San Francisco Giants vs Pittsburgh Pirates: MLB Prediction, Pick & Odds — Sep 1, 2026

57-81

PregameTue, 6:40 PM EDTMLB.TV

PIT

67-71

ATS/PIT -1.5O/U/7.5ML/PIT-167

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

2 signals · model + market

Park factor

98 total idx

PNC Park run environment

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

Recent form

5-5

SF +0.8 vs PIT -0.4 margin

SF brings the hotter last-ten margin into this matchup tonight with balance.

PNC Park · Pittsburgh, Pennsylvania

Line movement

200 snapshots

PIT spread

-1.5

open PK · -1.5

Total

O/U 7.5

open O/U 7.5

PIT no-vig %

59.3%

open 58.3% · +1.0

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

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

Logan Webb

SP · #62

8-8

W-L

3.72

ERA

1.10

WHIP

118

147.2

Last 3 starts

vsCIN-6.0 IP2 ER6 K

@BOSL(8-8)2.2 IP5 ER2 K

vsCOLW(8-7)6.0 IP1 ER7 K

Home starter

Paul Skenes

SP · #30

9-11

W-L

3.79

ERA

1.12

WHIP

175

145.0

Last 3 starts

@SD-6.0 IP0 ER4 K

vsDET-4.1 IP3 ER5 K

@MIAL(9-11)5.0 IP1 ER4 K

Vegas line

Spread-1.5

TotalO/U 7.5

Money+138 / -167

Model edge

Win prob39% / 61%

LeanHome · 11pp

ConfidenceEdge

Head to head

SeriesSF leads series 2-1

TypeRegular Season Series

Place a bet

Take PIT on the model side.

Our model leans this way; both DraftKings and FanDuel are open below.

Model side · moneyline

-167Bet PIT ML↗-167Bet PIT ML↗

Other markets

-1.5Bet the run line↗-1.5Bet the run line↗

O/U 7.5Bet the total↗O/U 7.5Bet the total↗

21+ in most states. Lines refreshed at the moment of fetch. We may earn a referral fee on book signups — your odds and terms are unchanged.

Scouting report · pre-game

PIT vs SF.

PIT is 5-5 recently; SF is 5-5.

60.8%

model · PIT win prob

Recent form

PIT5–5-0.4
SF5–5+0.8

Situational

PIT2d rest
SF1d restB2B
+1.0d differential

PIT has 1 more rest day. PIT has 9 straight road games; SF has 1.

Model angle

Weather and park remove about 0.2 runs from the total environment.

▼Weather + park-0.2pp

Players to watch

Paul SkenesPIT
6.3strikeouts · ±2.2
Paul Skenes · 6.3 K · last-10 6.1 vs season 6.5.
Braxton AshcraftPIT
5.4strikeouts · ±2.4
Braxton Ashcraft · 5.4 K · last-10 4.9 vs season 6.1.
Logan WebbSF
4.5strikeouts · ±2.3
Logan Webb · 4.5 K · last-10 4.2 vs season 4.9.

Line move

open -156→-163toward PIT

The market has moved 1pp toward the home side.

Aggregated from r107 anatomy · r109 CLV history · r112 calibration · r118 line moves · r124 player projections · refreshes every 5 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.

60.8%

ensemble · PIT favored

Elo Static
59.0%
P(PIT win)
34%
weight
Elo Pitching
58.9%
P(PIT win)
33%
weight
Bullpen Park
59.2%
P(PIT win)
34%
weight

Disagreement

0.12 pp

weighted σ across sub-models

Confidence

99% · 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.

Line shopping · 1 book

Where to bet this game.

Same model, different prices. The same moneyline can vary 25¢ across books — taking the worst price wipes out a real edge instantly. Star marks the best-paying book per side.

+1.5pp

best edge · PIT · DraftKings

no live price

PIT

★ best

-163DraftKings

edge +1.5pp · implied 59.3%

Book	SF	PIT	Best edge
DraftKings	+135	-163★	+1.5

Edges shown are model probability minus no-vig implied probability for that side at that book. Stars mark the highest-paying cell per side. Lines refresh every 60 seconds.

Player projections

PIT vs SF.

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.

126

projections · 101 high confidence

Strikeouts

Paul SkenesPIT
6.3± 2.2high
Jared JonesPIT
5.6± 2.1medium
Braxton AshcraftPIT
5.4± 2.4high

Hits

Bryce EldridgeSF
1.2± 1.2high
Rafael DeversSF
1.2± 0.8high
Nick GonzalesPIT
1.1± 0.9high

Total bases

Rafael DeversSF
2.3± 1.9high
Oneil CruzPIT
1.8± 1.6high
Bryce EldridgeSF
1.8± 1.9high

RBIs

Bryce EldridgeSF
0.8± 1.3high
Rafael DeversSF
0.8± 1.1high
Rafael Flores Jr.PIT
0.7± 1.1high

Earned runs

Logan WebbSF
2.8± 2.2high
Paul SkenesPIT
2.6± 2.2high
Landen RouppSF
2.6± 2.0high

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

PIT

.713

OPS

.718

▶

.733

.307

OBP

.318

▶

.329

4.11

Runs / G

4.50

▶

4.85

4.34

Team ERA

4.16

▶

4.19

1.35

WHIP

1.30

▶

1.31

7.7

K / 9

8.5

▶

9.3

The One modelEdge

Tue, Sep 1

Giants

76-92

vs60.8%PIT

PIT★

Pirates

72-79

SF 39.2%win prob60.8% PIT

PIT 1492 · SF 1452Box score→

Data via ESPN · MLB Stats API · Baseball Savant

San Francisco Giants at Pittsburgh Pirates

The matchup, end-to-end.

Take PIT on the model side.

PIT vs SF.

3 sub-models, blended.

Where to bet this game.

PIT vs SF.

Team rate stats vs league

Where to bet this game.

3 sub-models, blended.