UCLA vs. San Diego State: Final Score & Recap
Final score, recap & advanced stats
UCLATeam statsSDSU
- Q3 2:05UCLACarsen Ryan 6 Yd pass from Dante Moore (RJ Lopez Kick)35–10
- Q2 1:20UCLALogan Loya 24 Yd pass from Dante Moore (RJ Lopez Kick)28–10
- Q2 5:03SDSUJack Browning 44 Yd Field Goal 21–10
- Q2 10:21UCLATJ Harden 59 Yd Run (RJ Lopez Kick)21–7
- Q2 14:56UCLACarson Steele 13 Yd Run (RJ Lopez Kick)14–7
- Q1 2:33SDSUMekhi Shaw 21 Yd pass from Jalen Mayden (Jack Browning Kick)7–7
- Q1 7:26UCLAJosiah Norwood 81 Yd pass from Dante Moore (RJ Lopez Kick)7–0
How the model called it
- PassDante Moore 17/27, 290 YDS, 3 TD
- RushTJ Harden 9 CAR, 91 YDS, 1 TD
- RecJosiah Norwood 2 REC, 87 YDS, 1 TD
- PassJalen Mayden 19/37, 196 YDS, 1 TD, 3 INT
- RushKenan Christon II 9 CAR, 27 YDS
- RecMekhi Shaw 5 REC, 54 YDS, 1 TD
This exact spot, historically
empirical, no ratingsUCLA up 25 entering the 4th quarter. Across 581 historically comparable game states (within ±2 pts and ±3 min, from 3,056 games):
- Q2 14:56UCLA +18%
Carson Steele run for 13 yds for a TD (RJ Lopez KICK)
- Q1 2:33SDSU +18%
Jalen Mayden pass complete to Mekhi Shaw for 21 yds for a TD (Jack Browning KICK)
- Q1 7:26UCLA +18%
Dante Moore pass complete to Josiah Norwood for 81 yds for a TD (RJ Lopez KICK)
- Q2 10:21UCLA +13%
TJ Harden run for 59 yds for a TD (RJ Lopez KICK)
- Q2 1:20UCLA +9%
Dante Moore pass complete to Logan Loya for 24 yds for a TD (RJ Lopez KICK)
- Q2 5:03SDSU +6%
Jack Browning 44 yd FG GOOD
FAQ
What was the final score of UCLA vs. San Diego State?
UCLA 35, San Diego State 10.
Did Gridpex's model pick hit?
Yes — the model's pick (UCLA) was correct.
| UCLA | SDSU | |
|---|---|---|
| 550 | Total yards | 259 |
| 296 | Passing yards | 196 |
| 254 | Rushing yards | 63 |
| 26 | First downs | 17 |
| 5-11 | 3rd down | 5-17 |
| 1-1 | 4th down | 0-1 |
| 18/29 | Comp/Att | 20/38 |
| 10.2 | Yards per pass | 5.2 |
| 6.5 | Yards per rush | 1.9 |
| 2 | Turnovers | 3 |
| 7-89 | Penalties | 4-40 |
| 25:05 | Possession | 34:55 |
Costliest call
Q4 12:55 SDSU go for it on 4th & 6 at UCLA 22. The model preferred field goal, a gap of 0.56 points.
Drive chart
every possession, start to finish- Q3 2:05UCLACarsen Ryan 6 Yd pass from Dante Moore (RJ Lopez Kick)35–10
- Q2 1:20UCLALogan Loya 24 Yd pass from Dante Moore (RJ Lopez Kick)28–10
- Q2 5:03SDSUJack Browning 44 Yd Field Goal 21–10
- Q2 10:21UCLATJ Harden 59 Yd Run (RJ Lopez Kick)21–7
- Q2 14:56UCLACarson Steele 13 Yd Run (RJ Lopez Kick)14–7
- Q1 2:33SDSUMekhi Shaw 21 Yd pass from Jalen Mayden (Jack Browning Kick)7–7
- Q1 7:26UCLAJosiah Norwood 81 Yd pass from Dante Moore (RJ Lopez Kick)7–0
Model prediction
how the number is builtOur model simulates the game drive by drive from each side's opponent-adjusted efficiency (SDSU Elo 1497, UCLA Elo 1631 for reference), with home-field advantage. That projects SDSU +3 (43% to win) — 10.5 points clear of SDSU's market line of +13.5. That is a disagreement, not a betting edge — sides we favour have not covered at better than breakeven.
Why that percentage is worth reading: across 604 graded in-season games, the calls this model put near 58% came in at 58.2%. The full calibration table is published, bin by bin. Early in the season the cold-start path runs instead and calibrates less well (Brier 0.2028 against 0.1889).
Model as of Sep 1 · through week 1 · drive simulation
Season form — 2023
nationally rankedThe matchup, in context
series history + adjusted profilesUCLA leads 18–1 · 11% have been one-possession games
| UCLA | SDSU | |
|---|---|---|
| +9.3 (#34) | CORE overall | -14.2 (#106) |
| -5 / -15 | offense / defense | -4 / +10 |
| 1.92 | points / drive | 1.57 |
| 1.43 | allowed / drive | 2.22 |
| 24% | three-and-outs | 25% |
| +1 | pass over expected | -4 |
Bold is the side ahead. CORE strips the situation from every play and solves the schedule out across the league, so these compare two teams that never met.
Key matchups
The analyst read: each team's offense splits crossed against the opponent's defense, with the edges called out.
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Where this number comes from. Every projection on this page is produced by the same model, run before kickoff on opponent-adjusted efficiency, power ratings and situational data — walk-forward validated on eleven seasons and never adjusted after the fact. How the model works →