SF at STL
San Francisco Giants visit St. Louis Cardinals at Busch Stadium.
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SF @ STL
Model simulationprecomputedRuns10,000Generated picks8
Live game state
Live betaThe left side is what the live source reports right now. The right side is the GameTime forecast made before first pitch — it is frozen and does not change while the game is played.
Live now
Checking the live feed…
Pregame GameTime · frozen
- away runs
- 3 (0–6)
- home runs
- 3 (1–7)
Forecast frozen at Sep 16, 11:27 AM ET. It does not change during the game.
Simulation completePrecomputed for this game
San Francisco Giants @ St. Louis Cardinals
Model mlb-2026.07Runs 10,000generated today
SF @ STL: 36 player-prop markets simulated over 10000 deterministic iterations each.
10,000 game simulationsComplete inputs
Simulated 11:27 AM ET · pregameSan Francisco Giantsvs
St. Louis CardinalsProjected scoreSF 3 – 3 STLSTL 55% · from 10,000 simulated games
45%Win chance55%
3.1Projected runs3.3
0–6Likely range1–7
40%Sportsbook60%
Close call: the simulated means differ by 0.2 runs and the win probabilities sit near even. Team strength enters only through each club's player projections; no separate home-field advantage term is applied.
GameTimePicks simulation readfrom 10,000 simulated games · not validated to out-predict the market
St. Louis Cardinals
STL 3 – SF 3
Median simulation score
MoneylineSTL55% simulationsLEAN
TotalUnavailablePaused · its live record is below a coin flip
Run lineSF +1.566% coverSTRONG
Largest simulated player outcomes
Direction from simulated probability across 10,000 games · legacy prop engine · not a bet
Christian KossSF vs STL
OVER 0.5 Hits + Runs + RBIs82%8,230 / 10,000 games
Bryce EldridgeSF vs STL
OVER 0.5 Hits79%7,870 / 10,000 games
Christian KossSF vs STL
OVER 0.5 Hits73%7,320 / 10,000 games
Brett HarrisSF vs STL
UNDER 0.5 Hits68%6,760 / 10,000 games
Anthony MolinaSF vs STL
OVER 3.5 Strikeouts61%6,110 / 10,000 games
Simulation story
8 chapters · read from the published artifact› Loading the matchup model› Reading the outcome distribution from 10,000 simulated games› Preparing the score scenariosPress Play to step through what the model expects, chapter by chapter, or read the report below.
Evidence
Win probability · 10,000 simulated games
SF 45%55% STL
Simulation outcomes · 10,000 complete games
SF wins4,530of 10,000 games
STL wins5,470of 10,000 games
Extra innings1,240go past nine
Most likely final scores
SF 1 – 2 STL5%
SF 2 – 3 STL4%
SF 3 – 4 STL4%
SF 3 – 2 STL4%
SF 2 – 1 STL3%
SF 0 – 1 STL3%
Expected runs (mean)3.1–3.3SF–STL
SF range0–6p10–p90 runs
STL range1–7p10–p90 runs
Total runs6p10–p90 2–11
Run line (from simulated margins)STL −1.5 covers 34% of the time
Extra innings12% of games go past nine
Total-runs distribution
04812162021+
Simulation story
STL wins 55% of simulations.
Most common outcome: SF 1 – STL 2 (490 / 10,000 simulations).
35% of simulations finish within one run.
Biggest player factor: Christian Koss OVER 0.5 Hits + Runs + RBIs — 8,230 / 10,000 simulations.
Our simulation
Market snapshot (the book)
STL win55%
market60%
Total (median vs line)6
market8
STL −1.5 cover34%
market41%
RelationshipMODEL LOWER
The market column is the de-vigged sportsbook price, shown for comparison. It is never an input to our simulation, and we do not claim to beat it.
Paper-only · educational · not betting advice · same model output for every user · model mlb-2026.07