San Diego vs. Cal Poly: Final Score & Recap
Final score, recap & advanced stats
San Diego
Cal Poly
▸Final
Alex G. Spanos Stadium
SAN Team statsCAL
241Total yards422
145Pass326
96Rush96
11First downs22
4-163rd down3-10
1Turnovers2
34:16Possession25:44
- Q4 2:26CAL Mark Biggins 18 Yd Run (Noah Serna Kick)10–27
- Q3 4:16CAL Noah Serna 45 Yd Field Goal 10–20
- Q3 6:37SAN Ruben Lopez 48 Yd Interception Return (Aidan Lehman Kick)10–17
- Q2 2:24CAL Tyrece Fairly-Diyem 1 Yd pass from Sam Huard (Noah Serna Kick)3–17
- Q1 0:27CAL Troy Fletcher 30 Yd Run (Noah Serna Kick)3–10
- Q1 1:44SAN Aidan Lehman 38 Yd Field Goal 3–3
- Q1 7:32CAL Noah Serna 41 Yd Field Goal 0–3
What the numbers say
- SAN played behind the sticks — third downs that long convert rarely, and CAL did not face the same ones. 3rd & 10.8 vs 3rd & 8.0
Kickoff155 plays →Final
Tap or hover on the chart to see play details
CAL (top)SAN (bottom)· colored dots = scoresFinal: CAL 100%
San Diego
- PassDom Nankil 15/27, 145 YDS
- RushIsaiah Williams 22 CAR, 82 YDS
- RecJa'seem Reed 5 REC, 69 YDS
Cal Poly
- PassSam Huard 24/42, 326 YDS, 1 TD, 1 INT
- RushTroy Fletcher 8 CAR, 52 YDS, 1 TD
- RecTyrece Fairly-Diyem 7 REC, 129 YDS, 1 TD
This exact spot, historically
empirical, no ratingsCAL up 10 entering the 4th quarter. Across 1,137 historically comparable game states (within ±2 pts and ±3 min, from 3,056 games):
88%
CAL went on to win
A near-lock historically
FAQ
What was the final score of San Diego vs. Cal Poly?
San Diego 10, Cal Poly 27.
Did Gridpex's model pick hit?
Yes — the model's pick (CAL ) was correct.
SAN
CAL
24%
Success rate
41%
-0.19
EPA / pass
0.29
0.26
EPA / rush
-0.49
23%
Early-down success
39%
20%
Pass success
45%
30%
Rush success
33%
7%
Explosive play rate (15+ yds)
21%
26%
Stuffed rush rate
25%
56%
Pass rate
67%
10.8
Avg 3rd-down distance
8.0
SAN
10.8 yards to go on 3rd down
62 scrimmage snaps
CAL
8.0 yards to go on 3rd down
73 scrimmage snaps
| SAN | CAL | |
|---|---|---|
| 241 | Total yards | 422 |
| 145 | Passing yards | 326 |
| 96 | Rushing yards | 96 |
| 11 | First downs | 22 |
| 4-16 | 3rd down | 3-10 |
| 1-3 | 4th down | 0-1 |
| 15/27 | Comp/Att | 24/42 |
| 5.4 | Yards per pass | 7.8 |
| 2.8 | Yards per rush | 3.6 |
| 1 | Turnovers | 2 |
| 11-95 | Penalties | 4-25 |
| 34:16 | Possession | 25:44 |
SAN
9/11 with the model
−0.9 pts · −0.0% win prob
CAL
5/6 with the model
−0.4 pts · −0.0% win prob
Costliest call
Q4 13:09 SAN punt on 4th & 2 at SAN 28. The model preferred go for it, a gap of 0.48 points.
Drive chart
every possession, start to finishSAN 1/12 scoring drives← own end zone →CAL 6/13 scoring drives
CAL END1 pl, -1 yds· own 39Q0
SAN PUNT4 pl, 5 yds· opp 48Q1
CAL FG10 pl, 66 yds· own 20Q1
SAN FG10 pl, 64 yds· own 29Q1
CAL TD3 pl, 72 yds· own 28Q1
SAN PUNT5 pl, 12 yds· own 30Q1
CAL DOWNS11 pl, 71 yds· own 18Q2
SAN PUNT7 pl, 38 yds· own 11Q2
CAL TD3 pl, 90 yds· own 10Q2
SAN PUNT3 pl, 6 yds· own 23Q2
CAL PUNT7 pl, 35 yds· own 25Q2
SAN END1 pl, 6 yds· own 10Q2
CAL PUNT4 pl, 13 yds· own 30Q3
SAN PUNT3 pl, 5 yds· own 22Q3
CAL TD6 pl, 15 yds· own 32Q3
CAL MISS7 pl, 44 yds· own 34Q3
SAN FUM4 pl, -8 yds· own 22Q3
CAL FG4 pl, -4 yds· opp 14Q3
SAN PUNT3 pl, 1 yds· own 15Q3
CAL FUM6 pl, 30 yds· opp 30Q3
SAN PUNT3 pl, 8 yds· own 20Q4
CAL PUNT3 pl, -2 yds· own 25Q4
SAN DOWNS11 pl, 35 yds· own 34Q4
CAL TD7 pl, 69 yds· own 31Q4
SAN Uncateg8 pl, 27 yds· own 34Q4
- Q4 2:26CAL Mark Biggins 18 Yd Run (Noah Serna Kick)10–27
- Q3 4:16CAL Noah Serna 45 Yd Field Goal 10–20
- Q3 6:37SAN Ruben Lopez 48 Yd Interception Return (Aidan Lehman Kick)10–17
- Q2 2:24CAL Tyrece Fairly-Diyem 1 Yd pass from Sam Huard (Noah Serna Kick)3–17
- Q1 0:27CAL Troy Fletcher 30 Yd Run (Noah Serna Kick)3–10
- Q1 1:44SAN Aidan Lehman 38 Yd Field Goal 3–3
- Q1 7:32CAL Noah Serna 41 Yd Field Goal 0–3
Model prediction
how the number is builtOne or both teams lack a power rating (often an FCS opponent), so the model prediction is low-confidence for this game.
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 →