SEA vs MIA prediction for July 9, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects MIA 3.8 - SEA 3.0. MIA is favored with a 54.8% win probability. The run line is 1.5 and the total is 8.0. Model projects 6.8 total runs.
MIA
3.8
Projected Score
VS
O/U 8.0
SEA
3.0
Projected Score
Win Probability
MIASEA
+1.5
Run Line (MIA)
8.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 57.2% (2,789 games)
Projected Runs Range 10th – 90th percentile
SEA
135
MIA
246
Projected
MIA 3.8 — SEA 3.0
Actual
MIA 8 — SEA 4
Starting Pitcher Matchup
Bryce Miller R
SEA
FF44%96 mph20% whiff
FS24%85 mph28% whiff
SL12%88 mph30% whiff
Janson Junk R
MIA
FF34%94 mph10% whiff
SL23%86 mph14% whiff
CH19%87 mph27% whiff
Weather Impact
loanDepot park
87°F8 mph windRoof: retractable
HR: 1.074 Total: 1.040
thin air, 8mph out
Bullpen Comparison
SEA
3.37ERA
3.72FIP
8.85K/9
3.64BB/9
1.33WHIP
MIA
3.85ERA
3.56FIP
9.97K/9
4.48BB/9
1.21WHIP
Betting Edges
RUN_LINE AWAY -1.5
-28.6% EV
+128
TOTAL OVER 8.0
-24.4% EV
-115
F5_ML AWAY
-24.4% EV
-149
RUN_LINE HOME +1.5
-23.4% EV
-154
F5_ML HOME
+20.4% EV
+120
TOTAL UNDER 8.0
+16.2% EV
-105
First 5 Innings & NRFI
SEA F5
1.5 runs
33.1% win
MIA F5
2.0 runs
47.1% win
F5 Total
3.5
NRFI
60.6%
YRFI
39.4%
Avg 1st Inn Runs
0.77
HR Spotlight
Avg HRs
1.8
Over 0.5 HR
84%
Over 1.5 HR
54%
No HR
16%
Dominic Canzone SEA30.0%
ISO: 0.294 | Barrel: 14.5% | vs Janson Junk | Park: 0.93x Platoon: 1.12x
Luke Raley SEA30.0%
ISO: 0.286 | Barrel: 10.6% | vs Janson Junk | Park: 0.93x Platoon: 1.12x
J.P. Crawford SEA28.2%
ISO: 0.236 | Barrel: 4.8% | vs Janson Junk | Park: 0.93x Platoon: 1.12x
Pitcher Strikeout Projections
Bryce Miller
0.0 K projected
SEA | K/9: 0.0
Janson Junk
0.0 K projected
MIA | K/9: 0.0
Injury Report
SEA8 injured
Brendan Donovan 3B10-DAY-IL
Cooper Criswell RP60-DAY-IL
Julio Rodriguez CF7-DAY IL
Rob Refsnyder DH10-DAY-IL
Carlos Vargas RP60-DAY-IL
Will Wilson 3B60-DAY-IL
+2 more
MIA8 injured
John King RP15-DAY-IL
Owen Caissie RF10-DAY-IL
Anthony Bender RP15-DAY-IL
Josh Ekness RP60-DAY-IL
Andrew Nardi RP60-DAY-IL
Robby Snelling SP60-DAY-IL
+2 more
AI Intelligence Analysis
LEAN +1YELLOW ZONE57.3% WR (n=16)
Market severely underprices MIA home advantage: +112 (47.2% implied) vs model 51% (3.8% gap). Bryce Miller (B+, 1.85 Bayesian ERA, 33.2% K rate!!!) is elite away pitcher, but MIA's offensive projection (3.75 runs) exceeds SEA (3.0 runs) by 0.75 runs—lineup dominance overcomes pitcher disadvantage. F5_ML HOME (20.4% edge, 54.7% prob) superior to full-game, but +112 ML offers value if accepting lineup + home-field theory. Prefer F5 for cleaner edge, but full-game ML actionable.
Key Factors
- Bryce Miller (SEA away): B+ grade, elite 1.85 Bayesian ERA (top-5 in league), 33.2% K rate, 33.2% K/9 implied—one of elite pitchers on slate
- Janson Junk (MIA home): B- grade, 5.18 ERA, 8.0 K/9, 16.2% K rate—below-average, vulnerable starter
- Pitcher mismatch AGAINST MIA: Miller (1.85 ERA) >> Junk (5.18 ERA) = 3.33 ERA gap favoring away team
- Yet model favors HOME (51% prob): MIA's offensive superiority (3.75 vs 3.0 runs) + home field override pitcher disadvantage
- F5_ML HOME 20.4% edge (54.7% prob)—Miller likely limited to 2-3 innings, MIA's bats can exploit fresh arms after early exit
Risk Factors
- Bryce Miller is genuinely elite (1.85 ERA, 33.2% K rate)—could suppress MIA scoring despite home advantage; pitcher advantage might be underestimated
- YELLOW zone HOME ML at 57.3% WR (n=16)—decent but not strong; 8% edge is modest and zone doesn't confirm GREEN
- SEA's offensive capability with stars (Canzone, Raley, Crawford all 28-30% HR prob) could exploit Junk's weak pitching
PITCHER MISMATCHF5 ADVANTAGEHOME FIELD ADVANTAGESHARP SUPPORT
Edge Analysis
Moneyline
MIA 54.8%
-23.4 pts
Run Line
+1.5
-23.4 pts
Total
8.0
+16.2 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 →