Sat, Sep 5 2026
·
Week 1
·
🏟 Memorial Stadium
Bloomington, IN
·
Turf
·
52,959 cap
North Texas✈ 721 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 North Texas,
while Game Control favors Indiana.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
61.3%
North Texas wins
Lean
Game Control
58.6%
Indiana wins
Lean
Advanced Stats
All 4 factors agree → Indiana
· 83.1% ATS historically when all four align
↓ See full breakdown
North Texas 2026 Schedule
North Texas's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | North Texas at Indiana | +31 | — | — | — | — |
| Sat 9/12 | North Texas vs UNLV | +7.5 | — | — | — | — |
| Sat 9/19 | North Texas at Texas State | +8 | — | — | — | — |
| Sat 9/26 | North Texas vs Houston Christian | -16 | — | — | — | — |
| Thu 10/1 | North Texas at Tulsa | +6.5 | — | — | — | — |
| Sat 10/10 | North Texas vs Charlotte | -20.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/24 | North Texas at Navy | +9.5 | — | — | — | — |
| Thu 10/29 | North Texas vs Florida Atlantic | -2 | — | — | — | — |
| Sat 11/7 | North Texas vs Rice | -11 | — | — | — | — |
| Sat 11/14 | North Texas at UTSA | +9 | — | — | — | — |
| Sat 11/21 | North Texas at Tulane | +7 | — | — | — | — |
| Sat 11/28 | North Texas vs UAB | -12 | — | — | — | — |
Indiana 2026 Schedule
Indiana's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Indiana vs North Texas | -31 | — | — | — | — |
| Sat 9/12 | Indiana vs Howard | -36 | — | — | — | — |
| Sat 9/19 | Indiana vs Western Kentucky | -30 | — | — | — | — |
| Sat 9/26 | Indiana vs Northwestern | -25.5 | — | — | — | — |
| Sat 10/3 | Indiana at Rutgers | -22.5 | — | — | — | — |
| Sat 10/10 | Indiana at Nebraska | -16.5 | — | — | — | — |
| Sat 10/17 | Indiana vs Ohio State | +2 | — | — | — | — |
| Sat 10/24 | Indiana at Michigan | -5.5 | — | — | — | — |
| Sat 10/31 | Indiana vs Minnesota | -22 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 11/14 | Indiana vs USC | -10.5 | — | — | — | — |
| Sat 11/21 | Indiana at Washington | -6.5 | — | — | — | — |
| Sat 11/28 | Indiana vs Purdue | -29 | — | — | — | — |
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
→ Indiana
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Indiana
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Indiana
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?
North Texas Edge
North Texas +0.01
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?
Indiana Edge
Indiana +7.0
GC Edge (season-to-date)
Teams with this edge win 58.6% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
North Texas
Neal Brown #117
0–0 (0%)
· Yr 1 at school
OC
Neal Brown
Yr 1
#67
DC
Matt Powledge
Yr 1
#136
Indiana
Curt Cignetti #2
27–2 (93%)
· Yr 3 at school
OC
Mike Shanahan
Yr 3
#2
DC
Bryant Haines
Yr 3
#2
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 ✓