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
50.6%
—
Toss-up
Vegas Spread
Texas -35.5
O/U 59.0
William Hill (New Jersey)
Advanced Stats
PPA + Success Rate agree → Texas
· 73.9% ATS historically
↓ See full breakdown
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 |
Texas 2023 Schedule
Texas's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Texas vs Rice | -35.5W37–10 | 59.0 | W37–10 | U | N |
| Sat 9/9 | Texas at Alabama | +7.0W34–24 | 53.0 | W34–24 | O | Y |
| Sat 9/16 | Texas vs Wyoming | -31.0W31–10 | 48.5 | W31–10 | U | N |
| Sat 9/23 | Texas at Baylor | -17.5W38–6 | 49.5 | W38–6 | U | Y |
| Sat 9/30 | Texas vs Kansas | -15.5W40–14 | 61.0 | W40–14 | U | Y |
| Sat 10/7 | Texas vs Oklahoma | -4.0L30–34 | 62.0 | L30–34 | O | N |
| — Bye Week — | ||||||
| Sat 10/21 | Texas at Houston | -24.0W31–24 | 60.5 | W31–24 | U | N |
| Sat 10/28 | Texas vs BYU | -20.5W35–6 | 48.5 | W35–6 | U | Y |
| Sat 11/4 | Texas vs Kansas State | -4.0W33–30 | 49.5 | W33–30 | O | N |
| Sat 11/11 | Texas at TCU | -13.0W29–26 | 56.0 | W29–26 | U | N |
| Sat 11/18 | Texas at Iowa State | -7.5W26–16 | 43.5 | W26–16 | U | Y |
| Fri 11/24 | Texas vs Texas Tech | -16.5W57–7 | 53.5 | W57–7 | O | Y |
| Sat 12/2 | Texas vs Oklahoma State | -14.0W49–21 | 55.0 | W49–21 | O | Y |
| Mon 1/1 | Texas vs Washington | -3.0L31–37 | 61.5 | L31–37 | 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
→ Texas
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?
Rice Edge
Rice +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 0 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Rice Edge
Rice +0.0
GC Edge (season-to-date)
Teams with this edge win 50.6% of games historically
Based on 0 games this season
Actual Result
CSS Battle
Texas
1 — 0 sequences
✗ Predicted incorrectly
GC Battle
Texas
87.9 — 4.6 GC score
✗ Predicted incorrectly
Game Result
Texas won by 27
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on Texas, but the GC edge is small. When metrics agree but GC is near-neutral, the agreed-upon team has covered only 46.7% of the time historically (n=224) — potentially a fade signal.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Rice
Mike Bloomgren #1
18–40 (31%)
· Yr 6 at school
OC
Marques Tuiasosopo
Yr 3
#1
DC
Brian Smith
Yr 3
#1
Texas
Steve Sarkisian #1
16–12 (57%)
· Yr 3 at school
OC
Kyle Flood
Yr 3
#1
DC
Jeff Choate
Yr 1
#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 ✓