Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Stanford,
while Game Control favors USC.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
61.3%
Stanford wins
Lean
Game Control
58.6%
USC wins
Lean
Vegas Spread
USC -28.5
O/U 70.5
William Hill (New Jersey)
Advanced Stats
All 4 factors agree → USC
· 83.1% ATS historically when all four align
↓ See full breakdown
Stanford 2023 Schedule
Stanford's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Fri 9/1 | Stanford at Hawai'i | -2.0W37–24 | 54.0 | W37–24 | O | Y |
| Sat 9/9 | Stanford at USC | +28.5L10–56 | 70.5 | L10–56 | U | N |
| Sat 9/16 | Stanford vs Sacramento State | -7.0L23–30 | 60.5 | L23–30 | U | N |
| Sat 9/23 | Stanford vs Arizona | +13.0L20–21 | 60.0 | L20–21 | U | Y |
| Sat 9/30 | Stanford vs Oregon | +27.0L6–42 | 59.5 | L6–42 | U | N |
| — Bye Week — | ||||||
| Fri 10/13 | Stanford at Colorado | +13.0W46–43 | 59.0 | W46–43 | O | Y |
| Sat 10/21 | Stanford vs UCLA | +17.0L7–42 | 52.0 | L7–42 | U | N |
| Sat 10/28 | Stanford vs Washington | +27.5L33–42 | 62.0 | L33–42 | O | Y |
| Sat 11/4 | Stanford at Washington State | +13.0W10–7 | 59.5 | W10–7 | U | Y |
| Sat 11/11 | Stanford at Oregon State | +21.5L17–62 | 51.5 | L17–62 | O | N |
| Sat 11/18 | Stanford vs California | +6.5L15–27 | 52.5 | L15–27 | U | N |
| Sat 11/25 | Stanford vs Notre Dame | +26.0L23–56 | 50.5 | L23–56 | O | N |
USC 2023 Schedule
USC's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/26 | USC vs San José State | -31.5W56–28 | 66.0 | W56–28 | O | N |
| Sat 9/2 | USC vs Nevada | -37.5W66–14 | 63.5 | W66–14 | O | Y |
| Sat 9/9 | USC vs Stanford | -28.5W56–10 | 70.5 | W56–10 | U | Y |
| — Bye Week — | ||||||
| Sat 9/23 | USC at Arizona State | -34.5W42–28 | 62.0 | W42–28 | O | N |
| Sat 9/30 | USC at Colorado | -22.0W48–41 | 74.5 | W48–41 | O | N |
| Sat 10/7 | USC vs Arizona | -21.0W43–41 | 69.5 | W43–41 | O | N |
| Sat 10/14 | USC at Notre Dame | +3.0L20–48 | 61.0 | L20–48 | O | N |
| Sat 10/21 | USC vs Utah | -7.5L32–34 | 51.5 | L32–34 | O | N |
| Sat 10/28 | USC at California | -10.5W50–49 | 67.5 | W50–49 | O | N |
| Sat 11/4 | USC vs Washington | +3.0L42–52 | 76.0 | L42–52 | O | N |
| Sat 11/11 | USC at Oregon | +12.5L27–36 | 78.5 | L27–36 | U | Y |
| Sat 11/18 | USC vs UCLA | -6.0L20–38 | 65.5 | L20–38 | U | N |
| Wed 12/27 | USC vs Louisville | +4.5W42–28 | 58.0 | W42–28 | O | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2023 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 · 2023 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?
Stanford Edge
Stanford +0.50
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 2 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
USC Edge
USC +10.9
GC Edge (season-to-date)
Teams with this edge win 58.6% of games historically
Based on 2 games this season
Actual Result
CSS Battle
USC
2 — 0 sequences
✗ Predicted incorrectly
GC Battle
USC
93.9 — 2.7 GC score
✓ Predicted correctly
Game Result
USC won by 46
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
Stanford
Troy Taylor #1
1–2 (33%)
· Yr 1 at school
OC
Troy Taylor
Yr 1
#1
DC
Bobby April III
Yr 1
#1
USC
Lincoln Riley #1
14–3 (82%)
· Yr 2 at school
OC
Josh Henson
Yr 2
#1
DC
Alex Grinch
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 ✓