California vs. UCLA: Final Score & Recap
Final score, recap & advanced stats
CALTeam statsUCLA
- Q4 3:42CALMateen Bhaghani 20 Yd Field Goal 33–7
- Q4 13:35CALJeremiah Hunter 13 Yd pass from Fernando Mendoza (Mateen Bhaghani Kick)30–7
- Q4 14:56CALMateen Bhaghani 32 Yd Field Goal 23–7
- Q2 0:10CALJeremiah Hunter 14 Yd pass from Fernando Mendoza (Mateen Bhaghani Kick)21–7
- Q2 5:27CALJaydn Ott 100 Yd Kickoff Return (Mateen Bhaghani Kick)13–7
- Q2 5:43UCLALogan Loya 5 Yd pass from Dante Moore (Blake Glessner Kick)6–7
- Q1 0:37CALMateen Bhaghani 36 Yd Field Goal 6–0
- Q1 5:25CALMateen Bhaghani 43 Yd Field Goal 3–0
How the model called it
- PassFernando Mendoza 19/30, 178 YDS, 2 TD, 2 INT
- RushJaydn Ott 21 CAR, 80 YDS
- RecJeremiah Hunter 8 REC, 101 YDS, 2 TD
- PassDante Moore 23/38, 266 YDS, 1 TD, 2 INT
- RushCarson Steele 12 CAR, 53 YDS
- RecLogan Loya 9 REC, 88 YDS, 1 TD
This exact spot, historically
empirical, no ratingsCAL up 13 entering the 4th quarter. Across 1,178 historically comparable game states (within ±2 pts and ±3 min, from 3,056 games):
- Q2 0:10CAL +22%
Fernando Mendoza pass complete to Jeremiah Hunter for 14 yds for a TD (Mateen Bhaghani KICK)
- Q2 5:27CAL +21%
Jaydn Ott 100 Yd Kickoff Return (Mateen Bhaghani Kick)
- Q2 5:43UCLA +20%
Dante Moore pass complete to Logan Loya for 5 yds for a TD (Blake Glessner KICK)
- Q1 0:37CAL +9%
Mateen Bhaghani 36 yd FG GOOD
- Q3 2:06CAL +8%
California Penalty, False Start (-5 Yards) to the UCLA 26
- Q1 5:25CAL +8%
Mateen Bhaghani 43 yd FG GOOD
FAQ
What was the final score of California vs. UCLA?
California 33, UCLA 7.
Did Gridpex's model pick hit?
No — the model picked UCLA, which didn't hit. We report the misses too.
| CAL | UCLA | |
|---|---|---|
| 302 | Total yards | 379 |
| 178 | Passing yards | 309 |
| 124 | Rushing yards | 70 |
| 17 | First downs | 23 |
| 6-15 | 3rd down | 8-19 |
| 1-1 | 4th down | 3-6 |
| 19/30 | Comp/Att | 30/47 |
| 5.9 | Yards per pass | 6.6 |
| 3.9 | Yards per rush | 1.9 |
| 2 | Turnovers | 4 |
| 5-45 | Penalties | 5-41 |
| 30:01 | Possession | 29:59 |
Costliest call
Q2 6:58 UCLA field goal on 4th & 2 at CAL 5. The model preferred go for it, a gap of 0.71 points.
Drive chart
every possession, start to finish- Q4 3:42CALMateen Bhaghani 20 Yd Field Goal 33–7
- Q4 13:35CALJeremiah Hunter 13 Yd pass from Fernando Mendoza (Mateen Bhaghani Kick)30–7
- Q4 14:56CALMateen Bhaghani 32 Yd Field Goal 23–7
- Q2 0:10CALJeremiah Hunter 14 Yd pass from Fernando Mendoza (Mateen Bhaghani Kick)21–7
- Q2 5:27CALJaydn Ott 100 Yd Kickoff Return (Mateen Bhaghani Kick)13–7
- Q2 5:43UCLALogan Loya 5 Yd pass from Dante Moore (Blake Glessner Kick)6–7
- Q1 0:37CALMateen Bhaghani 36 Yd Field Goal 6–0
- Q1 5:25CALMateen Bhaghani 43 Yd Field Goal 3–0
Model prediction
how the number is builtOur model simulates the game drive by drive from each side's opponent-adjusted efficiency (UCLA Elo 1667, CAL Elo 1431 for reference), with home-field advantage. That projects UCLA -11.8 (79% to win) — 2.3 points clear of UCLA's market line of -9.5. 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 profiles#10 of 117 in 2023 by biggest miss · #35 of 127 in 2023 by longest odds.
The 2023 upset archive →UCLA leads 58–35–1 · 31% have been one-possession games
| CAL | UCLA | |
|---|---|---|
| -0.6 (#64) | CORE overall | +9.3 (#34) |
| -1 / +0 | offense / defense | -5 / -15 |
| 2.20 | points / drive | 1.92 |
| 2.53 | allowed / drive | 1.43 |
| 29% | three-and-outs | 24% |
| +2 | pass over expected | +1 |
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 →