Matchup Prediction
Toss-up — no clear edge
Neither metric shows a meaningful pre-game edge in this matchup.
Momentum Control
58.4%
—
Lean
Game Control
50.6%
Stanford wins
Toss-up
Vegas Spread
USC -9.5
O/U 66.5
teamrankings
Advanced Stats
All 4 factors agree → USC
· 83.1% ATS historically when all four align
↓ 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 |
Stanford 2022 Schedule
Stanford's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | Stanford vs Colgate | -40.0W41–10 | 51.5 | W41–10 | U | N |
| Sat 9/10 | Stanford vs USC | +9.5L28–41 | 66.5 | L28–41 | O | N |
| — Bye Week — | ||||||
| Sat 9/24 | Stanford at Washington | +14.0L22–40 | 62.5 | L22–40 | U | N |
| Sat 10/1 | Stanford at Oregon | +17.0L27–45 | 63.0 | L27–45 | O | N |
| Sat 10/8 | Stanford vs Oregon State | +4.5L27–28 | 53.0 | L27–28 | O | Y |
| Sat 10/15 | Stanford at Notre Dame | +16.5W16–14 | 53.5 | W16–14 | U | Y |
| Sat 10/22 | Stanford vs Arizona State | -3.0W15–14 | 52.0 | W15–14 | U | N |
| Sat 10/29 | Stanford at UCLA | +16.5L13–38 | 64.5 | L13–38 | U | N |
| Sat 11/5 | Stanford vs Washington State | +3.0L14–52 | 48.5 | L14–52 | O | N |
| Sat 11/12 | Stanford at Utah | +23.5L7–42 | 54.0 | L7–42 | U | N |
| Sat 11/19 | Stanford at California | +5.0L20–27 | 46.0 | L20–27 | O | N |
| Sat 11/26 | Stanford vs BYU | +6.0L26–35 | 57.5 | L26–35 | 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
All 4 Agree
→ USC
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ USC
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ USC
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 +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 0 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Stanford Edge
Stanford +1.8
GC Edge (season-to-date)
Teams with this edge win 50.6% of games historically
Based on 1 game this season
Actual Result
CSS Battle
USC
1 — 2 sequences
✗ Predicted incorrectly
GC Battle
USC
5.1 — 90.0 GC score
✗ Predicted incorrectly
Game Result
USC won by 13
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Stanford, but the GC edge is small. When metrics agree but GC is near-neutral, the agreed-upon team has covered only 46.7% of the time historically (n=224) — potentially a fade signal.
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
Stanford
David Shaw #1
93–45 (67%)
· Yr 12 at school
OC
Tavita Pritchard
Yr 2
#1
DC
Lance Anderson
Yr 2
#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 ✓