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
67.1%
TCU wins
Solid
Vegas Spread
TCU -7
O/U 63.5
William Hill (New Jersey)
Advanced Stats
All 4 factors agree → SMU
· 83.1% ATS historically when all four align
↓ See full breakdown
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 |
TCU 2023 Schedule
TCU's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | TCU vs Colorado | -20.5L42–45 | 59.5 | L42–45 | O | N |
| Sat 9/9 | TCU vs Nicholls | -41.5W41–6 | 59.5 | W41–6 | U | N |
| Sat 9/16 | TCU at Houston | -7.5W36–13 | 64.0 | W36–13 | U | Y |
| Sat 9/23 | TCU vs SMU | -7.0W34–17 | 63.5 | W34–17 | U | Y |
| Sat 9/30 | TCU vs West Virginia | -14.0L21–24 | 52.0 | L21–24 | U | N |
| Sat 10/7 | TCU at Iowa State | -6.5L14–27 | 52.5 | L14–27 | U | N |
| Sat 10/14 | TCU vs BYU | -5.0W44–11 | 52.5 | W44–11 | O | Y |
| Sat 10/21 | TCU at Kansas State | +5.5L3–41 | 60.0 | L3–41 | U | N |
| — Bye Week — | ||||||
| Thu 11/2 | TCU at Texas Tech | +2.5L28–35 | 59.5 | L28–35 | O | N |
| Sat 11/11 | TCU vs Texas | +13.0L26–29 | 56.0 | L26–29 | U | Y |
| Sat 11/18 | TCU vs Baylor | -13.0W42–17 | 62.0 | W42–17 | U | Y |
| Fri 11/24 | TCU at Oklahoma | +12.5L45–69 | 66.5 | L45–69 | 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
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 +0.00
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 2 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
TCU Edge
TCU +17.2
GC Edge (season-to-date)
Teams with this edge win 67.1% of games historically
Based on 3 games this season
Actual Result
CSS Battle
TCU
3 — 0 sequences
GC Battle
TCU
68.2 — 14.2 GC score
✓ Predicted correctly
Game Result
TCU won by 17
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on TCU 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
SMU
Rhett Lashlee #1
9–7 (56%)
· Yr 2 at school
OC
Casey Woods
Yr 2
#1
DC
Scott Symons
Yr 2
#1
TCU
Sonny Dykes #1
15–3 (83%)
· Yr 2 at school
OC
Kendal Briles
Yr 1
#1
DC
Joe Gillespie
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 ✓