Toronto Blue Jays vs San Francisco Giants: MLB Prediction, Pick & Odds — Jul 7, 2026

TOR

36-38

PregameTue, 9:45 PM EDT

31-43

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 starts 7/6

TypeRegular Season 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.

47.3%

ensemble · TOR favored

Elo Static
fallback · inputs missing
50.0%
P(SF win)
33%
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 TOR.

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.

128

projections · 82 high confidence

Strikeouts

Dylan CeaseTOR
8.4± 3.0low
Landen RouppSF
5.8± 2.1low
Kevin GausmanTOR
5.4± 2.4medium

Hits

Luis ArraezSF
1.4± 0.8high
Bryce EldridgeSF
1.3± 0.8high
Jung Hoo LeeSF
1.3± 1.3high

Total bases

Bryce EldridgeSF
2.4± 2.1high
Luis ArraezSF
2.2± 1.6high
Matt ChapmanSF
1.9± 3.4high

RBIs

Bryce EldridgeSF
0.8± 1.1high
Matt ChapmanSF
0.8± 1.1high
Kazuma OkamotoTOR
0.7± 0.7high

Earned runs

Adrian HouserSF
2.9± 2.7low
Simeon Woods RichardsonTOR
2.8± 3.3low
Landen RouppSF
2.7± 3.0low

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

Matchup · 2026

Team rate stats vs league

MLB Stats API

TOR

league avg

.702

OPS

.718

▶

.730

.313

OBP

◀

.319

.309

4.08

Runs / G

4.50

▶

4.19

4.02

Team ERA

◀

4.18

4.49

1.28

WHIP

◀

1.31

1.40

9.1

K / 9

◀

8.5

8.1

Data via ESPN · MLB Stats API · Baseball Savant

Toronto Blue Jays at San Francisco Giants

The matchup, end-to-end.

3 sub-models, blended.

SF vs TOR.

Team rate stats vs league

3 sub-models, blended.