Sat, Sep 19 2026
·
Week 3
·
🏟 Bill Snyder Family Football Stadium
Manhattan, KS
·
Turf
·
50,000 cap
Tulane✈ 737 miSame 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
Tulane
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Tulane entering this game.
Momentum Control
61.3%
Tulane wins
Lean
Game Control
58.3%
Tulane wins
Lean
Advanced Stats
All 4 factors agree → Kansas State
· 83.1% ATS historically when all four align
↓ See full breakdown
Tulane 2026 Schedule
Tulane's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Tulane at Duke | +4 | — | — | — | — |
| Sat 9/12 | Tulane vs South Alabama | -18.5 | — | — | — | — |
| Sat 9/19 | Tulane at Kansas State | +5.5 | — | — | — | — |
| Sat 9/26 | Tulane vs Southern Miss | -16 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/10 | Tulane at Army | -1 | — | — | — | — |
| Fri 10/16 | Tulane vs Memphis | -0.5 | — | — | — | — |
| Sat 10/24 | Tulane vs UTSA | -4 | — | — | — | — |
| Fri 10/30 | Tulane at Charlotte | -23.5 | — | — | — | — |
| Sat 11/7 | Tulane vs Tulsa | -10.5 | — | — | — | — |
| Sat 11/14 | Tulane at Rice | -17 | — | — | — | — |
| Sat 11/21 | Tulane vs North Texas | +0 | — | — | — | — |
| Fri 11/27 | Tulane at South Florida | +5.5 | — | — | — | — |
Kansas State 2026 Schedule
Kansas State's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Kansas State vs Nicholls | -29 | — | — | — | — |
| Sat 9/12 | Kansas State vs Washington State | -10 | — | — | — | — |
| Sat 9/19 | Kansas State vs Tulane | -5.5 | — | — | — | — |
| Sat 9/26 | Kansas State at Cincinnati | -2.5 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/10 | Kansas State vs Houston | -1.5 | — | — | — | — |
| Sat 10/17 | Kansas State vs Kansas | -7 | — | — | — | — |
| Sat 10/24 | Kansas State at Arizona State | +1.5 | — | — | — | — |
| Sat 10/31 | Kansas State at Colorado | -8 | — | — | — | — |
| Sat 11/7 | Kansas State vs Oklahoma State | -4 | — | — | — | — |
| Sat 11/14 | Kansas State at TCU | +3.5 | — | — | — | — |
| Sat 11/21 | Kansas State vs Arizona | +1.5 | — | — | — | — |
| Sat 11/28 | Kansas State at Iowa State | +3 | — | — | — | — |
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
→ Kansas State
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Kansas State
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Kansas 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?
Tulane Edge
Tulane +0.77
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?
Tulane Edge
Tulane +10.8
GC Edge (season-to-date)
Teams with this edge win 58.3% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
Tulane
Will Hall #137
0–0 (0%)
· Yr 1 at school
OC
Russ Callaway
Yr 1
#67
DC
Tayler Polk
Yr 1
#68
Kansas State
Collin Klein #77
0–0 (0%)
· Yr 1 at school
OC
Sean Gleeson
Yr 1
#37
DC
Jordan Peterson
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 ✓