Arizona State vs. UCLA: Final Score & Recap
Final score, recap & advanced stats
ASUTeam statsUCLA
- Q4 3:07ASUCameron Skattebo 17 Yd Run (Dario Longhetto Kick)17–7
- Q4 9:47UCLALogan Loya 16 Yd pass from Collin Schlee (Blake Glessner Kick)10–7
- Q3 1:59ASUElijhah Badger 25 Yd pass from Cameron Skattebo (Dario Longhetto Kick)10–0
- Q2 7:54ASUDario Longhetto 29 Yd Field Goal 3–0
How the model called it
What the numbers say
- ASU played behind the sticks — third downs that long convert rarely, and UCLA did not face the same ones. 3rd & 7.8 vs 3rd & 5.0
- UCLA lived on explosives rather than sustaining drives — a profile that vanished once those chunk plays were tackled. 14% explosive on a 38% success rate
- PassTrenton Bourguet 19/34, 149 YDS, 1 INT
- RushCam Skattebo 12 CAR, 61 YDS, 1 TD
- RecElijhah Badger 12 REC, 116 YDS, 1 TD
- PassCollin Schlee 11/18, 117 YDS, 1 TD
- RushKeegan Jones 3 CAR, 51 YDS
- RecLogan Loya 3 REC, 39 YDS, 1 TD
This exact spot, historically
empirical, no ratingsASU up 10 entering the 4th quarter. Across 1,138 historically comparable game states (within ±2 pts and ±3 min, from 3,056 games):
- Q4 9:47UCLA +43%
Collin Schlee pass complete to Logan Loya for 16 yds for a TD (Blake Glessner KICK)
- Q4 3:07ASU +39%
Cameron Skattebo run for 17 yds for a TD (Dario Longhetto KICK)
- Q3 1:59ASU +30%
Cameron Skattebo pass complete to Elijhah Badger for 25 yds for a TD (Dario Longhetto KICK)
- Q4 6:40ASU +25%
UCLA Penalty, Personal Foul (Gabriel Murphy) to the ASU 45 for a 1ST down
- Q3 15:00UCLA +18%
Collin Schlee run for 4 yds to the ASU 41
- Q3 1:52ASU +16%
Collin Schlee pass complete to Carson Steele for 17 yds to the UCLA 41 for a 1ST down
FAQ
What was the final score of Arizona State vs. UCLA?
Arizona State 17, UCLA 7.
Did Gridpex's model pick hit?
No — the model picked UCLA, which didn't hit. We report the misses too.
| ASU | UCLA | |
|---|---|---|
| 250 | Total yards | 300 |
| 176 | Passing yards | 117 |
| 74 | Rushing yards | 183 |
| 21 | First downs | 15 |
| 4-15 | 3rd down | 3-11 |
| 1-2 | 4th down | 0-4 |
| 22/39 | Comp/Att | 11/21 |
| 4.5 | Yards per pass | 5.6 |
| 2.5 | Yards per rush | 4.9 |
| 1 | Turnovers | 1 |
| 7-46 | Penalties | 9-86 |
| 36:54 | Possession | 23:06 |
Costliest call
Q2 0:00 ASU punt on 4th & 1 at ASU 29. The model preferred go for it, a gap of 1.11 points.
Drive chart
every possession, start to finish- Q4 3:07ASUCameron Skattebo 17 Yd Run (Dario Longhetto Kick)17–7
- Q4 9:47UCLALogan Loya 16 Yd pass from Collin Schlee (Blake Glessner Kick)10–7
- Q3 1:59ASUElijhah Badger 25 Yd pass from Cameron Skattebo (Dario Longhetto Kick)10–0
- Q2 7:54ASUDario Longhetto 29 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 1662, ASU Elo 1313 for reference), with home-field advantage. That projects UCLA -16.4 (86% to win), essentially in line with the market.
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#35 of 117 in 2023 by biggest miss · #13 of 127 in 2023 by longest odds.
The 2023 upset archive →UCLA leads 23–16–1 · 53% have been one-possession games
| ASU | UCLA | |
|---|---|---|
| -12.7 (#103) | CORE overall | +9.3 (#34) |
| -8 / +4 | offense / defense | -5 / -15 |
| 1.44 | points / drive | 1.92 |
| 2.95 | allowed / drive | 1.43 |
| 32% | three-and-outs | 24% |
| +9 | 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 →