Matchup Prediction
USC
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
USC entering this game.
Momentum Control
61.3%
USC wins
Lean
Game Control
64.9%
USC wins
Lean
Vegas Spread
USC -2.5
O/U 76.5
teamrankings
Advanced Stats
PPA + Success Rate agree → UCLA
· 73.9% ATS historically
↓ See full breakdown
USC 2022 Schedule
USC's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | USC vs Rice | -33.0W66–14 | 61.5 | W66–14 | O | Y |
| Sat 9/10 | USC at Stanford | -9.5W41–28 | 66.5 | W41–28 | O | Y |
| Sat 9/17 | USC vs Fresno State | -11.0W45–17 | 71.0 | W45–17 | U | Y |
| Sat 9/24 | USC at Oregon State | -5.5W17–14 | 70.5 | W17–14 | U | N |
| Sat 10/1 | USC vs Arizona State | -24.5W42–25 | 61.0 | W42–25 | O | N |
| Sat 10/8 | USC vs Washington State | -12.5W30–14 | 64.5 | W30–14 | U | Y |
| Sat 10/15 | USC at Utah | +3.5L42–43 | 65.0 | L42–43 | O | Y |
| — Bye Week — | ||||||
| Sat 10/29 | USC at Arizona | -14.0W45–37 | 74.0 | W45–37 | O | N |
| Sat 11/5 | USC vs California | -21.5W41–35 | 60.5 | W41–35 | O | N |
| Fri 11/11 | USC vs Colorado | -34.0W55–17 | 66.0 | W55–17 | O | Y |
| Sat 11/19 | USC at UCLA | -2.5W48–45 | 76.5 | W48–45 | O | Y |
| Sat 11/26 | USC vs Notre Dame | -4.0W38–27 | 63.5 | W38–27 | O | Y |
| Fri 12/2 | USC vs Utah | -3.0L24–47 | 67.5 | L24–47 | O | N |
| Mon 1/2 | USC vs Tulane | -1.5L45–46 | 67.0 | L45–46 | O | N |
UCLA 2022 Schedule
UCLA's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | UCLA vs Bowling Green | -24.0W45–17 | 56.5 | W45–17 | O | Y |
| Sat 9/10 | UCLA vs Alabama State | -48.5W45–7 | 61.5 | W45–7 | U | N |
| Sat 9/17 | UCLA vs South Alabama | -15.5W32–31 | 59.5 | W32–31 | O | N |
| Sat 9/24 | UCLA at Colorado | -22.0W45–17 | 57.0 | W45–17 | O | Y |
| Fri 9/30 | UCLA vs Washington | +2.5W40–32 | 65.0 | W40–32 | O | Y |
| Sat 10/8 | UCLA vs Utah | +3.0W42–32 | 64.5 | W42–32 | O | Y |
| — Bye Week — | ||||||
| Sat 10/22 | UCLA at Oregon | +7.0L30–45 | 70.5 | L30–45 | O | N |
| Sat 10/29 | UCLA vs Stanford | -16.5W38–13 | 64.5 | W38–13 | U | Y |
| Sat 11/5 | UCLA at Arizona State | -11.0W50–36 | 66.5 | W50–36 | O | Y |
| Sat 11/12 | UCLA vs Arizona | -19.5L28–34 | 76.5 | L28–34 | U | N |
| Sat 11/19 | UCLA vs USC | +2.5L45–48 | 76.5 | L45–48 | O | N |
| Fri 11/25 | UCLA at California | -11.5W35–28 | 62.5 | W35–28 | O | N |
| Fri 12/30 | UCLA vs Pittsburgh | -9.0L35–37 | 55.0 | L35–37 | O | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2022 season
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
Split
Metrics disagree
Elite · 82.4% ATS
PPA + PPO + Havoc
Split
Metrics disagree
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 · 2022 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 +0.14
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 9 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
USC Edge
USC +14.2
GC Edge (season-to-date)
Teams with this edge win 64.9% of games historically
Based on 10 games this season
Actual Result
CSS Battle
USC
1 — 2 sequences
✓ Predicted correctly
GC Battle
USC
33.9 — 41.8 GC score
✓ Predicted correctly
Game Result
USC won by 3
✓ Model called it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on USC with a solid GC edge. Teams with this profile have covered 53.0% of the time historically (n=330) — a mild lean.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
USC
Lincoln Riley #1
0–0 (0%)
· Yr 1 at school
OC
Josh Henson
Yr 1
#1
DC
Alex Grinch
Yr 1
#1
UCLA
Chip Kelly #1
18–25 (42%)
· Yr 5 at school
OC
Chip Kelly
Yr 1
#1
DC
Bill McGovern
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 ✓