MIA vs OAK prediction for July 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects OAK 4.0 - MIA 4.5. MIA is favored with a 54.3% win probability. The run line is 1.5 and the total is 10.0. Model projects 8.4 total runs.
OAK
4.0
Projected Score
VS
O/U 10.0
MIA
4.5
Projected Score
Win Probability
OAKMIA
+1.5
Run Line (OAK)
10.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 52.1% (2,777 games)
Projected Runs Range 10th – 90th percentile
MIA
346
OAK
246
Projected
OAK 4.0 — MIA 4.5
Actual
OAK 8 — MIA 9
Starting Pitcher Matchup
Eury Pérez R
MIA
FF47%98 mph20% whiff
ST15%83 mph34% whiff
SL15%88 mph40% whiff
Gage Jump L
OAK
FF48%96 mph18% whiff
SL26%88 mph24% whiff
CU12%83 mph24% whiff
Weather Impact
Oakland Coliseum
66°F10 mph wind
HR: 0.972 Total: 0.982
8mph in
Bullpen Comparison
MIA
3.85ERA
3.56FIP
9.97K/9
4.48BB/9
1.21WHIP
OAK
4.87ERA
3.91FIP
9.81K/9
4.20BB/9
1.44WHIP
Betting Edges
RUN_LINE HOME +1.5
-37.9% EV
-192
TOTAL OVER 10.0
-33.3% EV
-105
TOTAL UNDER 10.0
+23.0% EV
-115
ML HOME
-10.7% EV
-118
RUN_LINE AWAY -1.5
+7.2% EV
+158
F5_ML HOME
-5.2% EV
-122
First 5 Innings & NRFI
MIA F5
2.4 runs
41.5% win
OAK F5
2.5 runs
45.1% win
F5 Total
4.9
NRFI
51.6%
YRFI
48.4%
Avg 1st Inn Runs
1.05
HR Spotlight
Avg HRs
3.0
Over 0.5 HR
95%
Over 1.5 HR
80%
No HR
5%
Esteury Ruiz MIA30.0%
ISO: 0.367 | Barrel: 7.7% | vs Gage Jump | Park: 0.94x Platoon: 1.12x
Nick Kurtz OAK30.0%
ISO: 0.306 | Barrel: 18.3% | vs Eury Pérez | Park: 0.94x Platoon: 1.12x
Zack Gelof OAK30.0%
ISO: 0.191 | Barrel: 12.5% | vs Eury Pérez | Park: 0.94x
Pitcher Strikeout Projections
Eury Pérez
0.0 K projected
MIA | K/9: 0.0
Gage Jump
0.0 K projected
OAK | K/9: 0.0
Injury Report
MIA7 injured
Anthony Bender RP15-DAY-IL
Janson Junk SP15-DAY-IL
Josh Ekness RP60-DAY-IL
Andrew Nardi RP60-DAY-IL
Robby Snelling SP60-DAY-IL
Ronny Henriquez RP60-DAY-IL
+1 more
OAK8 injured
Shea Langeliers CDAY-TO-DAY
Brent Rooker DH10-DAY-IL
Tyler Soderstrom LF10-DAY-IL
Jacob Wilson SS10-DAY-IL
Denzel Clarke CF60-DAY-IL
Luis Severino SP60-DAY-IL
+2 more
AI Intelligence Analysis
LEAN +1YELLOW ZONE50.1% WR (n=302)
Eury Pérez (B- grade, 10.4 K-rate, 54% stuff score, 49% command) is a STRIKEOUT-PRONE away pitcher vs Gage Jump (B-grade, 7.4 K-rate, 44.5% stuff). Jump is more control-oriented; Pérez is a swinger-and-miss guy. Model shows UNDER 10.0 at massive 23% edge (65.8% model prob). This is a high-edge play BUT in YELLOW zone suggesting overconfidence. However, the game context supports the under: (1) OAK is an atrocious offensive team (rebuilding), (2) Pérez's 10.4 K-rate suggests low contact, high strikeouts = fewer balls in play = fewer runs despite being away, (3) Jump is solid (B-grade, 7.4 K-rate) at home. Weather: 66.1F is COLD for early July, reducing fly ball distance by 0.5-1 run. Wind: 10.3 mph tail wind component of -7.9 mph (blowing IN) — significant under bias. These are TWO specific macro factors supporting the UNDER. Away ML at 3.5% edge is negligible. UNDER 10.0 is the play here, not direction.
Key Factors
- Eury Pérez 10.4 K-rate (highest away pitcher on slate) with 54% stuff score — strikeout pitcher means fewer balls in play, fewer runs
- Gage Jump 7.4 K-rate at home (solid) vs weak OAK offense = suppressed run environment
- Temperature 66.1F (cold) reduces fly ball distance by estimated 0.5-1 run
- Wind 10.3 mph with -7.9 mph tail component (IN to plate) — significant under bias on fly balls
- OAK is last-place team with poor offensive profile
Risk Factors
- 23% edge in YELLOW zone (50.1% historical WR) suggests model overconfident; true edge likely 12-15%
- Pérez away could get hit if mechanics are off; strikeout pitchers prone to variance
HIGH K RATE PITCHERCOLD WEATHERWIND IMPACTWEAK OFFENSEHIGH EDGE WARNING
Edge Analysis
Moneyline
MIA 54.3%
-37.9 pts
Run Line
+1.5
-37.9 pts
Total
10.0
+23.0 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 →