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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Atlanta Braves at San Francisco Giants
ATL
ATL
46-25
PregameSat, 9:05 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
Grant Holmes
SP · #66
4-2
W-L
4.05
ERA
1.34
WHIP
59
K
66.2
IP
Last 3 starts
@CHW-3.2 IP3 ER2 K
vsTORW(4-2)6.0 IP2 ER4 K
@CIN-4.2 IP3 ER5 K
Home 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
Vegas line
Lines post once a sportsbook releases them.
Model edge
Our model rates this game once both starters are confirmed.
Head to head
No prior meetings logged this season yet.
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 · ATL 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 ATL.
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.
121
projections · 74 high confidence
Strikeouts
- Chris SaleATL7.4± 2.1low
- Landen RouppSF5.8± 2.1low
- Logan WebbSF5.3± 1.8low
Hits
- Jung Hoo LeeSF1.6± 1.4high
- Bryce EldridgeSF1.4± 1.0high
- Luis ArraezSF1.4± 0.7high
Total bases
- Matt ChapmanSF2.6± 3.6high
- Bryce EldridgeSF2.4± 2.1high
- Drake BaldwinATL2.2± 2.0high
RBIs
- Matt ChapmanSF1.3± 2.4high
- Drake BaldwinATL0.8± 0.8high
- Mauricio DubonATL0.7± 1.2high
Earned runs
- Adrian HouserSF3.1± 2.7low
- Robbie RaySF2.8± 3.5low
- Landen RouppSF2.7± 3.0low
Projections recompute every 30 minutes · prop lines plug in once sportsbook ingest lands
Matchup · 2026
Team rate stats vs league
MLB Stats API
ATL
league avg
SF
.744
OPS
◀
.717
.725
.320
OBP
◀
.319
.309
5.06
Runs / G
◀
4.50
4.11
3.34
Team ERA
◀
4.17
4.51
1.19
WHIP
◀
1.31
1.39
8.8
K / 9
◀
8.5
8.1
Data via ESPN · MLB Stats API · Baseball Savant
