DET at TOR
Detroit Tigers visit Toronto Blue Jays at Rogers Centre.
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DET @ TOR
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.
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Pregame GameTime · frozen
No GameTime pregame forecast for this game.
Simulation completePrecomputed for this game
Detroit Tigers @ Toronto Blue Jays
Model mlb-2026.07Runs 10,000generated today
DET @ TOR: 43 player-prop markets simulated over 10000 deterministic iterations each.
10,000 game simulationsDegraded inputs
Simulated 11:27 AM ET · pregameDetroit Tigersvs
Toronto Blue JaysProjected scoreDET 4 – 5 TORTOR 54% · from 10,000 simulated games
46%Win chance54%
4.4Projected runs4.6
1–8Likely range1–9
47%Sportsbook53%
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
Toronto Blue Jays
TOR 4 – DET 4
Median simulation score
MoneylineTOR54% simulationsLEAN
TotalUnavailablePaused · its live record is below a coin flip
Run lineDET +1.564% coverSTRONG
Largest simulated player outcomes
Direction from simulated probability across 10,000 games · legacy prop engine · not a bet
Riley GreeneDET vs TOR
OVER 0.5 Hits85%8,450 / 10,000 games
Spencer TorkelsonDET vs TOR
OVER 0.5 Hits + Runs + RBIs77%7,710 / 10,000 games
Brett BatemanTOR vs DET
OVER 1.5 Hits + Runs + RBIs70%7,020 / 10,000 games
Kevin McGonigleDET vs TOR
OVER 1.5 Hits + Runs + RBIs67%6,740 / 10,000 games
Kazuma OkamotoTOR vs DET
OVER 1.5 Hits + Runs + RBIs67%6,680 / 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
DET 46%54% TOR
Simulation outcomes · 10,000 complete games
DET wins4,580of 10,000 games
TOR wins5,420of 10,000 games
Extra innings990go past nine
Most likely final scores
DET 3 – 4 TOR4%
DET 4 – 5 TOR3%
DET 2 – 3 TOR3%
DET 4 – 3 TOR2%
DET 5 – 6 TOR2%
DET 1 – 2 TOR2%
Expected runs (mean)4.4–4.6DET–TOR
DET range1–8p10–p90 runs
TOR range1–9p10–p90 runs
Total runs9p10–p90 4–15
Run line (from simulated margins)TOR −1.5 covers 36% of the time
Extra innings10% of games go past nine
Total-runs distribution
04812162021+
Simulation story
TOR wins 54% of simulations.
Most common outcome: DET 3 – TOR 4 (350 / 10,000 simulations).
30% of simulations finish within one run.
Biggest player factor: Riley Greene OVER 0.5 Hits — 8,450 / 10,000 simulations.
Our simulation
Market snapshot (the book)
TOR win54%
market53%
Total (median vs line)9
market8.5
TOR −1.5 cover36%
market36%
RelationshipALIGNED
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