Matchup Prediction
SMU
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
SMU entering this game.
Momentum Control
71.6%
SMU wins
Solid
Game Control
76%
SMU wins
Strong
Vegas Spread
SMU -20
O/U 46.0
DraftKings
Advanced Stats
All 4 factors agree → SMU
· 83.1% ATS historically when all four align
↓ See full breakdown
Navy 2023 Schedule
Navy's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 8/26 | Navy vs Notre Dame | +20.5L3–42 | 49.0 | L3–42 | U | N |
| Sat 9/9 | Navy vs Wagner | -43.5W24–0 | 49.0 | W24–0 | U | N |
| Thu 9/14 | Navy at Memphis | +12.5L24–28 | 47.0 | L24–28 | O | Y |
| — Bye Week — | ||||||
| Sat 9/30 | Navy vs South Florida | -4.0L30–44 | 54.0 | L30–44 | O | N |
| Sat 10/7 | Navy vs North Texas | -6.5W27–24 | 60.5 | W27–24 | U | N |
| Sat 10/14 | Navy at Charlotte | -3.5W14–0 | 44.0 | W14–0 | U | Y |
| Sat 10/21 | Navy vs Air Force | +11.0L6–17 | 34.0 | L6–17 | U | Y |
| — Bye Week — | ||||||
| Sat 11/4 | Navy at Temple | -7.0L18–32 | 46.0 | L18–32 | O | N |
| Sat 11/11 | Navy vs UAB | +3.5W31–6 | 52.5 | W31–6 | U | Y |
| Sat 11/18 | Navy vs East Carolina | -2.5W10–0 | 30.5 | W10–0 | U | Y |
| Sat 11/25 | Navy at SMU | +20.0L14–59 | 46.0 | L14–59 | O | N |
| — Bye Week — | ||||||
| Sat 12/9 | Navy vs Army | +2.0L11–17 | 28.0 | L11–17 | U | N |
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 |
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
All 4 Agree
→ SMU
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ SMU
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 +1.44
CSS Edge (season-to-date)
Teams with this edge win 71.6% of games historically
Based on 10 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
SMU Edge
SMU +26.5
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 11 games this season
Actual Result
CSS Battle
SMU
5 — 0 sequences
✓ Predicted correctly
GC Battle
SMU
96.1 — 2.1 GC score
✓ Predicted correctly
Game Result
SMU 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 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
Navy
Brian Newberry #1
1–2 (33%)
· Yr 1 at school
OC
Grant Chesnut
Yr 1
#1
DC
P.J. Volker
Yr 1
#1
SMU
Rhett Lashlee #1
9–7 (56%)
· Yr 2 at school
OC
Casey Woods
Yr 2
#1
DC
Scott Symons
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: 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 ✓