WSH vs OAK prediction for July 18, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects OAK 4.8 - WSH 6.0. WSH is favored with a 55.2% win probability. The run line is 1.5 and the total is 11.5. Model projects 10.7 total runs.
OAK
4.8
Projected Score
VS
O/U 11.5
WSH
6.0
Projected Score
Win Probability
OAKWSH
+1.5
Run Line (OAK)
11.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 55.5% (2,808 games)
Projected Runs Range 10th – 90th percentile
WSH
468
OAK
357
SOLID
1.0u
WSH @ OAK NRFI
Edge: 4.5% | Odds: 116
WSH @ OAK NRFI leans on a 0.947 pitcher_quality mark for home starter J.T. Ginn - elite command tier (0.422 command_score) paired with a 21.6% K rate and 0.10 BB rate. Away pitcher Zack Littell sits at 0.543 quality with significantly weaker stuff (0.087 stuff_score) and a 14.8% K rate. The 60.8°F temperature at Oakland Coliseum suppresses ball carry; park_total_mult of 0.989 confirms the environment works against scoring. Model projects 4.58 home runs and 6.12 away runs (10.7 total), well below the 11.5 market line. Sim score of 5.7-4.8 reflects low first-inning run probability. The 4.5% edge reflects market undervaluation of Ginn's command advantage and cold-weather suppression combined.
Starting Pitcher Matchup
Foster Griffin L
WSH
FC32%88 mph19% whiff
FF17%92 mph18% whiff
ST15%80 mph30% whiff
Jacob Lopez L
OAK
FF34%91 mph14% whiff
SL30%77 mph21% whiff
FC20%86 mph23% whiff
Weather Impact
Oakland Coliseum
61°F8 mph wind
HR: 0.982 Total: 0.989
5mph in
Bullpen Comparison
WSH
4.62ERA
4.93FIP
7.77K/9
4.06BB/9
1.38WHIP
OAK
4.87ERA
3.91FIP
9.81K/9
4.20BB/9
1.44WHIP
Betting Edges
RUN_LINE HOME +1.5
-40.5% EV
-156
F5_ML HOME
-18.5% EV
-118
TOTAL OVER 11.5
-14.1% EV
-105
NRFI NRFI
+13.0% EV
+146
RUN_LINE AWAY -1.5
+10.1% EV
+130
ML HOME
-9.0% EV
-104
First 5 Innings & NRFI
WSH F5
3.3 runs
51.4% win
OAK F5
2.6 runs
36.7% win
F5 Total
5.9
NRFI
50.5%
YRFI
49.5%
Avg 1st Inn Runs
1.13
HR Spotlight
Avg HRs
2.4
Over 0.5 HR
90%
Over 1.5 HR
69%
No HR
10%
Pitcher Strikeout Projections
Foster Griffin
0.0 K projected
WSH | K/9: 0.0
Jacob Lopez
0.0 K projected
OAK | K/9: 0.0
Injury Report
WSH8 injured
Jake Irvin SP60-DAY-IL
Drew Millas C10-DAY-IL
Trevor Williams SP60-DAY-IL
Richard Lovelady RP15-DAY-IL
Miles Mikolas SPSUSPENSION
Brad Lord RP15-DAY-IL
+2 more
OAK8 injured
Zack Gelof 3B10-DAY-IL
Nick Kurtz 1B10-DAY-IL
Justin Sterner RP15-DAY-IL
Brent Rooker DH60-DAY-IL
Denzel Clarke CF60-DAY-IL
Luis Severino SP60-DAY-IL
+2 more
AI Intelligence Analysis
LEAN +1GREEN ZONE57.6% WR (n=39)
Zack Littell (WSH) 5.29 ERA, weak stuff (D), mediocre command (B+) is dominated by J.T. Ginn (OAK) competent arm (C+, balanced). 3.2% ML edge on WSH away; NRFI edge (9.5%, 44.2% prob) is cleaner. Lean on NRFI, avoid weak ML.
Key Factors
- SP MISMATCH against WSH: Zack Littell (WSH) 5.29 ERA, stuff D (worst grade on slate), K-rate 14.8%. J.T. Ginn (OAK) C+, balanced (SI 34.8%, CH 19.7%), K-rate 21.6%. Ginn dominates Littell.
- Oakland Coliseum 60.8°F (coldest game on slate!), 8.0 mph wind IN (-5.0). Cold suppresses runs and HR distance significantly.
- NRFI GREEN zone (57.6% WR, 39 samples) — most reliable edge on slate. 9.5% edge (44.2% prob NRFI) is actionable.
- ML edge (3.2% on WSH) is weak. Don't bet directionless MLS; focus on NRFI prop.
- James Wood (WSH) 30% HR prob but cold weather mitigates. Oakland's cold park is pitcher-friendly.
Risk Factors
- NRFI prop is specialist bet type. Playability limited.
- Littell's D-grade stuff might actually help NRFI (hard contact plays, but first inning is short sample).
- Cold 60.8°F does suppress runs, helping UNDER (model 10.7 vs market 11.5), but NRFI is the real value play.
PITCHER MISMATCHNRFI VALUECOLD WEATHER
Edge Analysis
Moneyline
WSH 55.2%
-40.5 pts
Run Line
+1.5
-40.5 pts
Total
11.5
+4.7 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 →