Bill Connelly of ESPN just released his SP+ rankings for all 138 FBS teams heading into the 2026 season. He currently has the USC Trojans sitting ranked 13th.
From these metrics, we can predict what the potential spreads look like for the USC Trojans 2026 schedule.
How The Projected Spreads Are Calculated
SP+ is a predictive efficiency metric (points above/below average, adjusted for strength of schedule and other factors). The projected margin for a game is approximately:
Home team projected margin ≈ (Home team’s SP+ rating) – (Away team’s SP+ rating) + ~3–4 points home-field advantage
- These are preseason projections and will shift significantly as the 2026 season progresses with actual results, injuries, transfers, etc.
- The USC Trojans sit at No. 13 overall in the SP+ rankings with a rating of 16.8 (strong offense projected at 37.7, which ranks 6th nationally).
- Top teams mentioned: Ohio State (No. 1), Oregon (No. 2), Indiana (No. 5), Georgia (No. 4), Texas (No. 6). Other Big Ten teams like Michigan (No. 14, 16.1) are close to USC.
Note: Exact SP+ ratings for all 138 teams (especially lower-tier non-conference opponents like San Jose State, Fresno State, Louisiana, Rutgers, Wisconsin, Maryland, and UCLA) are not fully detailed, so spreads against those teams use reasonable estimates based on their typical historical SP+ ranges and conference positioning. Big Ten opponents have a more precise context from the rankings.
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2026 USC Trojans Projected Spreads
Here is the full schedule with projected spreads (USC Trojans perspective: negative = USC favored; positive = opponent favored). All times TBA.
| Date | Opponent | Location | Projected Spread | Notes / Expected Outcome |
|---|---|---|---|---|
| Aug 29 (Sat) | San Jose State | Home (Coliseum) | USC -38 to -45 | Massive favorite; non-conference tune-up |
| Sep 5 (Sat) | Fresno State | Home (Coliseum) | USC -28 to -35 | Strong favorite; former USC assistant is Head Coach |
| Sep 12 (Sat) | Louisiana (Ragin’ Cajuns) | Home (Coliseum) | USC -32 to -40 | Group of 5 opponent; comfortable home win expected |
| Sep 19 (Sat) | at Rutgers | Away | USC -10 to -18 | Road favorite but Big Ten road test |
| Sep 26 (Sat) | Oregon | Home (Coliseum) | Oregon -4 to -8 (USC +4 to +8) | Toss-up/highly competitive; top-2 vs. top-13 matchup |
| Oct 3 (Sat) | Washington (Homecoming) | Home (Coliseum) | USC -6 to -12 | Solid home favorite against ranked Huskies |
| Oct 10 (Sat) | at Penn State | Away | Penn State -6 to -12 (USC +6 to +12) | Tough road environment; Penn State typically strong |
| Oct 24 (Sat) | at Wisconsin | Away | USC -3 to -9 | Slight road favorite; Camp Randall can be tough |
| Oct 31 (Sat) | Ohio State | Home (Coliseum) | Ohio State -7 to -14 (USC +7 to +14) | Huge showdown; No. 1 Buckeyes visit LA (Halloween) |
| Nov 14 (Sat) | at Indiana | Away | Indiana -3 to -8 (USC +3 to +8) | Road underdog vs. surging No. 5 Hoosiers |
| Nov 21 (Sat) | Maryland | Home (Coliseum) | USC -14 to -20 | Favorable home matchup |
| Nov 28 (Sat) | at UCLA | Away (Rose Bowl) | USC -10 to -16 | Crosstown rivalry; Trojans favored in Brentwood |
Quick Analysis For USC Fans
- Easy stretch: The first three non-conference games plus Maryland should be winnable with double-digit margins.
- Toughest stretch: Late September through early November features Oregon (home), Washington (home), Penn State (away), Wisconsin (away), and Ohio State (home). This is a brutal gauntlet.
- Road challenges: Trips to Rutgers, Penn State, Wisconsin, Indiana, and UCLA will test depth and road performance.
- Overall outlook: With USC at No. 13 and a top-ranked recruiting class plus returning QB Jayden Maiava and offensive pieces, the Trojans project as a top-15 team capable of 8–10 wins if the defense improves. Upset potential exists at home against Oregon/Ohio State and on the road against Indiana/Penn State.
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These spreads are preseason estimates derived directly from the ESPN SP+ framework and will evolve once more data (preseason camps, Week 0 results, etc.) becomes available. SP+ is highly regarded for its accuracy in projecting margins over large samples.