Sat, Sep 17 2022
·
Week 3
·
🏟 Rose Bowl
Pasadena, CA
·
Turf
·
92,542 cap
South Alabama✈ 1,766 mi-2 hr TZ
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
49.4%
South Alabama wins
Toss-up
Vegas Spread
UCLA -15.5
O/U 59.5
teamrankings
Advanced Stats
PPA + Success Rate agree → UCLA
· 73.9% ATS historically
↓ See full breakdown
South Alabama 2022 Schedule
South Alabama's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | South Alabama vs Nicholls | -13.5W48–7 | 53.0 | W48–7 | O | Y |
| Sat 9/10 | South Alabama at Central Michigan | +6.0W38–24 | 57.5 | W38–24 | O | Y |
| Sat 9/17 | South Alabama at UCLA | +15.5L31–32 | 59.5 | L31–32 | O | Y |
| Sat 9/24 | South Alabama vs Louisiana Tech | -13.0W38–14 | 59.0 | W38–14 | U | Y |
| Sat 10/1 | South Alabama at Louisiana | -8.5W20–17 | 47.0 | W20–17 | U | N |
| — Bye Week — | ||||||
| Sat 10/15 | South Alabama vs UL Monroe | -17.0W41–34 | 51.0 | W41–34 | O | N |
| Thu 10/20 | South Alabama vs Troy | -3.0L6–10 | 47.0 | L6–10 | U | N |
| Sat 10/29 | South Alabama at Arkansas State | -9.0W31–3 | 52.5 | W31–3 | U | Y |
| Sat 11/5 | South Alabama at Georgia Southern | -3.5W38–31 | 60.5 | W38–31 | O | Y |
| Sat 11/12 | South Alabama vs Texas State | -16.0W38–21 | 46.0 | W38–21 | O | Y |
| Sat 11/19 | South Alabama at Southern Miss | -7.5W27–20 | 45.0 | W27–20 | O | N |
| Sat 11/26 | South Alabama vs Old Dominion | -16.5W27–20 | 47.0 | W27–20 | U | N |
| Wed 12/21 | South Alabama vs Western Kentucky | -4.0L23–44 | 58.0 | L23–44 | 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?
South Alabama +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
South Alabama Edge
South Alabama +2.4
GC Edge (season-to-date)
Teams with this edge win 49.4% of games historically
Based on 2 games this season
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
South Alabama
Kane Wommack #1
5–7 (42%)
· Yr 2 at school
OC
Major Applewhite
Yr 2
#1
DC
Corey Batoon
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: CSS is not a predictive 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: GS is not a predictive 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: CSS is not a predictive 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: GS is not a predictive 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 ✓