Park factor
96 total idx
LoanDepot Park run environment
Park factor boosts run scoring, so contact quality matters more tonight here.
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San Francisco Giants at Miami Marlins
SF
SF
29-43
PregameSun, 1:40 PM EDT
MIA
MIA
36-38
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 + marketloanDepot park · Miami, Florida
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
Adrian Houser
SP · #12
2-6
W-L
5.54
ERA
1.54
WHIP
46
K
65.0
IP
Last 3 starts
vsWSHL(2-6)4.1 IP3 ER6 K
@MIL-4.1 IP2 ER5 K
@COLL(2-5)3.2 IP4 ER4 K
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.
48.1%
ensemble · SF favored
Elo Static
fallback · inputs missing
50.0%
P(MIA win)
33%
weight
Elo Pitching
fallback · inputs missing
50.0%
P(MIA win)
32%
weight
Bullpen Park
fallback · inputs missing
50.0%
P(MIA 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
MIA 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 · 86 high confidence
Strikeouts
- Max MeyerMIA6.6± 1.8medium
- Landen RouppSF5.8± 2.1low
- Logan WebbSF5.3± 1.8low
Hits
- Jung Hoo LeeSF1.6± 1.4high
- Otto LopezMIA1.5± 0.8high
- Bryce EldridgeSF1.4± 1.0high
Total bases
- Matt ChapmanSF2.6± 3.6high
- Bryce EldridgeSF2.4± 2.1high
- Otto LopezMIA2.1± 1.6high
RBIs
- Matt ChapmanSF1.3± 2.4high
- Joe MackMIA0.7± 1.0high
- Kyle StowersMIA0.7± 1.0high
Earned runs
- Sandy AlcantaraMIA3.3± 3.0medium
- Adrian HouserSF3.1± 2.7low
- Robbie RaySF2.8± 3.5low
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
MIA
.725
OPS
◀
.717
.701
.309
OBP
.319
▶.321
4.11
Runs / G
4.50
▶4.22
4.52
Team ERA
4.17
▶4.17
1.40
WHIP
1.31
▶1.26
8.1
K / 9
8.5
▶8.8
Data via ESPN · MLB Stats API · Baseball Savant
