Matchup Prediction
SMU
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
SMU entering this game.
Momentum Control
58.4%
SMU wins
Lean
Game Control
67.1%
SMU wins
Solid
Vegas Spread
SMU -6
O/U 63.0
teamrankings
Advanced Stats
PPA + Success Rate agree → SMU
· 73.9% ATS historically
↓ See full breakdown
Tulsa 2021 Schedule
Tulsa's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Thu 9/2 | Tulsa vs UC Davis | -22.0L17–19 | 54.5 | L17–19 | U | N |
| Sat 9/11 | Tulsa at Oklahoma State | +11.5L23–28 | 51.0 | L23–28 | U | Y |
| Sat 9/18 | Tulsa at Ohio State | +24.5L20–41 | 60.5 | L20–41 | O | Y |
| Sat 9/25 | Tulsa vs Arkansas State | -14.5W41–34 | 65.0 | W41–34 | O | N |
| Fri 10/1 | Tulsa vs Houston | -3.0L10–45 | 54.0 | L10–45 | O | N |
| Sat 10/9 | Tulsa vs Memphis | -3.0W35–29 | 60.5 | W35–29 | O | Y |
| Sat 10/16 | Tulsa at South Florida | -7.5W32–31 | 56.0 | W32–31 | O | N |
| — Bye Week — | ||||||
| Fri 10/29 | Tulsa vs Navy | -11.0L17–20 | 46.0 | L17–20 | U | N |
| Sat 11/6 | Tulsa at Cincinnati | +22.5L20–28 | 56.0 | L20–28 | U | Y |
| Sat 11/13 | Tulsa at Tulane | -3.0W20–13 | 55.5 | W20–13 | U | Y |
| Sat 11/20 | Tulsa vs Temple | -22.0W44–10 | 50.5 | W44–10 | O | Y |
| Sat 11/27 | Tulsa at SMU | +6.0W34–31 | 63.0 | W34–31 | O | Y |
| Mon 12/20 | Tulsa vs Old Dominion | -7.5W30–17 | 55.0 | W30–17 | U | Y |
SMU 2021 Schedule
SMU's 2021 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/4 | SMU vs Abilene Christian | -32.0W56–9 | 66.0 | W56–9 | U | Y |
| Sat 9/11 | SMU vs North Texas | -22.5W35–12 | 75.5 | W35–12 | U | Y |
| Sat 9/18 | SMU at Louisiana Tech | -11.0W39–37 | 65.0 | W39–37 | O | N |
| Sat 9/25 | SMU at TCU | +8.0W42–34 | 66.0 | W42–34 | O | Y |
| Sat 10/2 | SMU vs South Florida | -21.5W41–17 | 68.5 | W41–17 | U | Y |
| Sat 10/9 | SMU at Navy | -13.5W31–24 | 57.0 | W31–24 | U | N |
| — Bye Week — | ||||||
| Thu 10/21 | SMU vs Tulane | -14.0W55–26 | 70.5 | W55–26 | O | Y |
| Sat 10/30 | SMU at Houston | -1.0L37–44 | 61.5 | L37–44 | O | N |
| Sat 11/6 | SMU at Memphis | -3.5L25–28 | 72.0 | L25–28 | U | N |
| Sat 11/13 | SMU vs UCF | -7.0W55–28 | 61.5 | W55–28 | O | Y |
| Sat 11/20 | SMU at Cincinnati | +9.5L14–48 | 65.5 | L14–48 | U | N |
| Sat 11/27 | SMU vs Tulsa | -6.0L31–34 | 63.0 | L31–34 | O | N |
| Wed 12/29 | SMU vs Virginia | +2.5 | 71.0 | — | — | — |
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
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 · 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?
SMU Edge
SMU +0.82
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 11 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +18.1
GC Edge (season-to-date)
Teams with this edge win 67.1% of games historically
Based on 11 games this season
Actual Result
CSS Battle
Tulsa
2 — 3 sequences
✗ Predicted incorrectly
GC Battle
SMU
48.4 — 40.6 GC score
✓ Predicted correctly
Game Result
Tulsa won by 3
✗ Model missed it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on SMU with a solid GC edge. Teams with this profile have covered 53.0% of the time historically (n=330) — a mild lean.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
Tulsa
Philip Montgomery #1
31–43 (42%)
· Yr 7 at school
OC
Philip Montgomery
Yr 1
#1
DC
Joseph Gillespie
Yr 1
#1
SMU
Sonny Dykes #1
25–14 (64%)
· Yr 4 at school
OC
Garrett Riley
Yr 1
#1
DC
Jim Leavitt
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 ✓