Park factor
98 total idx
PNC Park run environment
Park factor boosts run scoring, so contact quality matters more tonight here.
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San Francisco Giants at Pittsburgh Pirates
SF
SF
32-46
PregameTue, 6:40 PM EDT
PIT
PIT
39-40
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 + marketPNC Park · Pittsburgh, Pennsylvania
Drill down
Sub-model tables · ensemble breakdown · last meeting · book shop · player props
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
SeriesSF leads series 2-1
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.
49.3%
ensemble · SF favored
Elo Static
fallback · inputs missing
50.0%
P(PIT win)
33%
weight
Elo Pitching
fallback · inputs missing
50.0%
P(PIT win)
32%
weight
Bullpen Park
fallback · inputs missing
50.0%
P(PIT win)
35%
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
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.
130
projections · 85 high confidence
Strikeouts
- Paul SkenesPIT7.3± 2.8medium
- Braxton AshcraftPIT6.5± 2.4medium
- Landen RouppSF5.9± 1.8medium
Hits
- Casey SchmittSF1.5± 0.8high
- Bryan ReynoldsPIT1.4± 1.0high
- Jung Hoo LeeSF1.2± 1.0high
Total bases
- Bryan ReynoldsPIT2.6± 3.1high
- Casey SchmittSF2.2± 2.2high
- Rafael DeversSF2.0± 2.0high
RBIs
- Bryan ReynoldsPIT0.9± 1.2high
- Ryan O'HearnPIT0.7± 1.9high
- Bryce EldridgeSF0.7± 1.2high
Earned runs
- Mitch KellerPIT3.4± 2.7medium
- Adrian HouserSF2.9± 2.7low
- Landen RouppSF2.8± 2.5medium
Projections recompute every 30 minutes · prop lines plug in once sportsbook ingest lands
Matchup · 2026
Team rate stats vs league
MLB Stats API
SF
league avg
PIT
.729
OPS
.719
▶.742
.310
OBP
.319
▶.334
4.10
Runs / G
4.50
▶4.97
4.40
Team ERA
4.18
▶4.16
1.38
WHIP
1.31
▶1.30
8.0
K / 9
8.5
▶9.2
Data via ESPN · MLB Stats API · Baseball Savant