Sat, Oct 31 2026
·
Week 9
·
🏟 Jones AT&T Stadium
Lubbock, TX
·
Turf
·
60,862 cap
Arizona✈ 534 mi+2 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
Texas Tech
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Texas Tech entering this game.
Momentum Control
71.6%
Texas Tech wins
Solid
Game Control
76%
Texas Tech wins
Strong
Advanced Stats
All 4 factors agree → Texas Tech
· 83.1% ATS historically when all four align
↓ See full breakdown
Arizona 2026 Schedule
Arizona's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Arizona vs Northern Arizona | -30.5 | — | — | — | — |
| Sat 9/12 | Arizona at BYU | +5 | — | — | — | — |
| Sat 9/19 | Arizona vs Northern Illinois | -29 | — | — | — | — |
| Sat 9/26 | Arizona at Washington State | -9 | — | — | — | — |
| Sat 10/3 | Arizona vs Cincinnati | -11.5 | — | — | — | — |
| Sat 10/10 | Arizona at West Virginia | -11 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/24 | Arizona vs Iowa State | -6.5 | — | — | — | — |
| Sat 10/31 | Arizona at Texas Tech | +19.5 | — | — | — | — |
| Sat 11/7 | Arizona vs TCU | -6 | — | — | — | — |
| Sat 11/14 | Arizona vs Utah | -3.5 | — | — | — | — |
| Sat 11/21 | Arizona at Kansas State | -1.5 | — | — | — | — |
| Sat 11/28 | Arizona vs Arizona State | -7.5 | — | — | — | — |
Texas Tech 2026 Schedule
Texas Tech's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Texas Tech vs Abilene Christian | -37 | — | — | — | — |
| Sat 9/12 | Texas Tech at Oregon State | -31.5 | — | — | — | — |
| Sat 9/19 | Texas Tech vs Houston | -22.5 | — | — | — | — |
| Sat 9/26 | Texas Tech vs Sam Houston | -38 | — | — | — | — |
| Sat 10/3 | Texas Tech at Colorado | -28.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/17 | Texas Tech vs Arizona State | -24.5 | — | — | — | — |
| Sat 10/24 | Texas Tech at Cincinnati | -23.5 | — | — | — | — |
| Sat 10/31 | Texas Tech vs Arizona | -19.5 | — | — | — | — |
| Sat 11/7 | Texas Tech vs West Virginia | -30 | — | — | — | — |
| Sat 11/14 | Texas Tech at Oklahoma State | -20 | — | — | — | — |
| Sat 11/21 | Texas Tech at Baylor | -22.5 | — | — | — | — |
| Sat 11/28 | Texas Tech vs TCU | -22.5 | — | — | — | — |
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
→ Texas Tech
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Texas Tech
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Texas Tech
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?
Texas Tech Edge
Texas Tech +1.21
CSS Edge (season-to-date)
Teams with this edge win 71.6% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Texas Tech Edge
Texas Tech +23.1
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
Arizona
Brent Brennan #58
13–12 (52%)
· Yr 3 at school
OC
Seth Doege
Yr 2
#33
DC
Danny Gonzales
Yr 2
#38
Texas Tech
Joey McGuire #47
35–18 (66%)
· Yr 5 at school
OC
Mack Leftwich
Yr 2
#3
DC
Shiel Wood
Yr 2
#4
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 ✓