Matchup Prediction
Iowa
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Iowa entering this game.
Momentum Control
73.7%
Iowa wins
Solid
Game Control
75.9%
Iowa wins
Solid
Vegas Spread
Iowa -6.5
O/U 44.5
ESPN Bet
Advanced Stats
PPA + Success Rate agree → Iowa
· 73.9% ATS historically
↓ See full breakdown
Iowa 2024 Schedule
Iowa's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | Iowa vs Illinois State | -29.5 | — | — | — | — |
| Sat 9/7 | Iowa vs Iowa State | -3.0L19–20 | 35.0 | L19–20 | O | N |
| Sat 9/14 | Iowa vs Troy | -23.5W38–21 | 39.5 | W38–21 | O | N |
| Sat 9/21 | Iowa at Minnesota | -3.0W31–14 | 34.5 | W31–14 | O | Y |
| — Bye Week — | ||||||
| Sat 10/5 | Iowa at Ohio State | +17.0L7–35 | 46.0 | L7–35 | U | N |
| Sat 10/12 | Iowa vs Washington | -2.5W40–16 | 41.5 | W40–16 | O | Y |
| Sat 10/19 | Iowa at Michigan State | -7.0L20–32 | 39.5 | L20–32 | O | N |
| Sat 10/26 | Iowa vs Northwestern | -16.5W40–14 | 38.5 | W40–14 | O | Y |
| Sat 11/2 | Iowa vs Wisconsin | -2.5W42–10 | 40.0 | W42–10 | O | Y |
| Fri 11/8 | Iowa at UCLA | -6.5L17–20 | 44.5 | L17–20 | U | N |
| — Bye Week — | ||||||
| Sat 11/23 | Iowa at Maryland | -4.0W29–13 | 40.5 | W29–13 | O | Y |
| Fri 11/29 | Iowa vs Nebraska | -3.5W13–10 | 41.5 | W13–10 | U | N |
| Mon 12/30 | Iowa vs Missouri | +1.0L24–27 | 41.0 | L24–27 | O | N |
UCLA 2024 Schedule
UCLA's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | UCLA at Hawai'i | -13.5W16–13 | 55.5 | W16–13 | U | N |
| — Bye Week — | ||||||
| Sat 9/14 | UCLA vs Indiana | +3.5L13–42 | 46.5 | L13–42 | O | N |
| Sat 9/21 | UCLA at LSU | +21.5L17–34 | 56.5 | L17–34 | U | Y |
| Sat 9/28 | UCLA vs Oregon | +23.5L13–34 | 54.5 | L13–34 | U | Y |
| Sat 10/5 | UCLA at Penn State | +30.0L11–27 | 48.0 | L11–27 | U | Y |
| Sat 10/12 | UCLA vs Minnesota | +3.5L17–21 | 39.0 | L17–21 | U | N |
| Sat 10/19 | UCLA at Rutgers | +4.0W35–32 | 42.5 | W35–32 | O | Y |
| — Bye Week — | ||||||
| Sat 11/2 | UCLA at Nebraska | +7.5W27–20 | 38.5 | W27–20 | O | Y |
| Fri 11/8 | UCLA vs Iowa | +6.5W20–17 | 44.5 | W20–17 | U | Y |
| Fri 11/15 | UCLA at Washington | +4.5L19–31 | 47.0 | L19–31 | O | N |
| Sat 11/23 | UCLA vs USC | +5.0L13–19 | 53.0 | L13–19 | U | N |
| Sat 11/30 | UCLA vs Fresno State | -7.5W20–13 | 46.5 | W20–13 | U | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2024 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
→ Iowa
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 · 2024 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?
Iowa Edge
Iowa +1.25
CSS Edge (season-to-date)
Teams with this edge win 73.7% of games historically
Based on 8 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Iowa Edge
Iowa +20.6
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 8 games this season
Actual Result
CSS Battle
Tie
1 — 1 sequences
✗ Predicted incorrectly
GC Battle
UCLA
45.7 — 32.0 GC score
✗ Predicted incorrectly
Game Result
UCLA won by 3
✗ Model missed it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Iowa 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
Iowa
Kirk Ferentz #1
196–119 (62%)
· Yr 26 at school
OC
Tim Lester
Yr 1
#1
DC
Phil Parker
Yr 3
#1
UCLA
DeShaun Foster #1
0–0 (0%)
· Yr 1 at school
OC
Eric Bieniemy
Yr 1
#1
DC
Ikaika Malloe
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 ✓