SF vs BAL prediction for April 11, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects BAL 3.7 - SF 3.2. BAL is favored with a 57.2% win probability. The run line is 1.5 and the total is 7.5. Model projects 7.0 total runs.
BAL
3.7
Projected Score
VS
O/U 7.5
SF
3.2
Projected Score
Win Probability
BALSF
+1.5
Run Line (BAL)
7.5
Total Line
10,000
Simulations
SFBAL L5
Calibrated accuracy at this confidence: 57.4% (2,040 games)
Projected Runs Range 10th – 90th percentile
SF
135
BAL
246
Pick Results
Jung Hoo Lee OVER 0.5 Strikeoutsbatter_ksLOSS-2.00u
Pete Alonso OVER 0.5 Hitsbatter_hitsLOSS-1.50u
Starting Pitcher Matchup
Logan Webb R
SF
SI40%92 mph12% whiff
CH23%86 mph19% whiff
ST19%84 mph18% whiff
Chris Bassitt R
BAL
SI41%92 mph12% whiff
FC16%89 mph26% whiff
CU16%71 mph31% whiff
Weather Impact
Oriole Park at Camden Yards
64°F7 mph wind
HR: 1.005 Total: 1.003
neutral
Bullpen Comparison
SF
4.58ERA
4.74FIP
9.69K/9
5.05BB/9
1.42WHIP
BAL
3.61ERA
4.12FIP
10.85K/9
4.87BB/9
1.24WHIP
Betting Edges
RUN_LINE HOME +1.5
-29.8% EV
-169
RUN_LINE AWAY -1.5
-26.5% EV
+140
TOTAL OVER 7.5
-18.8% EV
-105
ML AWAY
-16.2% EV
-120
F5_ML AWAY
-14.9% EV
-132
F5 UNDER 3.5
+13.1% EV
+108
First 5 Innings & NRFI
SF F5
1.6 runs
35.9% win
BAL F5
1.9 runs
43.0% win
F5 Total
3.6
NRFI
66.1%
YRFI
33.9%
Avg 1st Inn Runs
0.62
HR Spotlight
Avg HRs
1.9
Over 0.5 HR
84%
Over 1.5 HR
56%
No HR
16%
Gunnar Henderson BAL50.0%
ISO: 0.250 | Barrel: 19.6% | vs Logan Webb | Park: 1.03x Platoon: 1.12x
Pete Alonso BAL25.7%
ISO: 0.122 | Barrel: 5.0% | vs Logan Webb | Park: 1.03x
Taylor Ward BAL24.5%
ISO: 0.171 | Barrel: 9.7% | vs Logan Webb | Park: 1.03x
Pitcher Strikeout Projections
Logan Webb
0.0 K projected
SF | K/9: 0.0
Chris Bassitt
0.0 K projected
BAL | K/9: 0.0
Injury Report
SF8 injured
Sam Hentges RP15-DAY-IL
Joel Peguero RP15-DAY-IL
Parks Harber 3BDAY-TO-DAY
Jason Foley RP60-DAY-IL
Jose Butto RP15-DAY-IL
Hayden Birdsong RP60-DAY-IL
+2 more
BAL8 injured
Tyler O'Neill RFDAY-TO-DAY
Zach Eflin SP60-DAY-IL
Maverick Handley COUT
Jordan Westburg 3B60-DAY-IL
Dietrich Enns RP15-DAY-IL
Yaramil Hiraldo RP15-DAY-IL
+2 more
AI Intelligence Analysis
LEAN +1GREEN ZONE61.9% WR (n=9)
Model projects BAL 57.2% home (11.6% ML edge, 55.2% prob vs. market 49.5% implied). Chris Bassitt shows corrupted ERA (15.35(!)) — DATA INTEGRITY ISSUE — but grades are B- (0.512 overall, 0.367 stuff). Logan Webb is C+ (0.381 overall, weaker). Home advantage + pitcher grades support BAL lean, but Bassitt ERA corruption is RED FLAG. Do not full unit.
Key Factors
- PITCHER QUALITY MISMATCH: Chris Bassitt (B- grade, 15.35 ERA(?), 0.512 overall score, 22.2% K-rate, good command 0.688) vs. Logan Webb (C+ grade, 5.4 ERA, 0.381 overall score, 18.99% K-rate). Bassitt ERA is IMPOSSIBLE (15.35) — data corruption. However, B- grade and 0.512 overall score suggest pitcher is LEGITIMATE quality, likely in B-/C+ range actually.
- Home field advantage (BAL) + slight pitcher edge = model 55.2% home win prob. Market 49.5% implied is DISRESPECTING home field.
- Gunnar Henderson (50% HR prob) is elite power for BAL. Pete Alonso (25.7% HR prob) adds secondary power.
- Park factor (1.03, slight enhancement) + neutral weather (64F) = slight home run benefit.
- GREEN ZONE (61.9% WR on home ML 10-15% edge, n=9) is VERY STRONG — historical home favorites with this edge profile have 61.9% actual WR. This is significantly better than overall home ML zone (54.7% WR).
Risk Factors
- Bassitt ERA 15.35 is data corruption — cannot trust home pitcher profile without external verification. If Bassitt unavailable or injured, game conditions shift.
- Green zone (61.9% WR) is based on n=9 sample — small sample size creates volatility.
- SF road record unknown but historical away underdog (Webb is underdog pitcher) can be risky. Away favorite is RED zone.
Edge Analysis
Moneyline
BAL 57.2%
-29.8 pts
Run Line
+1.5
-29.8 pts
Total
7.5
+11.4 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 →