Matchup Prediction
Toss-up — no clear edge
Neither metric shows a meaningful pre-game edge in this matchup.
Momentum Control
58.4%
—
Lean
Game Control
75.9%
UNLV wins
Solid
Vegas Spread
Kansas -7
O/U 57.0
DraftKings
Advanced Stats
PPA + Success Rate agree → UNLV
· 73.9% ATS historically
↓ See full breakdown
UNLV 2024 Schedule
UNLV's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | UNLV at Houston | +3.0W27–7 | 56.0 | W27–7 | U | Y |
| Sat 9/7 | UNLV vs Utah Tech | -41.5W72–14 | 55.5 | W72–14 | O | Y |
| Fri 9/13 | UNLV vs Kansas | +7.0W23–20 | 57.0 | W23–20 | U | Y |
| — Bye Week — | ||||||
| Sat 9/28 | UNLV vs Fresno State | -2.5W59–14 | 51.0 | W59–14 | O | Y |
| Fri 10/4 | UNLV vs Syracuse | -5.5L41–44 | 55.5 | L41–44 | O | N |
| Fri 10/11 | UNLV at Utah State | -19.0W50–34 | 67.5 | W50–34 | O | N |
| Sat 10/19 | UNLV at Oregon State | -6.5W33–25 | 61.0 | W33–25 | U | Y |
| Fri 10/25 | UNLV vs Boise State | +4.0L24–29 | 64.0 | L24–29 | U | N |
| — Bye Week — | ||||||
| Sat 11/9 | UNLV at Hawai'i | -12.0W29–27 | 51.5 | W29–27 | O | N |
| Sat 11/16 | UNLV vs San Diego State | -22.0W41–20 | 55.5 | W41–20 | O | N |
| Fri 11/22 | UNLV at San José State | -7.5W27–16 | 59.5 | W27–16 | U | Y |
| Sat 11/30 | UNLV vs Nevada | -17.5W38–14 | 54.5 | W38–14 | U | Y |
| Fri 12/6 | UNLV at Boise State | +3.5L7–21 | 57.5 | L7–21 | U | N |
| Wed 12/18 | UNLV vs California | -3.0W24–13 | 45.0 | W24–13 | U | Y |
Kansas 2024 Schedule
Kansas's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Thu 8/29 | Kansas vs Lindenwood | -48.5W48–3 | 62.5 | W48–3 | U | N |
| Sat 9/7 | Kansas at Illinois | -4.5L17–23 | 58.5 | L17–23 | U | N |
| Fri 9/13 | Kansas vs UNLV | -7.0L20–23 | 57.0 | L20–23 | U | N |
| Sat 9/21 | Kansas at West Virginia | +1.5L28–32 | 56.0 | L28–32 | O | N |
| Sat 9/28 | Kansas vs TCU | -1.5L27–38 | 58.5 | L27–38 | O | N |
| Sat 10/5 | Kansas at Arizona State | +2.5L31–35 | 50.0 | L31–35 | O | N |
| — Bye Week — | ||||||
| Sat 10/19 | Kansas vs Houston | -4.5W42–14 | 45.5 | W42–14 | O | Y |
| Sat 10/26 | Kansas at Kansas State | +10.0L27–29 | 56.5 | L27–29 | U | Y |
| — Bye Week — | ||||||
| Sat 11/9 | Kansas vs Iowa State | +2.5W45–36 | 50.5 | W45–36 | O | Y |
| Sat 11/16 | Kansas at BYU | +3.0W17–13 | 55.5 | W17–13 | U | Y |
| Sat 11/23 | Kansas vs Colorado | +2.5W37–21 | 59.0 | W37–21 | U | Y |
| Sat 11/30 | Kansas at Baylor | -2.5L17–45 | 62.5 | L17–45 | U | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2024 season
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
→ UNLV
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 · 2024 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?
UNLV +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UNLV Edge
UNLV +31.6
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 2 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
CSS and GC disagree on this matchup. When the metrics split, historical cover rates are essentially random — treat this as a coin flip against the spread.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
UNLV
Barry Odom #1
9–5 (64%)
· Yr 2 at school
OC
Brennan Marion
Yr 2
#1
DC
Michael Scherer
Yr 2
#1
Kansas
Lance Leipold #1
17–21 (45%)
· Yr 4 at school
OC
Jeff Grimes
Yr 1
#1
DC
Brian Borland
Yr 3
#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 ✓