Thu, Nov 2 2023
·
Week 10
·
🏟 Jones AT&T Stadium
Lubbock, TX
·
Turf
·
60,862 cap
TCU✈ 267 miSame TZ
Matchup Prediction
TCU
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
TCU entering this game.
Momentum Control
61.3%
TCU wins
Lean
Game Control
64.9%
TCU wins
Lean
Vegas Spread
Texas Tech -2.5
O/U 59.5
DraftKings
Advanced Stats
PPA + Success Rate agree → TCU
· 73.9% ATS historically
↓ See full breakdown
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 |
Texas Tech 2023 Schedule
Texas Tech's 2023 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/2 | Texas Tech at Wyoming | -13.0L33–35 | 50.5 | L33–35 | O | N |
| Sat 9/9 | Texas Tech vs Oregon | +4.5L30–38 | 70.0 | L30–38 | U | N |
| Sat 9/16 | Texas Tech vs Tarleton State | -36.5W41–3 | 75.5 | W41–3 | U | Y |
| Sat 9/23 | Texas Tech at West Virginia | -6.0L13–20 | 53.5 | L13–20 | U | N |
| Sat 9/30 | Texas Tech vs Houston | -8.5W49–28 | 52.0 | W49–28 | O | Y |
| Sat 10/7 | Texas Tech at Baylor | -2.5W39–14 | 59.5 | W39–14 | U | Y |
| Sat 10/14 | Texas Tech vs Kansas State | -1.0L21–38 | 57.0 | L21–38 | O | N |
| Sat 10/21 | Texas Tech at BYU | -3.0L14–27 | 49.0 | L14–27 | U | N |
| — Bye Week — | ||||||
| Thu 11/2 | Texas Tech vs TCU | -2.5W35–28 | 59.5 | W35–28 | O | Y |
| Sat 11/11 | Texas Tech at Kansas | +3.5W16–13 | 61.5 | W16–13 | U | Y |
| Sat 11/18 | Texas Tech vs UCF | -2.0W24–23 | 59.0 | W24–23 | U | N |
| Fri 11/24 | Texas Tech at Texas | +16.5L7–57 | 53.5 | L7–57 | O | N |
| Sat 12/16 | Texas Tech vs California | -3.5W34–14 | 54.5 | W34–14 | U | Y |
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
Split
Metrics disagree
Elite · 82.4% ATS
PPA + PPO + Havoc
Split
Metrics disagree
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ TCU
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?
TCU Edge
TCU +0.43
CSS Edge (season-to-date)
Teams with this edge win 61.3% of games historically
Based on 7 games this season
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
TCU Edge
TCU +11.3
GC Edge (season-to-date)
Teams with this edge win 64.9% of games historically
Based on 8 games this season
Actual Result
CSS Battle
Texas Tech
3 — 1 sequences
✗ Predicted incorrectly
GC Battle
Texas Tech
62.5 — 13.8 GC score
✗ Predicted incorrectly
Game Result
Texas Tech won by 7
✗ Model missed it
Spread Context
ATS Historical Context
Based on 2021–2025 backtest · FBS vs FBS · Regular season
Both metrics agree on TCU. Teams with this edge profile have covered 50.3% historically — essentially a coin flip against the spread.
ATS data is informational only. Past cover rates do not guarantee future results.
Coaching Matchup
TCU
Sonny Dykes #1
15–3 (83%)
· Yr 2 at school
OC
Kendal Briles
Yr 1
#1
DC
Joe Gillespie
Yr 1
#1
Texas Tech
Joey McGuire #1
9–7 (56%)
· Yr 2 at school
OC
Zach Kittley
Yr 2
#1
DC
Tim DeRuyter
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 ✓