Preseason projection — This game has not yet been played and 2026 in-season data is not yet available.
Edges are based on 2025 full-season performance.
Confidence will increase once in-season games are logged.
Matchup Prediction
Maryland
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Maryland entering this game.
Momentum Control
58.4%
Maryland wins
Lean
Game Control
76%
Maryland wins
Strong
Advanced Stats
All 4 factors agree → Maryland
· 83.1% ATS historically when all four align
↓ See full breakdown
UCLA 2026 Schedule
UCLA's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | UCLA at California | +3.5 | 53.5 | — | — | — |
| Sat 9/12 | UCLA vs San Diego State | -3.5 | — | — | — | — |
| Sat 9/19 | UCLA vs Purdue | -13.5 | — | — | — | — |
| Sat 9/26 | UCLA at Maryland | -1 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/10 | UCLA at Oregon | +21.5 | — | — | — | — |
| Sat 10/17 | UCLA vs Wisconsin | -10.5 | — | — | — | — |
| Sat 10/24 | UCLA vs Michigan State | -11 | — | — | — | — |
| Sat 10/31 | UCLA vs Nevada | -26 | — | — | — | — |
| Sat 11/7 | UCLA at Minnesota | +0 | — | — | — | — |
| Sat 11/14 | UCLA vs Illinois | +2 | — | — | — | — |
| Sat 11/21 | UCLA at Michigan | +11.5 | — | — | — | — |
| Sat 11/28 | UCLA vs USC | +8.5 | — | — | — | — |
Maryland 2026 Schedule
Maryland's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Maryland vs Howard | -26 | — | — | — | — |
| Sat 9/12 | Maryland at UConn | +1 | — | — | — | — |
| Sat 9/19 | Maryland vs Virginia Tech | -7 | — | — | — | — |
| Sat 9/26 | Maryland vs UCLA | +1 | — | — | — | — |
| Sat 10/3 | Maryland at Nebraska | +5.5 | — | — | — | — |
| Sat 10/10 | Maryland at Ohio State | +28 | — | — | — | — |
| Sat 10/17 | Maryland vs Rutgers | -5.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/31 | Maryland vs Illinois | +5.5 | — | — | — | — |
| Sat 11/7 | Maryland at Purdue | -5 | — | — | — | — |
| Sat 11/14 | Maryland vs Wisconsin | -7 | — | — | — | — |
| Sat 11/21 | Maryland at USC | +17 | — | — | — | — |
| Sat 11/28 | Maryland vs Penn State | +8.5 | — | — | — | — |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2025 season (prior year)
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
All 4 Agree
→ Maryland
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Maryland
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Maryland
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 (prior year — 2026 data not yet available) ·
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?
Maryland Edge
Maryland +0.15
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Maryland Edge
Maryland +22.0
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
UCLA
Bob Chesney #20
0–0 (0%)
· Yr 1 at school
OC
Dean Kennedy
Yr 1
#21
DC
Colin Hitschler
Yr 1
#36
Maryland
Mike Locksley #109
37–49 (43%)
· Yr 8 at school
OC
Clint Trickett
Yr 1
#127
DC
Ted Monachino
Yr 1
#68
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 ✓