Preseason projection — This game has not yet been played and 2026 in-season data is not yet available.
Edges are based on 2025 full-season performance.
Confidence will increase once in-season games are logged.
Matchup Prediction
Toledo
has the edge in this matchup
Both Momentum Control (CSS) and Game Control metrics favor
Toledo entering this game.
Momentum Control
58.4%
Toledo wins
Lean
Game Control
76%
Toledo wins
Strong
Advanced Stats
All 4 factors agree → Toledo
· 83.1% ATS historically when all four align
↓ See full breakdown
Temple 2026 Schedule
Temple's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Temple vs Rhode Island | -19 | — | — | — | — |
| Sat 9/12 | Temple vs Penn State | +15.5 | — | — | — | — |
| Sat 9/19 | Temple at Toledo | +11 | — | — | — | — |
| Sat 9/26 | Temple vs Army | +2 | — | — | — | — |
| Sat 10/3 | Temple at South Florida | +14 | — | — | — | — |
| Sat 10/10 | Temple vs UConn | +3 | — | — | — | — |
| Sat 10/17 | Temple vs Charlotte | -20 | — | — | — | — |
| — Bye Week — | ||||||
| Sat 10/31 | Temple at East Carolina | +12 | — | — | — | — |
| Sat 11/7 | Temple at Navy | +9 | — | — | — | — |
| Sat 11/14 | Temple vs UAB | -14 | — | — | — | — |
| Thu 11/19 | Temple vs Rice | -14 | — | — | — | — |
| Fri 11/27 | Temple at Memphis | +12.5 | — | — | — | — |
Toledo 2026 Schedule
Toledo's 2026 Schedule
| Date | Matchup | Spread | Total | Result | O/U | Cover |
|---|---|---|---|---|---|---|
| Sat 9/5 | Toledo at Michigan State | -4 | — | — | — | — |
| Sat 9/12 | Toledo vs Central Connecticut | -27.5 | — | — | — | — |
| Sat 9/19 | Toledo vs Temple | -11 | — | — | — | — |
| Sat 9/26 | Toledo vs San Diego State | -1.5 | — | — | — | — |
| Sat 10/3 | Toledo at Ball State | -23.5 | — | — | — | — |
| Sat 10/10 | Toledo vs Buffalo | -15.5 | — | — | — | — |
| Sat 10/17 | Toledo at Eastern Michigan | -13 | — | — | — | — |
| Sat 10/24 | Toledo vs Western Michigan | -10 | — | — | — | — |
| — Bye Week — | ||||||
| Wed 11/4 | Toledo at Sacramento State | -6.5 | — | — | — | — |
| Wed 11/11 | Toledo vs Massachusetts | -32 | — | — | — | — |
| Wed 11/18 | Toledo vs Bowling Green | -17.5 | — | — | — | — |
| Fri 11/27 | Toledo at Ohio | -8.5 | — | — | — | — |
Advanced Stats
Advanced Analytics Matchup
Matchup-adjusted (offense vs opponent defense) ·
2025 season (prior year)
Agreement Signals — When All Metrics Agree
Elite · 83.1% ATS
PPA + PPO + SR + Havoc
All 4 Agree
→ Toledo
Elite · 82.4% ATS
PPA + PPO + Havoc
3 Agree
→ Toledo
Elite · 73.9% ATS
PPA + Success Rate
Both Agree
→ Toledo
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 · 2025 season (prior year — 2026 data not yet available) ·
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?
Toledo Edge
Toledo +0.79
CSS Edge (season-to-date)
Teams with this edge win 58.4% of games historically
Based on 2025 full season · preseason estimate
Game Control (GC)
Win Probability Dominance
Who controls games start to finish?
Toledo Edge
Toledo +24.6
GC Edge (season-to-date)
Teams with this edge win 76% of games historically
Based on 2025 full season · preseason estimate
Coaching Matchup
Temple
K. C. Keeler #36
5–7 (42%)
· Yr 2 at school
OC
Tyler Walker
Yr 2
#45
DC
Brian Smith
Yr 2
#135
Toledo
Mike Jacobs #77
0–0 (0%)
· Yr 1 at school
OC
Cris Reisert
Yr 1
#67
DC
Jahmal Brown
Yr 1
#68
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 ✓