Which side does The One Analytics' model favor in TOR at SF?

The model favors SF, based on a weighted edge across matchup factors. The pick is logged before the game and graded in public.

When is the pick logged, and can it change?

Every model pick is timestamped before the game starts and is never edited after the fact.

Toronto Blue Jays vs San Francisco Giants: Final Score & Box Score — Jul 8, 2026

FINAL+10TOR

TOR

44-49

100

38-54

Oracle Park · San Francisco, California

34,023 attended

Line movement

43 snapshots

SF spread

+1.5

open +1.5

Total

O/U 7.0

open O/U 7.0

SF no-vig %

46.9%

open 46.9%

Stepped lines reflect captured market snapshots from odds_snapshots. Spread sign convention: negative = SF favored. Live mode caps the in-game branch to the last 60 minutes.

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.

59.5%

ensemble · SF favored

Elo Static
59.7%
P(SF win)
33%
weight
Elo Pitching
57.9%
P(SF win)
32%
weight
Bullpen Park
59.9%
P(SF win)
34%
weight

Disagreement

0.89 pp

weighted σ across sub-models

Confidence

94% · high

maps from disagreement

Substrate count

3 / 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.

The receipts

Closing-line value.

0.00

CLV pp

Open price

+104

Close price

+104

Open no-vig

46.9%

Close no-vig

46.9%

Line barely moved · Graded: miss

How we read this game

Which side does The One Analytics' model favor in TOR at SF?: The model favors SF, based on a weighted edge across matchup factors. The pick is logged before the game and graded in public.
When is the pick logged, and can it change?: Every model pick is timestamped before the game starts and is never edited after the fact.

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.

125

projections · 97 high confidence

Strikeouts

Dylan CeaseTOR
8.5± 2.7high
Kevin GausmanTOR
5.4± 2.3high
Trey YesavageTOR
5.3± 3.0medium

Hits

Luis ArraezSF
1.4± 1.4high
Vladimir Guerrero Jr.TOR
1.2± 0.9high
Jung Hoo LeeSF
1.1± 0.7high

Total bases

Osleivis BasabeSF
2.0± 4.7low
Rafael DeversSF
1.9± 2.4high
George SpringerTOR
1.8± 1.7high

RBIs

Osleivis BasabeSF
1.3± 3.1low
George SpringerTOR
0.8± 1.2high
Daniel SusacSF
0.7± 1.5high

Earned runs

Max ScherzerTOR
3.7± 4.0low
Carson WhisenhuntSF
3.5± 2.6low
Kevin GausmanTOR
3.1± 2.1high

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

.684

OPS

.720

▶

.725

.305

OBP

.318

▶

.310

3.95

Runs / G

4.50

▶

4.20

4.09

Team ERA

◀

4.18

4.38

1.31

WHIP

◀

1.31

1.35

9.0

K / 9

◀

8.5

7.7

Postgame · final

The morning-after recap.

Line score, top performers, model verdict against Vegas, and how the closing line shaped up vs the actual outcome.

Final

TOR wins

TOR 10 · SF 0 (by 10)

Model verdict

✗ Missed

Picked SF +10pp

Against the spread

No spread

Line score

Inning by inning

Team	1	2	3	4	5	6	7	8	9	R
TOR	5	0	0	0	0	0	0	2	3	10
SF	0	0	0	0	0	0	0	0	0	0

Final play

“End of the 9th inning”

What's next

Tomorrow's slate is open.

See every model edge for tonight's remaining games and tomorrow's slate side-by-side, or jump straight to DraftKings & FanDuel for the full board.

MLB edges →Full slate →

Tomorrow's board↗Tomorrow's board↗

21+ · we may earn a referral fee · your odds unchanged.

Team pages

Toronto Blue JaysSan Francisco Giants

Elsewhere in the MLB

Data via ESPN · MLB Stats API · Baseball Savant

batting box score. Tap a column header to sort by that stat; tap again to reverse, a third time to clear. The team leader in each stat is highlighted. Players who did not play are listed at the bottom.
Player
E. Clement2B	.297	5	1	1	0	0	0	0
N. LukesRF	.289	4	2	2	0	0	1	0
V. Guerrero Jr.1B	.262	5	1	1	2	1	0	0
G. SpringerDH	.222	4	2	1	1	1	1	1
D. VarshoCF	.246	4	2	2	1	0	1	0
K. Okamoto3B	.237	5	2	2	4	1	0	1
A. KirkC	.185	4	0	1	0	0	1	0
J. ClaseLF	.250	3	0	0	0	0	0	1
A. GimenezSS	.234	4	0	1	1	0	0	0
M. StrawRF	.229	2	0	0	0	0	0	0
Team		40	10	11	9	3	4	3

Player

.297

.289

.262

.222

.246

.237

.185

.250

.234

.229

Team

pitching box score. Tap a column header to sort by that stat; tap again to reverse, a third time to clear. The team leader in each stat is highlighted. Players who did not play are listed at the bottom.
Player
D. CeaseSP	8.0	1	0	0	3	11	118
T. RogersRP	1.0	0	0	0	0	0	4
Team	9.0	1	0	0	3	11	122

Player

D. CeaseSP

8.0

118

T. RogersRP

1.0

Team

9.0

122

batting box score. Tap a column header to sort by that stat; tap again to reverse, a third time to clear. The team leader in each stat is highlighted. Players who did not play are listed at the bottom.
Player
H. RamosLF	.278	4	0	1	0	0	0	3
L. Arraez2B	.324	4	0	0	0	0	0	0
C. SchmittLF	.275	4	0	0	0	0	0	0
R. Devers1B	.244	3	0	0	0	0	1	0
J. LeeRF	.308	3	0	0	0	0	0	0
W. AdamesSS	.229	2	0	0	0	0	1	1
B. EldridgeDH	.269	3	0	0	0	0	0	2
D. GilbertCF	.240	2	0	0	0	0	1	2
E. HaaseC	.162	2	0	0	0	0	0	2
D. CavanaughC	.167	1	0	0	0	0	0	1
Team		28	0	1	0	0	3	11

pitching box score. Tap a column header to sort by that stat; tap again to reverse, a third time to clear. The team leader in each stat is highlighted. Players who did not play are listed at the bottom.
Player
L. WebbSP	7.0	5	5	5	2	2	106
S. BivensRP	1.0	3	2	0	1	1	31
R. WalkerRP	1.0	3	3	3	1	0	28
Team	9.0	11	10	8	4	3	165

Team

TOR

3 sub-models, blended.

Closing-line value.

SF vs TOR.

Team rate stats vs league

The morning-after recap.

Inning by inning

Tomorrow's slate is open.

Closing-line value.

Batters

Pitchers

Batters

Pitchers

SF vs TOR.

3 sub-models, blended.