UCLA vs. Michigan State: Final Score & Recap
Final score, recap & advanced stats
UCLATeam statsMSU
- Q4 13:09MSUNick Marsh 5 Yd pass from Alessio Milivojevic (Martin Connington PAT failed)38–13
- Q3 1:44UCLAJalen Berger 13 Yd pass from Nico Iamaleava (Mateen Bhaghani Kick)38–7
- Q3 3:55UCLAJaivian Thomas 1 Yd Run (Mateen Bhaghani Kick)31–7
- Q2 4:09UCLATitus Mokiao-Atimalala 12 Yd pass from Nico Iamaleava (Mateen Bhaghani Kick)24–7
- Q2 10:17UCLAJalen Berger 3 Yd pass from Nico Iamaleava (Mateen Bhaghani Kick)17–7
- Q1 1:39UCLAJalen Berger 16 Yd Run (Mateen Bhaghani Kick)10–7
- Q1 4:41UCLAMateen Bhaghani 47 Yd Field Goal 3–7
- Q1 9:07MSUAidan Chiles 2 Yd Run (Martin Connington Kick)0–7
How the model called it
What the numbers say
- UCLA played behind the sticks — third downs that long convert rarely, and MSU did not face the same ones. 3rd & 7.1 vs 3rd & 4.7
- PassNico Iamaleava 16/24, 180 YDS, 3 TD
- RushJalen Berger 12 CAR, 89 YDS, 1 TD
- RecMikey Matthews 2 REC, 46 YDS
- PassAlessio Milivojevic 8/18, 100 YDS, 1 TD
- RushMakhi Frazier 12 CAR, 58 YDS
- RecNick Marsh 7 REC, 77 YDS, 1 TD
This exact spot, historically
empirical, no ratingsUCLA up 31 entering the 4th quarter. Across 343 historically comparable game states (within ±2 pts and ±3 min, from 3,056 games):
- Q1 15:00MSU +17%
UCLA Penalty, false start (Garrett DiGiorgio) to the UCLA 20
- Q1 13:56MSU +7%
Aidan Chiles pass complete to Michael Masunas for 6 yds to the MSU 47
- Q3 3:55UCLA +6%
Jaivian Thomas run for 1 yd for a TD (Mateen Bhaghani KICK)
- Q2 15:00UCLA +6%
Nico Iamaleava pass incomplete to Kwazi Gilmer
FAQ
What was the final score of UCLA vs. Michigan State?
UCLA 38, Michigan State 13.
Did Gridpex's model pick hit?
No — the model picked MSU, which didn't hit. We report the misses too.
| UCLA | MSU | |
|---|---|---|
| 418 | Total yards | 253 |
| 180 | Passing yards | 166 |
| 238 | Rushing yards | 87 |
| 20 | First downs | 14 |
| 6-13 | 3rd down | 8-15 |
| 3-3 | 4th down | 0-4 |
| 16/24 | Comp/Att | 16/35 |
| 7.5 | Yards per pass | 4.7 |
| 5.5 | Yards per rush | 3.5 |
| 0 | Turnovers | 1 |
| 8-60 | Penalties | 2-14 |
| 36:24 | Possession | 23:36 |
Drive chart
every possession, start to finish- Q4 13:09MSUNick Marsh 5 Yd pass from Alessio Milivojevic (Martin Connington PAT failed)38–13
- Q3 1:44UCLAJalen Berger 13 Yd pass from Nico Iamaleava (Mateen Bhaghani Kick)38–7
- Q3 3:55UCLAJaivian Thomas 1 Yd Run (Mateen Bhaghani Kick)31–7
- Q2 4:09UCLATitus Mokiao-Atimalala 12 Yd pass from Nico Iamaleava (Mateen Bhaghani Kick)24–7
- Q2 10:17UCLAJalen Berger 3 Yd pass from Nico Iamaleava (Mateen Bhaghani Kick)17–7
- Q1 1:39UCLAJalen Berger 16 Yd Run (Mateen Bhaghani Kick)10–7
- Q1 4:41UCLAMateen Bhaghani 47 Yd Field Goal 3–7
- Q1 9:07MSUAidan Chiles 2 Yd Run (Martin Connington Kick)0–7
Model prediction
how the number is builtOur model simulates the game drive by drive from each side's opponent-adjusted efficiency (MSU Elo 1415, UCLA Elo 1373 for reference), with home-field advantage. That projects MSU -4.1 (62% to win) — 2.9 points clear of UCLA's market line of -7. 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 — 2025
nationally rankedThe matchup, in context
series history + adjusted profiles#15 of 112 in 2025 by biggest miss · #61 of 129 in 2025 by longest odds.
The 2025 upset archive →| UCLA | MSU | |
|---|---|---|
| -6.4 (#84) | CORE overall | -8.6 (#91) |
| -2 / +4 | offense / defense | -4 / +4 |
| 1.90 | points / drive | 1.95 |
| 3.43 | allowed / drive | 2.64 |
| 21% | three-and-outs | 26% |
| +1 | pass over expected | +2 |
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 →