Matchup Prediction
UCF
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UCF entering this game.
Momentum Control
71.6%
UCF wins
Solid
Game Control
76%
UCF wins
Strong
Vegas Spread
UCF -8
O/U 56.5
William Hill (New Jersey)
Advanced Stats
PPA + Success Rate agree → UCF
· 73.9% ATS historically
↓ See full breakdown
Baylor 2023 Schedule
Baylor's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Baylor vs Texas State | -26.5L31–42 | 58.5 | L31–42 | O | N |
| Sat 9/9 | Baylor vs Utah | +7.0L13–20 | 46.5 | L13–20 | U | Y |
| Sat 9/16 | Baylor vs Long Island University | -44.0W30–7 | 54.0 | W30–7 | U | N |
| Sat 9/23 | Baylor vs Texas | +17.5L6–38 | 49.5 | L6–38 | U | N |
| Sat 9/30 | Baylor at UCF | +8.0W36–35 | 56.5 | W36–35 | O | Y |
| Sat 10/7 | Baylor vs Texas Tech | +2.5L14–39 | 59.5 | L14–39 | U | N |
| — Bye Week — | ||||||
| Sat 10/21 | Baylor at Cincinnati | +2.5W32–29 | 51.5 | W32–29 | O | Y |
| Sat 10/28 | Baylor vs Iowa State | +3.0L18–30 | 47.0 | L18–30 | O | N |
| Sat 11/4 | Baylor vs Houston | -3.0L24–25 | 58.5 | L24–25 | U | N |
| Sat 11/11 | Baylor at Kansas State | +20.5L25–59 | 55.5 | L25–59 | O | N |
| Sat 11/18 | Baylor at TCU | +13.0L17–42 | 62.0 | L17–42 | U | N |
| Sat 11/25 | Baylor vs West Virginia | +6.5L31–34 | 53.5 | L31–34 | O | Y |
UCF 2023 Schedule
UCF's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Thu 8/31 | UCF vs Kent State | -35.0W56–6 | 54.0 | W56–6 | O | Y |
| Sat 9/9 | UCF at Boise State | -3.0W18–16 | 58.5 | W18–16 | U | N |
| Sat 9/16 | UCF vs Villanova | -26.5W48–14 | 54.0 | W48–14 | O | Y |
| Sat 9/23 | UCF at Kansas State | +6.0L31–44 | 53.5 | L31–44 | O | N |
| Sat 9/30 | UCF vs Baylor | -8.0L35–36 | 56.5 | L35–36 | O | N |
| Sat 10/7 | UCF at Kansas | -2.0L22–51 | 65.0 | L22–51 | O | N |
| — Bye Week — | ||||||
| Sat 10/21 | UCF at Oklahoma | +17.0L29–31 | 67.5 | L29–31 | U | Y |
| Sat 10/28 | UCF vs West Virginia | -6.5L28–41 | 59.5 | L28–41 | O | N |
| Sat 11/4 | UCF at Cincinnati | -3.5W28–26 | 59.5 | W28–26 | U | N |
| Sat 11/11 | UCF vs Oklahoma State | +2.5W45–3 | 63.5 | W45–3 | U | Y |
| Sat 11/18 | UCF at Texas Tech | +2.0L23–24 | 59.0 | L23–24 | U | Y |
| Sat 11/25 | UCF vs Houston | -15.5W27–13 | 61.5 | W27–13 | U | N |
| Fri 12/22 | UCF vs Georgia Tech | -6.0L17–30 | 66.5 | L17–30 | U | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2023 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
→ UCF
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 · 2023 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?
UCF Edge
UCF +1.00
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?
UCF Edge
UCF +21.0
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 4 games this season
Actual Result
CSS Battle
Baylor
2 — 3 sequences
✗ Predicted incorrectly
GC Battle
UCF
79.3 — 10.4 GC score
✓ Predicted correctly
Game Result
Baylor won by 1
✗ Model missed it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UCF with a large edge. Historically, dominant teams like this are fully priced into the spread — the agreed-upon team covers just 50.2% of the time. The metrics predict game control better than they beat the number.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Baylor
Dave Aranda #1
21–18 (54%)
· Yr 4 at school
OC
Jeff Grimes
Yr 3
#1
DC
Matt Powledge
Yr 1
#1
UCF
Gus Malzahn #1
21–9 (70%)
· Yr 3 at school
OC
Darin Hinshaw
Yr 1
#1
DC
David Gibbs
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 ✓