Matchup Prediction
UTSA
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
UTSA entering this game.
Momentum Control
71.6%
UTSA wins
Solid
Game Control
76%
UTSA wins
Strong
Vegas Spread
UT San Antonio -17
O/U 53.0
teamrankings
Advanced Stats
All 4 factors agree → UTSA
· 83.1% ATS historically when all four align
↓ See full breakdown
Rice 2021 Schedule
Rice's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | Rice at Arkansas | +19.5L17–38 | 50.0 | L17–38 | O | N |
| Sat 9/11 | Rice vs Houston | +7.5L7–44 | 50.0 | L7–44 | O | N |
| Sat 9/18 | Rice at Texas | +26.0L0–58 | 52.0 | L0–58 | O | N |
| Sat 9/25 | Rice vs Texas Southern | -37.0W48–34 | 53.5 | W48–34 | O | N |
| Sat 10/2 | Rice vs Southern Miss | -1.5W24–19 | 45.0 | W24–19 | U | Y |
| — Bye Week — | ||||||
| Sat 10/16 | Rice at UTSA | +17.0L0–45 | 53.0 | L0–45 | U | N |
| Sat 10/23 | Rice at UAB | +23.5W30–24 | 44.5 | W30–24 | O | Y |
| Sat 10/30 | Rice vs North Texas | -1.5L24–30 | 55.0 | L24–30 | U | N |
| Sat 11/6 | Rice at Charlotte | +6.5L24–31 | 51.5 | L24–31 | O | N |
| Sat 11/13 | Rice vs Western Kentucky | +19.0L21–42 | 61.0 | L21–42 | O | N |
| Sat 11/20 | Rice at UTEP | +9.0L28–38 | 47.0 | L28–38 | O | N |
| Sat 11/27 | Rice vs Louisiana Tech | +3.5W35–31 | 52.5 | W35–31 | O | Y |
UTSA 2021 Schedule
UTSA's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | UTSA at Illinois | +4.5W37–30 | 52.0 | W37–30 | O | Y |
| Sat 9/11 | UTSA vs Lamar | -38.0W54–0 | 65.0 | W54–0 | U | Y |
| Sat 9/18 | UTSA vs Middle Tennessee | -11.5W27–13 | 60.0 | W27–13 | U | Y |
| Sat 9/25 | UTSA at Memphis | +3.0W31–28 | 66.5 | W31–28 | U | Y |
| Sat 10/2 | UTSA vs UNLV | -21.5W24–17 | 55.5 | W24–17 | U | N |
| Sat 10/9 | UTSA at Western Kentucky | +3.5W52–46 | 71.0 | W52–46 | O | Y |
| Sat 10/16 | UTSA vs Rice | -17.0W45–0 | 53.0 | W45–0 | U | Y |
| Sat 10/23 | UTSA at Louisiana Tech | -5.5W45–16 | 59.5 | W45–16 | O | Y |
| — Bye Week — | ||||||
| Sat 11/6 | UTSA at UTEP | -12.0W44–23 | 53.5 | W44–23 | O | Y |
| Sat 11/13 | UTSA vs Southern Miss | -32.5W27–17 | 54.0 | W27–17 | U | N |
| Sat 11/20 | UTSA vs UAB | -3.5W34–31 | 54.0 | W34–31 | O | N |
| Sat 11/27 | UTSA at North Texas | -8.5L23–45 | 60.0 | L23–45 | O | N |
| Fri 12/3 | UTSA vs Western Kentucky | +3.0W49–41 | 74.5 | W49–41 | O | Y |
| Tue 12/21 | UTSA vs San Diego State | +3.0L24–38 | 48.0 | L24–38 | O | N |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2021 season
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
All 4 Agree
→ UTSA
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ UTSA
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ UTSA
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 · 2021 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?
UTSA Edge
UTSA +1.17
CSS Edge (season-to-date)
Teams with this edge win 71.6% of games historically
Based on 6 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
UTSA Edge
UTSA +32.3
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 6 games this season
Actual Result
CSS Battle
UTSA
2 — 0 sequences
✓ Predicted correctly
GC Battle
UTSA
98.2 — 0.6 GC score
✓ Predicted correctly
Game Result
UTSA won by 45
✓ Model called it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on UTSA 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
Rice
Mike Bloomgren #1
7–26 (21%)
· Yr 4 at school
OC
Marques Tuiasosopo
Yr 1
#1
DC
Brian Smith
Yr 1
#1
UTSA
Jeff Traylor #1
10–5 (67%)
· Yr 2 at school
OC
Barry Lunney Jr.
Yr 1
#1
DC
Jess Loepp / Rod Wright
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: CSS is not a predictive 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: GS is not a predictive 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: CSS is not a predictive 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: GS is not a predictive 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 ✓