Sat, Nov 28 2026
·
Week 13
·
🏟 California Memorial Stadium
Berkeley, CA
·
Turf
·
62,717 cap
Pittsburgh✈ 2,248 mi-3 hr TZ
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
Pittsburgh
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Pittsburgh entering this game.
Momentum Control
61.3%
Pittsburgh wins
Lean
Game Control
64.9%
Pittsburgh wins
Lean
Advanced Stats
PPA + Success Rate agree → Pittsburgh
· 73.9% ATS historically
↓ See full breakdown
Pittsburgh 2026 Schedule
Pittsburgh's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Pittsburgh vs Miami (OH) | -12.5 | — | — | — | — |
| Sat 9/12 | Pittsburgh vs UCF | -10 | — | — | — | — |
| Thu 9/17 | Pittsburgh vs Syracuse | -19 | — | — | — | — |
| Sat 9/26 | Pittsburgh vs Bucknell | -30 | — | — | — | — |
| Fri 10/2 | Pittsburgh at Virginia Tech | -8.5 | — | — | — | — |
| Sat 10/10 | Pittsburgh vs North Carolina | -14.5 | — | — | — | — |
| Sat 10/17 | Pittsburgh at Boston College | -13.5 | — | — | — | — |
| Sat 10/24 | Pittsburgh at Miami | +16 | — | — | — | — |
| Sat 10/31 | Pittsburgh vs Georgia Tech | -6 | — | — | — | — |
| — Bye Week — | ||||||
| Fri 11/13 | Pittsburgh vs Florida State | -4 | — | — | — | — |
| Sat 11/21 | Pittsburgh at Louisville | +5.5 | — | — | — | — |
| Sat 11/28 | Pittsburgh at California | -2 | — | — | — | — |
California 2026 Schedule
California's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | California vs UCLA | -3.5 | 53.5 | — | — | — |
| Sat 9/12 | California at Syracuse | -9.5 | — | — | — | — |
| Sat 9/19 | California vs Wagner | -28 | — | — | — | — |
| Fri 9/25 | California vs Clemson | +2 | — | — | — | — |
| Sat 10/3 | California at UNLV | +1.5 | — | — | — | — |
| Sat 10/10 | California vs Virginia Tech | -9 | — | — | — | — |
| Sat 10/17 | California vs Wake Forest | -1.5 | — | — | — | — |
| Fri 10/23 | California at SMU | +13 | — | — | — | — |
| Fri 10/30 | California at NC State | +4 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 11/14 | California at Virginia | +5 | — | — | — | — |
| Sat 11/21 | California vs Stanford | -15 | — | — | — | — |
| Sat 11/28 | California vs Pittsburgh | +2 | — | — | — | — |
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
Split
Metrics disagree
Elite · 82.4% ATS
PPA + PPO + Havoc
Split
Metrics disagree
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Pittsburgh
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?
Pittsburgh Edge
Pittsburgh +0.83
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Pittsburgh Edge
Pittsburgh +13.5
GC Edge (season-to-date)
Teams with this edge win 64.9% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
Pittsburgh
Pat Narduzzi #40
80–61 (57%)
· Yr 12 at school
OC
Kade Bell
Yr 3
#20
DC
Randy Bates
Yr 3
#100
California
Tosh Lupoi #77
0–0 (0%)
· Yr 1 at school
OC
Jordan Somerville
Yr 1
#67
DC
Michael Hutchings
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 ✓