San Diego Padres vs San Francisco Giants: MLB Prediction, Pick & Odds — Sep 12, 2026

42-37

PregameSat, 4:05 PM EDT

33-47

Verdict

Pass · no edge tonight.

The model doesn't see daylight against the posted line on this game. We don't surface negative-EV picks; check the drill-down for sub-model context.

Why this matchup

1 signal · model + market

Park factor

94 total idx

Oracle Park run environment

Park factor boosts run scoring, so contact quality matters more tonight here.

Oracle Park · San Francisco, California

Drill down

Sub-model tables · ensemble breakdown · last meeting · book shop · player props

Pregame · scouting report

The matchup, end-to-end.

Probable starters, projected lineups, head-to-head this season, our model edge against the market — every angle on one surface.

Away starter

TBD

Probable starter not yet announced. ESPN typically posts the card 12–24 hours before first pitch.

Home starter

TBD

Probable starter not yet announced. ESPN typically posts the card 12–24 hours before first pitch.

Vegas line

Lines post once a sportsbook releases them.

Model edge

Our model rates this game once both starters are confirmed.

Head to head

SeriesSeries tied 1-1

TypePreseason Series

Model ensemble · how the prediction is built

3 sub-models, blended.

Each sub-model uses a different rating substrate. Bayesian model averaging weights them by rolling Brier score so the ensemble inherits each model's strengths. Disagreement flags games where the sub-models don't see eye-to-eye — lower confidence, wider band.

49.5%

ensemble · SD favored

Elo Static
fallback · inputs missing
50.0%
P(SF win)
34%
weight
Elo Pitching
fallback · inputs missing
50.0%
P(SF win)
32%
weight
Bullpen Park
fallback · inputs missing
50.0%
P(SF win)
34%
weight

Disagreement

0.00 pp

weighted σ across sub-models

Confidence

100% · high

maps from disagreement

Substrate count

0 / 3 active

ones with full inputs tonight

Weights recalibrated nightly on a 90-day rolling window with strict point-in-time correctness — no model gets credit for a game it hasn't seen. Headline % is Platt-scaled per league; sub-model rows show raw BMA inputs.

Player projections

SF vs SD.

Per-player stat projections built from a recency-weighted blend of the last ten games, season average, and matchup context. Confidence reflects sample size and stability — the top of each list is who to watch.

130

projections · 80 high confidence

Strikeouts

Landen RouppSF
5.9± 1.8medium
Logan WebbSF
5.5± 1.5low
Tyler MahleSF
5.2± 2.1low

Hits

Samad TaylorSD
1.5± 1.2medium
Casey SchmittSF
1.5± 0.8high
Fernando Tatis Jr.SD
1.2± 1.0high

Total bases

Casey SchmittSF
2.2± 2.2high
Jung Hoo LeeSF
2.2± 1.8high
Rafael DeversSF
2.0± 2.0high

RBIs

Manny MachadoSD
0.7± 1.6high
Ty FranceSD
0.7± 1.6high
Samad TaylorSD
0.6± 1.2medium

Earned runs

Griffin CanningSD
3.5± 3.0low
Tyler MahleSF
3.1± 4.0low
Adrian HouserSF
2.9± 2.7low

Projections recompute every 30 minutes · prop lines plug in once sportsbook ingest lands

Matchup · 2026

Team rate stats vs league

MLB Stats API

league avg

.658

OPS

.719

▶

.728

.294

OBP

.319

▶

.309

3.91

Runs / G

4.50

▶

4.10

3.85

Team ERA

◀

4.18

4.42

1.29

WHIP

◀

1.31

1.38

8.4

K / 9

◀

8.5

8.0

Data via ESPN · MLB Stats API · Baseball Savant

San Diego Padres at San Francisco Giants

The matchup, end-to-end.

3 sub-models, blended.

SF vs SD.

Team rate stats vs league