won by 29
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
12–22 (35%)
· Yr 4 at school
OC
Justin Frye
Yr 1
#1
DC
Jerry Azzinaro
Yr 1
#1
USC
Donte Williams #1
1–0 (100%)
· Yr 1 at school
OC
Graham Harrell
Yr 1
#1
DC
Todd Orlando
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 ✓