MLB Baseball

TEX vs OAK Prediction

April 15, 2026

10,000 Monte Carlo simulations

TEX vs OAK prediction for April 15, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects OAK 3.5 - TEX 3.5. OAK is favored with a 50.7% win probability. The run line is 1.5 and the total is 9.5. Model projects 7.0 total runs.

OAK
3.5
Projected Score
VS O/U 9.5
TEX
3.5
Projected Score
Win Probability
50.7%
49.3%
OAKTEX
+1.5
Run Line (OAK)
9.5
Total Line
10,000
Simulations
TEX W4OAK L4
Calibrated accuracy at this confidence: 53.6% (2,040 games)

Projected Runs Range 10th – 90th percentile

TEX
245
OAK
245

Pick Results

Jeff Mcneil OVER 0.5 Strikeoutsbatter_ksLOSS-2.00u

Starting Pitcher Matchup

Kumar Rocker R
TEX
SI26%96 mph9% whiff
FC21%90 mph32% whiff
FF20%96 mph17% whiff
J.T. Ginn R
OAK
SI44%95 mph17% whiff
SL17%86 mph27% whiff
FC16%92 mph17% whiff

Weather Impact

Oakland Coliseum
58°F12 mph wind
HR: 0.964 Total: 0.978
8mph in

Bullpen Comparison

TEX
2.16ERA
3.71FIP
8.14K/9
3.65BB/9
1.15WHIP
OAK
4.11ERA
4.05FIP
9.14K/9
5.36BB/9
1.58WHIP

Betting Edges

TOTAL OVER 9.5
-43.4% EV
-115
TOTAL UNDER 9.5
+38.3% EV
-105
RUN_LINE HOME +1.5
-36.4% EV
-172
F5 UNDER 5.5
+23.6% EV
-130
NRFI NRFI
+16.9% EV
+100
RUN_LINE AWAY -1.5
-16.3% EV
+142

First 5 Innings & NRFI

TEX F5
1.8 runs
37.1% win
OAK F5
2.1 runs
43.6% win
F5 Total
3.9
NRFI
61.3%
YRFI
38.8%
Avg 1st Inn Runs
0.74

HR Spotlight

Avg HRs
2.0
Over 0.5 HR
87%
Over 1.5 HR
60%
No HR
13%

Pitcher Strikeout Projections

Kumar Rocker
0.0 K projected
TEX | K/9: 0.0
J.T. Ginn
0.0 K projected
OAK | K/9: 0.0

Injury Report

TEX7 injured
Cody Freeman 3B10-DAY-IL
Cody Bradford SP15-DAY-IL
Sebastian Walcott SSDAY-TO-DAY
Carter Baumler RP15-DAY-IL
Jordan Montgomery SP60-DAY-IL
Nabil Crismatt SPOUT
+1 more
OAK1 injured
Gunnar Hoglund SP60-DAY-IL

Edge Analysis

Moneyline
OAK 50.7%
-36.4 pts
Run Line
+1.5
-36.4 pts
Total
9.5
+38.3 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. Full methodology →

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