Matchup Prediction
UCLA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UCLA entering this game.
Momentum Control
58.4%
UCLA wins
Lean
Game Control
76%
UCLA wins
Strong
Vegas Spread
UCLA -16.5
O/U 64.5
teamrankings
Advanced Stats
PPA + Success Rate agree → UCLA
· 73.9% ATS historically
↓ See full breakdown
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 |
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?
UCLA Edge
UCLA +0.67
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 6 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UCLA Edge
UCLA +32.3
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 7 games this season
Actual Result
CSS Battle
UCLA
4 — 0 sequences
✓ Predicted correctly
GC Battle
UCLA
89.8 — 3.8 GC score
✓ Predicted correctly
Game Result
UCLA won by 25
✓ Model called it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UCLA with a large edge. Historically, dominant teams like this are fully priced into the spread — the agreed-upon team covers just 50.2% of the time. The metrics predict game control better than they beat the number.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Stanford
David Shaw #1
93–45 (67%)
· Yr 12 at school
OC
Tavita Pritchard
Yr 2
#1
DC
Lance Anderson
Yr 2
#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 ✓