Park factor
94 total idx
Oracle Park run environment
Park factor boosts run scoring, so contact quality matters more tonight here.
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San Diego Padres at San Francisco Giants
SD
SD
43-37
PregameSun, 7:20 PM EDT
SF
SF
33-48
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 + marketOracle Park · San Francisco, California
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
SeriesSeries tied 1-1
TypePreseason 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.
49.5%
ensemble · SD 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 SD.
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.
127
projections · 77 high confidence
Strikeouts
- Landen RouppSF5.9± 1.8medium
- Logan WebbSF5.5± 1.5low
- Tyler MahleSF5.2± 2.1low
Hits
- Casey SchmittSF1.4± 1.0high
- Samad TaylorSD1.3± 1.2medium
- Fernando Tatis Jr.SD1.2± 1.0high
Total bases
- Jung Hoo LeeSF2.2± 1.8high
- Casey SchmittSF2.1± 2.3high
- Ty FranceSD1.9± 3.4high
RBIs
- Ty FranceSD0.9± 1.7high
- Manny MachadoSD0.7± 1.6high
- Rafael DeversSF0.6± 0.5high
Earned runs
- Griffin CanningSD3.5± 3.0low
- Tyler MahleSF3.1± 4.0low
- Randy VasquezSD2.8± 2.1medium
Projections recompute every 30 minutes · prop lines plug in once sportsbook ingest lands
Matchup · 2026
Team rate stats vs league
MLB Stats API
SD
league avg
SF
.661
OPS
.718
▶.727
.296
OBP
.319
▶.309
3.95
Runs / G
4.50
▶4.06
3.82
Team ERA
◀
4.18
4.40
1.29
WHIP
◀
1.31
1.38
8.4
K / 9
◀
8.5
8.0
Data via ESPN · MLB Stats API · Baseball Savant