MLB Baseball

TB vs MIA Prediction

June 6, 2026

10,000 Monte Carlo simulations

TB vs MIA prediction for June 6, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects MIA 4.0 - TB 4.6. TB is favored with a 54.4% win probability. The run line is 1.5 and the total is 7.5. Model projects 8.6 total runs.

MIA
4.0
Projected Score
VS O/U 7.5
TB
4.6
Projected Score
Win Probability
45.6%
54.4%
MIATB
+1.5
Run Line (MIA)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 55.7% (2,183 games)

Projected Runs Range 10th – 90th percentile

TB
357
MIA
246
FINALMIA 4 — TB 3
Projected
MIA 4.0 — TB 4.6
Actual
MIA 4 — TB 3

Starting Pitcher Matchup

Shane McClanahan L
TB
FF38%95 mph18% whiff
CH29%87 mph33% whiff
SL20%88 mph29% whiff
Lake Bachar R
MIA
FF33%95 mph18% whiff
SL25%90 mph49% whiff
ST20%86 mph40% whiff

Weather Impact

loanDepot park
84°F10 mph windRoof: retractable
HR: 1.075 Total: 1.041
9mph out

Bullpen Comparison

TB
4.37ERA
4.14FIP
8.12K/9
3.38BB/9
1.33WHIP
MIA
3.60ERA
3.72FIP
9.36K/9
4.28BB/9
1.20WHIP

Betting Edges

RUN_LINE HOME +1.5
-38.8% EV
-154
TOTAL UNDER 7.5
-10.0% EV
-105
RUN_LINE AWAY -1.5
-8.5% EV
+128
F5_ML AWAY
-6.9% EV
-145
ML AWAY
-4.5% EV
-133
F5_ML HOME
-3.0% EV
+116

First 5 Innings & NRFI

TB F5
2.5 runs
46.0% win
MIA F5
2.2 runs
38.2% win
F5 Total
4.7
NRFI
55.9%
YRFI
44.1%
Avg 1st Inn Runs
0.93

HR Spotlight

Avg HRs
2.1
Over 0.5 HR
87%
Over 1.5 HR
61%
No HR
13%
Jonathan Aranda TB30.0%
ISO: 0.244 | Barrel: 11.3% | vs Lake Bachar | Park: 0.93x Platoon: 1.12x
Junior Caminero TB30.0%
ISO: 0.236 | Barrel: 13.2% | vs Lake Bachar | Park: 0.93x
Esteury Ruiz MIA27.2%
ISO: 0.385 | Barrel: 16.1% | vs Shane McClanahan | Park: 0.93x Platoon: 1.12x

Pitcher Strikeout Projections

Shane McClanahan
0.0 K projected
TB | K/9: 0.0
Lake Bachar
0.0 K projected
MIA | K/9: 0.0

Injury Report

TB8 injured
Chandler Simpson LFDAY-TO-DAY
Jonathan Heasley RP15-DAY-IL
Craig Kimbrel RP15-DAY-IL
Manuel Rodriguez RP60-DAY-IL
Steven Wilson RP60-DAY-IL
Edwin Uceta RP60-DAY-IL
+2 more
MIA8 injured
Thomas White SPDAY-TO-DAY
Josh Ekness RP15-DAY-IL
Griffin Conine LF60-DAY-IL
Andrew Nardi RP60-DAY-IL
Janson Junk SP15-DAY-IL
Eury Perez SP15-DAY-IL
+2 more

AI Intelligence Analysis

NEUTRALYELLOW ZONE47.5% WR (n=204)
Model is nearly neutral (TB 54.4% away win vs market 57.1% implied), and all edges are weak (<1% on ML, 0.7% on totals, all under 1% edges). This is a coin-flip game where no edge exists. Lake Bachar (MIA, 3.73 ERA) vs Shane McClanahan (TB, 2.65 ERA) is slight MIA disadvantage (home pitcher disadvantage to TB arm). But model says TB 54% and market says TB 57%, so 3% market-model gap is normal variance, not edge. Recommend SKIP — zero actionable edge.

Key Factors

  • SP matchup: McClanahan (TB, 2.65 ERA, elite) vs Bachar (MIA, 3.73 ERA, solid). TB arm is better, but not a mismatch. Both are B-/B tier.
  • No edge <1%. All model projections align with market within 1% margin of error. This is a market-efficient game.
  • Zone performance is poor: Away ML in all brackets shows 47.5% historical WR (YELLOW, n=204). Model says 54.6% but zone says 47.5%. Model is overconfident on road dogs.

Risk Factors

  • Zone data conflicts model confidence: Away ML zone is YELLOW 47.5% WR. Model 54.6% confidence is likely inflated by systematic bias toward away underdogs.
  • All edges <1% signal no information advantage. Game is fairly priced.
  • Performance profile shows away dogs underperform vs overs in recent 30-day window (away combo 55.2% WR, but away-favorite only 64.7% WR — this is underdog away, worse profile).
Sharp MoneyAgainst ModelMarket implies TB 57.1% (vs model 54.6%), suggesting market is slightly more bullish away.
NEUTRALZONE CONFLICT

Edge Analysis

Moneyline
TB 54.4%
-38.8 pts
Run Line
+1.5
-38.8 pts
Total
7.5
+0.7 pts
How this prediction was generated: This page shows output from the Olympus Bets MLB Baseball Monte Carlo engine. Each game is simulated 10,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →

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