Sat, Nov 1 2025
·
Week 10
·
🏟 Stanford Stadium
Stanford, CA
·
Turf
·
50,424 cap
Pittsburgh✈ 2,253 mi-3 hr TZ
Matchup Prediction
Pittsburgh
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Pittsburgh entering this game.
Momentum Control
80.6%
Pittsburgh wins
Strong
Game Control
75.9%
Pittsburgh wins
Solid
Vegas Spread
Pittsburgh -13.5
O/U 51.5
DraftKings
Advanced Stats
All 4 factors agree → Pittsburgh
· 83.1% ATS historically when all four align
↓ See full breakdown
Pittsburgh 2025 Schedule
Pittsburgh's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/30 | Pittsburgh vs Duquesne | -39.5W61–9 | 58.5 | W61–9 | O | Y |
| Sat 9/6 | Pittsburgh vs Central Michigan | -21.5W45–17 | 48.5 | W45–17 | O | Y |
| Sat 9/13 | Pittsburgh at West Virginia | -6.5L24–31 | 54.5 | L24–31 | O | N |
| — Bye Week — | ||||||
| Sat 9/27 | Pittsburgh vs Louisville | +3.0L27–34 | 53.5 | L27–34 | O | N |
| Sat 10/4 | Pittsburgh vs Boston College | -6.5W48–7 | 54.5 | W48–7 | O | Y |
| Sat 10/11 | Pittsburgh at Florida State | +10.5W34–31 | 56.5 | W34–31 | O | Y |
| Sat 10/18 | Pittsburgh at Syracuse | -9.5W30–13 | 54.5 | W30–13 | U | Y |
| Sat 10/25 | Pittsburgh vs NC State | -5.5W53–34 | 52.5 | W53–34 | O | Y |
| Sat 11/1 | Pittsburgh at Stanford | -13.5W35–20 | 51.5 | W35–20 | O | Y |
| — Bye Week — | ||||||
| Sat 11/15 | Pittsburgh vs Notre Dame | +12.5L15–37 | 55.5 | L15–37 | U | N |
| Sat 11/22 | Pittsburgh at Georgia Tech | +2.5W42–28 | 61.5 | W42–28 | O | Y |
| Sat 11/29 | Pittsburgh vs Miami | +6.5L7–38 | 49.5 | L7–38 | U | N |
| Sat 12/27 | Pittsburgh vs East Carolina | -13.5L17–23 | 51.5 | L17–23 | U | N |
Stanford 2025 Schedule
Stanford's 2025 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/23 | Stanford at Hawai'i | +1.5L20–23 | 53.5 | L20–23 | U | N |
| Sat 9/6 | Stanford at BYU | +20.5L3–27 | 44.5 | L3–27 | U | N |
| Sat 9/13 | Stanford vs Boston College | +14.0W30–20 | 44.5 | W30–20 | O | Y |
| Sat 9/20 | Stanford at Virginia | +16.5L20–48 | 48.5 | L20–48 | O | N |
| Sat 9/27 | Stanford vs San José State | -3.0W30–29 | 49.5 | W30–29 | O | N |
| — Bye Week — | ||||||
| Sat 10/11 | Stanford at SMU | +19.5L10–34 | 55.5 | L10–34 | U | N |
| Sat 10/18 | Stanford vs Florida State | +17.5W20–13 | 55.5 | W20–13 | U | Y |
| Sat 10/25 | Stanford at Miami | +28.5L7–42 | 45.5 | L7–42 | O | N |
| Sat 11/1 | Stanford vs Pittsburgh | +13.5L20–35 | 51.5 | L20–35 | O | N |
| Sat 11/8 | Stanford at North Carolina | +8.5L15–20 | 41.5 | L15–20 | U | Y |
| — Bye Week — | ||||||
| Sat 11/22 | Stanford vs California | +4.5W31–10 | 47.5 | W31–10 | U | Y |
| Sat 11/29 | Stanford vs Notre Dame | +32.5L20–49 | 50.5 | L20–49 | O | 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
→ Pittsburgh
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Pittsburgh
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 ·
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 +2.21
CSS Edge (season-to-date)
Teams with this edge win 80.6% of games historically
Based on 8 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Pittsburgh Edge
Pittsburgh +45.3
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
Stanford
1 — 0 sequences
✗ Predicted incorrectly
GC Battle
Pittsburgh
12.2 — 72.1 GC score
✓ Predicted correctly
Game Result
Pittsburgh won by 15
✓ Model called it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Pittsburgh 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
Pittsburgh
Pat Narduzzi #1
72–55 (57%)
· Yr 11 at school
OC
Kade Bell
Yr 2
#1
DC
Randy Bates
Yr 3
#1
Stanford
Troy Taylor #1
6–18 (25%)
· Yr 3 at school
OC
Troy Taylor
Yr 3
#1
DC
Bobby April
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 ✓