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%
—
Toss-up
Vegas Spread
Utah -6.5
O/U 50.5
DraftKings
Advanced Stats
All 4 factors agree → Utah
· 83.1% ATS historically when all four align
↓ See full breakdown
Utah 2025 Schedule
Utah's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | Utah at UCLA | -6.5W43–10 | 50.5 | W43–10 | O | Y |
| Sat 9/6 | Utah vs Cal Poly | -42.5W63–9 | 53.5 | W63–9 | O | Y |
| Sat 9/13 | Utah at Wyoming | -24.5W31–6 | 47.5 | W31–6 | U | Y |
| Sat 9/20 | Utah vs Texas Tech | -3.5L10–34 | 58.5 | L10–34 | U | N |
| Sat 9/27 | Utah at West Virginia | -13.5W48–14 | 46.5 | W48–14 | O | Y |
| — Bye Week — | ||||||
| Sat 10/11 | Utah vs Arizona State | -9.5W42–10 | 44.5 | W42–10 | O | Y |
| Sat 10/18 | Utah at BYU | -4.0L21–24 | 49.5 | L21–24 | U | N |
| Sat 10/25 | Utah vs Colorado | -14.5W53–7 | 50.5 | W53–7 | O | Y |
| Sat 11/1 | Utah vs Cincinnati | -11.5W45–14 | 57.5 | W45–14 | O | Y |
| — Bye Week — | ||||||
| Sat 11/15 | Utah at Baylor | -9.5W55–28 | 60.5 | W55–28 | O | Y |
| Sat 11/22 | Utah vs Kansas State | -18.5W51–47 | 52.5 | W51–47 | O | N |
| Fri 11/28 | Utah at Kansas | -10.5W31–21 | 59.5 | W31–21 | U | N |
| Wed 12/31 | Utah vs Nebraska | -13.5W44–22 | 51.5 | W44–22 | O | Y |
UCLA 2025 Schedule
UCLA's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | UCLA vs Utah | +6.5L10–43 | 50.5 | L10–43 | O | N |
| Sat 9/6 | UCLA at UNLV | -2.5L23–30 | 54.5 | L23–30 | U | N |
| Fri 9/12 | UCLA vs New Mexico | -15.5L10–35 | 52.5 | L10–35 | U | N |
| — Bye Week — | ||||||
| Sat 9/27 | UCLA at Northwestern | +6.0L14–17 | 45.5 | L14–17 | U | Y |
| Sat 10/4 | UCLA vs Penn State | +24.5W42–37 | 48.5 | W42–37 | O | Y |
| Sat 10/11 | UCLA at Michigan State | +7.0W38–13 | 51.5 | W38–13 | U | Y |
| Sat 10/18 | UCLA vs Maryland | -3.5W20–17 | 52.5 | W20–17 | U | N |
| Sat 10/25 | UCLA at Indiana | +26.5L6–56 | 53.5 | L6–56 | O | N |
| — Bye Week — | ||||||
| Sat 11/8 | UCLA vs Nebraska | -1.5L21–28 | 45.5 | L21–28 | O | N |
| Sat 11/15 | UCLA at Ohio State | +33.5L10–48 | 46.5 | L10–48 | O | N |
| Sat 11/22 | UCLA vs Washington | +10.5L14–48 | 51.5 | L14–48 | O | N |
| Sat 11/29 | UCLA at USC | +21.0L10–29 | 59.0 | L10–29 | U | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2025 season
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
All 4 Agree
→ Utah
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Utah
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Utah
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 · 2025 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?
Utah Edge
Utah +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?
Utah Edge
Utah +0.0
GC Edge (season-to-date)
Teams with this edge win 50.6% of games historically
Based on 0 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UCLA, 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
Utah
Kyle Whittingham #1
167–86 (66%)
· Yr 21 at school
OC
Jason Beck
Yr 1
#1
DC
Morgan Scalley
Yr 3
#1
UCLA
DeShaun Foster #1
5–7 (42%)
· Yr 2 at school
OC
Tino Sunseri
Yr 1
#1
DC
Ikaika Malloe
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 ✓