Matchup Prediction
SMU
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
SMU entering this game.
Momentum Control
73.7%
SMU wins
Solid
Game Control
75.9%
SMU wins
Solid
Vegas Spread
SMU -12
O/U 59.5
DraftKings
Advanced Stats
PPA + Success Rate agree → SMU
· 73.9% ATS historically
↓ See full breakdown
SMU 2023 Schedule
SMU's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | SMU vs Louisiana Tech | -21.0W38–14 | 66.0 | W38–14 | U | Y |
| Sat 9/9 | SMU at Oklahoma | +16.5L11–28 | 68.5 | L11–28 | U | N |
| Sat 9/16 | SMU vs Prairie View A&M | -42.5W69–0 | 63.5 | W69–0 | O | Y |
| Sat 9/23 | SMU at TCU | +7.0L17–34 | 63.5 | L17–34 | U | N |
| Sat 9/30 | SMU vs Charlotte | -22.5W34–16 | 53.0 | W34–16 | U | N |
| — Bye Week — | ||||||
| Thu 10/12 | SMU at East Carolina | -11.5W31–10 | 48.5 | W31–10 | U | Y |
| Fri 10/20 | SMU at Temple | -24.0W55–0 | 53.0 | W55–0 | O | Y |
| Sat 10/28 | SMU vs Tulsa | -20.5W69–10 | 55.0 | W69–10 | O | Y |
| Sat 11/4 | SMU at Rice | -12.0W36–31 | 59.5 | W36–31 | O | N |
| Fri 11/10 | SMU vs North Texas | -21.5W45–21 | 67.5 | W45–21 | U | Y |
| Sat 11/18 | SMU at Memphis | -9.5W38–34 | 64.5 | W38–34 | O | N |
| Sat 11/25 | SMU vs Navy | -20.0W59–14 | 46.0 | W59–14 | O | Y |
| Sat 12/2 | SMU at Tulane | +4.0W26–14 | 50.5 | W26–14 | U | Y |
| Thu 12/28 | SMU vs Boston College | -13.5L14–23 | 49.0 | L14–23 | U | N |
Rice 2023 Schedule
Rice's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Rice at Texas | +35.5L10–37 | 59.0 | L10–37 | U | Y |
| Sat 9/9 | Rice vs Houston | +7.5W43–41 | 51.0 | W43–41 | O | Y |
| Sat 9/16 | Rice vs Texas Southern | -35.5W59–7 | 60.0 | W59–7 | O | Y |
| Sat 9/23 | Rice at South Florida | -2.5L29–42 | 56.5 | L29–42 | O | N |
| Sat 9/30 | Rice vs East Carolina | -3.5W24–17 | 47.0 | W24–17 | U | Y |
| Sat 10/7 | Rice vs UConn | -10.0L31–38 | 47.5 | L31–38 | O | N |
| — Bye Week — | ||||||
| Thu 10/19 | Rice at Tulsa | +3.0W42–10 | 56.5 | W42–10 | U | Y |
| Sat 10/28 | Rice vs Tulane | +10.0L28–30 | 55.0 | L28–30 | O | Y |
| Sat 11/4 | Rice vs SMU | +12.0L31–36 | 59.5 | L31–36 | O | Y |
| Sat 11/11 | Rice at UTSA | +13.5L14–34 | 53.5 | L14–34 | U | N |
| Sat 11/18 | Rice at Charlotte | +0.5W28–7 | 46.5 | W28–7 | U | Y |
| Sat 11/25 | Rice vs Florida Atlantic | -5.0W24–21 | 46.5 | W24–21 | U | N |
| Tue 12/26 | Rice vs Texas State | +3.5L21–45 | 58.5 | L21–45 | O | 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
→ SMU
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?
SMU Edge
SMU +1.14
CSS Edge (season-to-date)
Teams with this edge win 73.7% of games historically
Based on 7 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +20.1
GC Edge (season-to-date)
Teams with this edge win 75.9% of games historically
Based on 8 games this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on SMU 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
SMU
Rhett Lashlee #1
9–7 (56%)
· Yr 2 at school
OC
Casey Woods
Yr 2
#1
DC
Scott Symons
Yr 2
#1
Rice
Mike Bloomgren #1
18–40 (31%)
· Yr 6 at school
OC
Marques Tuiasosopo
Yr 3
#1
DC
Brian Smith
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 ✓