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

The model favors PIT, 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.

San Francisco Giants vs Pittsburgh Pirates: MLB Prediction, Pick & Odds — Sep 2, 2026

57-82

PregameWed, 6:40 PM EDTMLB.TV

PIT

68-71

ATS/PIT -1.5O/U/8.5ML/PIT-156

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 8.5

open O/U 8.5

PIT no-vig %

58.3%

open 56.7% · +1.5

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

Landen Roupp

SP · #65

8-13

W-L

4.25

ERA

1.31

WHIP

133

139.2

Last 3 starts

vsARIW(8-13)5.0 IP1 ER3 K

@CLEL(7-13)5.1 IP3 ER2 K

vsCOLL(7-12)5.2 IP4 ER5 K

Home starter

Jared Jones

SP · #17

2-6

W-L

4.83

ERA

1.28

WHIP

72.2

Last 3 starts

@STLL(2-6)4.2 IP3 ER6 K

@LAD-4.0 IP2 ER5 K

vsBOSL(2-5)5.0 IP1 ER7 K

Vegas line

Spread-1.5

TotalO/U 8.5

Money+129 / -156

Model edge

Win prob39% / 61%

LeanHome · 11pp

ConfidenceEdge

Head to head

SeriesPIT leads series 1-0

TypeCurrent 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

-156Bet PIT ML↗-156Bet PIT ML↗

Other markets

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

O/U 8.5Bet the total↗O/U 8.5Bet 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 enters on a 3-game win streak; SF is 5-5 over its last 10.

61.3%

model · PIT win prob

Recent form

PIT5–5-0.4
SF5–5+0.8

Situational

PIT1d restB2B
SF1d restB2B
+0.0d differential

Rest is even. PIT has 0 straight road games; SF has 2.

Model angle

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

▼Weather + park-0.2pp

Players to watch

Paul SkenesPIT
6.2strikeouts · ±2.3
Paul Skenes · 6.2 K · last-10 6 vs season 6.4.
Braxton AshcraftPIT
5.4strikeouts · ±2.4
Braxton Ashcraft · 5.4 K · last-10 4.9 vs season 6.1.
Bubba ChandlerPIT
4.4strikeouts · ±1.9
Bubba Chandler · 4.4 K · last-10 4.4 vs season 4.5.

Head-to-head · 1-0 · last 1

PIT is 1-0 in the recent head-to-head sample.

2026-09-01SF @ PIT12–13

Line move

open -146→-156toward PIT

The market has moved 1.6pp 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.

61.2%

ensemble · PIT favored

Elo Static
59.9%
P(PIT win)
34%
weight
Elo Pitching
59.2%
P(PIT win)
33%
weight
Bullpen Park
59.8%
P(PIT win)
34%
weight

Disagreement

0.31 pp

weighted σ across sub-models

Confidence

98% · 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.

+1.51

CLV pp · steam

Open price

-146

Close price

-156

Open no-vig

56.7%

Close no-vig

58.3%

Market steamed in our direction

How we read this game

Which side does The One Analytics' model favor in SF at PIT?: The model favors PIT, 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.

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.

+3.0pp

best edge · PIT · DraftKings

no live price

PIT

★ best

-156DraftKings

edge +3.0pp · implied 58.3%

Book	SF	PIT	Best edge
DraftKings	+129	-156★	+3.0

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
6.2± 2.3high
Jared JonesPIT
5.6± 2.1medium
Braxton AshcraftPIT
5.4± 2.4high

Hits

Bryce EldridgeSF
1.3± 1.1high
Nick GonzalesPIT
1.2± 1.1high
Rafael DeversSF
1.2± 0.7high

Total bases

Rafael DeversSF
2.5± 1.7high
Brandon LowePIT
2.1± 2.4high
Oneil CruzPIT
2.0± 1.7high

RBIs

Rafael DeversSF
1.0± 1.2high
Oneil CruzPIT
0.8± 1.4high
Bryce EldridgeSF
0.8± 1.3high

Earned runs

Logan WebbSF
3.0± 2.7high
Paul SkenesPIT
2.6± 1.8high
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

.716

OPS

.719

▶

.736

.309

OBP

.319

▶

.331

4.17

Runs / G

4.50

▶

4.91

4.39

Team ERA

4.17

▶

4.24

1.36

WHIP

1.31

▶

1.32

7.7

K / 9

8.5

▶

9.3

The One modelEdge

Wed, Sep 2

Giants

76-93

vs61.3%PIT

PIT★

Pirates

73-79

SF 38.7%win prob61.3% PIT

PIT 1495 · SF 1449Box score→

Team pages

San Francisco GiantsPittsburgh Pirates

Elsewhere in the MLB

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.

Closing-line value.

Where to bet this game.

PIT vs SF.

Team rate stats vs league

PIT vs SF.

3 sub-models, blended.

Where to bet this game.

Closing-line value.