Sat, Sep 3 2022
·
Week 1
·
🏟 Apogee Stadium
Denton, TX
·
Turf
·
30,850 cap
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
SMU -9.5
O/U 67.5
teamrankings
Advanced Stats
PPA + Success Rate agree → SMU
· 73.9% ATS historically
↓ See full breakdown
SMU 2022 Schedule
SMU's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/3 | SMU at North Texas | -9.5W48–10 | 67.5 | W48–10 | U | Y |
| Sat 9/10 | SMU vs Lamar | -48.5W45–16 | 66.0 | W45–16 | U | N |
| Sat 9/17 | SMU at Maryland | +3.0L27–34 | 74.0 | L27–34 | U | N |
| Sat 9/24 | SMU vs TCU | +2.5L34–42 | 72.0 | L34–42 | O | N |
| — Bye Week — | ||||||
| Wed 10/5 | SMU at UCF | +3.0L19–41 | 65.0 | L19–41 | U | N |
| Fri 10/14 | SMU vs Navy | -12.5W40–34 | 59.0 | W40–34 | O | N |
| Sat 10/22 | SMU vs Cincinnati | +3.5L27–29 | 59.5 | L27–29 | U | Y |
| Sat 10/29 | SMU at Tulsa | -1.0W45–34 | 63.5 | W45–34 | O | Y |
| Sat 11/5 | SMU vs Houston | -3.5W77–63 | 66.0 | W77–63 | O | Y |
| Sat 11/12 | SMU at South Florida | -17.5W41–23 | 72.5 | W41–23 | U | Y |
| Thu 11/17 | SMU at Tulane | +3.5L24–59 | 65.0 | L24–59 | O | N |
| Sat 11/26 | SMU vs Memphis | -4.5W34–31 | 69.0 | W34–31 | U | N |
| Sat 12/17 | SMU vs BYU | -4.5L23–24 | 65.0 | L23–24 | U | N |
North Texas 2022 Schedule
North Texas's 2022 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/27 | North Texas at UTEP | -1.5W31–13 | 52.5 | W31–13 | U | Y |
| Sat 9/3 | North Texas vs SMU | +9.5L10–48 | 67.5 | L10–48 | U | N |
| Sat 9/10 | North Texas vs Texas Southern | -38.5W59–27 | 64.5 | W59–27 | O | N |
| Sat 9/17 | North Texas at UNLV | +2.5L27–58 | 62.5 | L27–58 | O | N |
| Sat 9/24 | North Texas at Memphis | +13.0L34–44 | 68.5 | L34–44 | O | Y |
| Sat 10/1 | North Texas vs Florida Atlantic | +3.0W45–28 | 67.5 | W45–28 | O | Y |
| — Bye Week — | ||||||
| Sat 10/15 | North Texas vs Louisiana Tech | -6.5W47–27 | 68.0 | W47–27 | O | Y |
| Sat 10/22 | North Texas at UTSA | +10.0L27–31 | 73.0 | L27–31 | U | Y |
| Sat 10/29 | North Texas at Western Kentucky | +10.0W40–13 | 70.0 | W40–13 | U | Y |
| Sat 11/5 | North Texas vs Florida International | -21.0W52–14 | 63.5 | W52–14 | O | Y |
| Sat 11/12 | North Texas at UAB | +6.5L21–41 | 58.0 | L21–41 | O | N |
| — Bye Week — | ||||||
| Sat 11/26 | North Texas vs Rice | -14.5W21–17 | 57.0 | W21–17 | U | N |
| Fri 12/2 | North Texas at UTSA | +8.5L27–48 | 70.0 | L27–48 | O | N |
| Sat 12/17 | North Texas vs Boise State | +12.0L32–35 | 63.0 | L32–35 | O | Y |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2022 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 · 2022 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 +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 1 game this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +0.0
GC Edge (season-to-date)
Teams with this edge win 50.6% of games historically
Based on 1 game this season
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on North 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
SMU
Rhett Lashlee #1
0–0 (0%)
· Yr 1 at school
OC
Casey Woods
Yr 1
#1
DC
Scott Symons
Yr 1
#1
North Texas
Seth Littrell #1
37–38 (49%)
· Yr 7 at school
OC
Mike Bloesch
Yr 2
#1
DC
Phil Bennett
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: 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 ✓