Matchup Prediction
Metrics disagree on this matchup
Momentum Control favors Baylor,
while Game Control favors BYU.
Split signals historically show weaker predictive confidence — treat as a toss-up.
⚡ Split Signal — Metrics Disagree
Momentum Control
71.6%
Baylor wins
Solid
Game Control
64.9%
BYU wins
Lean
Vegas Spread
Baylor -3
O/U 47.0
DraftKings
Advanced Stats
Advanced factors are split · No strong agreement signal
↓ See full breakdown
BYU 2024 Schedule
BYU's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | BYU vs Southern Illinois | -29 | — | — | — | — |
| Fri 9/6 | BYU at SMU | +12.5W18–15 | 55.5 | W18–15 | U | Y |
| Sat 9/14 | BYU at Wyoming | -9.5W34–14 | 40.5 | W34–14 | O | Y |
| Sat 9/21 | BYU vs Kansas State | +7.5W38–9 | 49.5 | W38–9 | U | Y |
| Sat 9/28 | BYU at Baylor | +3.0W34–28 | 47.0 | W34–28 | O | Y |
| — Bye Week — | ||||||
| Sat 10/12 | BYU vs Arizona | -3.0W41–19 | 48.5 | W41–19 | O | Y |
| Fri 10/18 | BYU vs Oklahoma State | -8.5W38–35 | 53.0 | W38–35 | O | N |
| Sat 10/26 | BYU at UCF | +2.5W37–24 | 53.5 | W37–24 | O | Y |
| — Bye Week — | ||||||
| Sat 11/9 | BYU at Utah | -3.5W22–21 | 40.5 | W22–21 | O | N |
| Sat 11/16 | BYU vs Kansas | -3.0L13–17 | 55.5 | L13–17 | U | N |
| Sat 11/23 | BYU at Arizona State | +3.5L23–28 | 49.0 | L23–28 | O | N |
| Sat 11/30 | BYU vs Houston | -9.5W30–18 | 39.5 | W30–18 | O | Y |
| Sat 12/28 | BYU vs Colorado | +3.0W36–14 | 55.5 | W36–14 | U | Y |
Baylor 2024 Schedule
Baylor's 2024 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/31 | Baylor vs Tarleton State | -33.5W45–3 | 53.5 | W45–3 | U | Y |
| Sat 9/7 | Baylor at Utah | +14.5L12–23 | 52.5 | L12–23 | U | Y |
| Sat 9/14 | Baylor vs Air Force | -17.5W31–3 | 40.5 | W31–3 | U | Y |
| Sat 9/21 | Baylor at Colorado | +2.5L31–38 | 52.5 | L31–38 | O | N |
| Sat 9/28 | Baylor vs BYU | -3.0L28–34 | 47.0 | L28–34 | O | N |
| Sat 10/5 | Baylor at Iowa State | +13.0L21–43 | 45.0 | L21–43 | O | N |
| — Bye Week — | ||||||
| Sat 10/19 | Baylor at Texas Tech | +4.5W59–35 | 56.0 | W59–35 | O | Y |
| Sat 10/26 | Baylor vs Oklahoma State | -7.0W38–28 | 64.5 | W38–28 | O | Y |
| Sat 11/2 | Baylor vs TCU | -2.5W37–34 | 64.0 | W37–34 | O | Y |
| — Bye Week — | ||||||
| Sat 11/16 | Baylor at West Virginia | -2.0W49–35 | 60.0 | W49–35 | O | Y |
| Sat 11/23 | Baylor at Houston | -7.0W20–10 | 51.0 | W20–10 | U | Y |
| Sat 11/30 | Baylor vs Kansas | +2.5W45–17 | 62.5 | W45–17 | U | Y |
| Tue 12/31 | Baylor vs LSU | -3.0L31–44 | 62.5 | L31–44 | O | 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
Split
Metrics disagree
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?
Baylor Edge
Baylor +1.33
CSS Edge (season-to-date)
Teams with this edge win 71.6% of games historically
Based on 3 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
BYU Edge
BYU +12.4
GC Edge (season-to-date)
Teams with this edge win 64.9% of games historically
Based on 4 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
BYU
Kalani Sitake #1
61–41 (60%)
· Yr 9 at school
OC
Aaron Roderick
Yr 3
#1
DC
Jay Hill
Yr 2
#1
Baylor
Dave Aranda #1
23–25 (48%)
· Yr 5 at school
OC
Jake Spavital
Yr 1
#1
DC
Matt Powledge
Yr 2
#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 ✓