Sat, Nov 14 2026
·
Week 11
·
🏟 Falcon Stadium
Colorado Springs, CO
·
Turf
·
46,692 cap
San José State✈ 930 mi+1 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
Metrics disagree on this matchup
Momentum Control favors San José State,
while Game Control favors Air Force.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
61.3%
San José State wins
Lean
Game Control
50.6%
Air Force wins
Toss-up
Advanced Stats
PPA + Success Rate agree → San José State
· 73.9% ATS historically
↓ See full breakdown
San José State 2026 Schedule
San José State's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/29 | San José State at USC | +31.5 | — | — | — | — |
| Fri 9/4 | San José State at Eastern Michigan | +8.5 | — | — | — | — |
| Sat 9/12 | San José State vs Cal Poly | -6 | — | — | — | — |
| Sat 9/19 | San José State vs Fresno State | +15 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/3 | San José State at Hawai'i | +17 | — | — | — | — |
| Sat 10/10 | San José State vs Wyoming | +5 | — | — | — | — |
| Sat 10/17 | San José State at UTEP | +1.5 | — | — | — | — |
| Sat 10/24 | San José State at Nevada | +3 | — | — | — | — |
| Sat 10/31 | San José State vs New Mexico | +15.5 | — | — | — | — |
| Sat 11/7 | San José State vs Northern Illinois | -3 | — | — | — | — |
| Sat 11/14 | San José State at Air Force | +16.5 | — | — | — | — |
| Sat 11/21 | San José State vs UNLV | +17.5 | — | — | — | — |
| Sat 11/28 | San José State vs North Dakota State | +15 | — | — | — | — |
Air Force 2026 Schedule
Air Force's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Air Force vs Duquesne | -19.5 | — | — | — | — |
| Sat 9/12 | Air Force vs North Dakota State | +1 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 9/26 | Air Force at Nevada | -10.5 | — | — | — | — |
| Sat 10/3 | Air Force vs Navy | +1 | — | — | — | — |
| Sat 10/10 | Air Force at Northern Illinois | -11.5 | — | — | — | — |
| Sat 10/17 | Air Force vs UNLV | +3.5 | — | — | — | — |
| Sat 10/24 | Air Force at Wyoming | -4 | — | — | — | — |
| Sat 10/31 | Air Force vs UConn | -6 | — | — | — | — |
| Sat 11/7 | Air Force at Army | +4.5 | — | — | — | — |
| Sat 11/14 | Air Force vs San José State | -16.5 | — | — | — | — |
| Sat 11/21 | Air Force vs UTEP | -17 | — | — | — | — |
| Sat 11/28 | Air Force at New Mexico | +6.5 | — | — | — | — |
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
Split
Metrics disagree
Elite · 82.4% ATS
PPA + PPO + Havoc
Split
Metrics disagree
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ San José State
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?
San José State Edge
San José State +0.45
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?
Air Force Edge
Air Force +0.2
GC Edge (season-to-date)
Teams with this edge win 50.6% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
San José State
Ken Niumatalolo #54
10–15 (40%)
· Yr 3 at school
OC
Craig Stutzmann
Yr 3
#136
DC
Bojay Filimoeatu
Yr 1
#68
Air Force
Troy Calhoun #107
139–97 (59%)
· Yr 20 at school
OC
Mike Thiessen
Yr 3
#113
DC
Brian Knorr
Yr 3
#101
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 ✓