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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Athletics at San Francisco Giants
ATH
ATH
36-37
PregameTue, 9:45 PM EDT
SF
SF
29-43
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
Aaron Civale
SP · #45
5-2
W-L
4.20
ERA
1.47
WHIP
39
K
55.2
IP
Last 3 starts
vsSEAL(5-2)4.0 IP7 ER2 K
@LAA-5.0 IP5 ER2 K
vsSFW(5-1)5.0 IP2 ER2 K
Home starter
Robbie Ray
SP · #38
4-6
W-L
4.42
ERA
1.39
WHIP
66
K
73.1
IP
Last 3 starts
vsWSH-5.2 IP5 ER3 K
@CHCW(4-6)5.0 IP0 ER4 K
@COL-4.0 IP1 ER6 K
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 wins series 2-0
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.
48.1%
ensemble · ATH 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 ATH.
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.
128
projections · 83 high confidence
Strikeouts
- Landen RouppSF5.8± 2.1low
- Logan WebbSF5.3± 1.8low
- Trevor McDonaldSF5.1± 2.0low
Hits
- Jung Hoo LeeSF1.6± 1.4high
- Bryce EldridgeSF1.4± 1.0high
- Luis ArraezSF1.4± 0.7high
Total bases
- Nick KurtzATH3.0± 3.3high
- Matt ChapmanSF2.6± 3.6high
- Zack GelofATH2.5± 2.0high
RBIs
- Matt ChapmanSF1.3± 2.4high
- Nick KurtzATH1.2± 1.6high
- Tyler SoderstromATH0.8± 1.3high
Earned runs
- Luis MoralesATH5.0± 0.3low
- Jeffrey SpringsATH3.2± 1.8medium
- Adrian HouserSF3.1± 2.7low
Projections recompute every 30 minutes · prop lines plug in once sportsbook ingest lands
Matchup · 2026
Team rate stats vs league
MLB Stats API
ATH
league avg
SF
.751
OPS
◀
.717
.725
.331
OBP
◀
.319
.309
4.60
Runs / G
◀
4.50
4.11
4.81
Team ERA
4.17
▶4.52
1.44
WHIP
1.31
▶1.40
8.2
K / 9
◀
8.5
8.1
Data via ESPN · MLB Stats API · Baseball Savant
