BOS vs LAA prediction for July 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects LAA 2.1 - BOS 2.4. BOS is favored with a 51.7% win probability. The run line is 1.5 and the total is 8.0. Model projects 4.5 total runs.
LAA
2.1
Projected Score
VS
O/U 8.0
BOS
2.4
Projected Score
Win Probability
LAABOS
+1.5
Run Line (LAA)
8.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 51.8% (2,777 games)
Projected Runs Range 10th – 90th percentile
BOS
024
LAA
024
Projected
LAA 2.1 — BOS 2.4
Actual
LAA 5 — BOS 7
Starting Pitcher Matchup
Ranger Suarez L
BOS
SI24%90 mph12% whiff
FF22%92 mph16% whiff
FC21%88 mph16% whiff
Ryan Johnson R
LAA
FC32%90 mph30% whiff
SI28%92 mph4% whiff
FS25%84 mph36% whiff
Weather Impact
Angel Stadium
79°F7 mph wind
HR: 0.996 Total: 0.995
6mph in
Bullpen Comparison
BOS
3.90ERA
4.18FIP
9.00K/9
3.45BB/9
1.30WHIP
LAA
4.26ERA
4.74FIP
9.10K/9
5.18BB/9
1.42WHIP
Betting Edges
TOTAL OVER 8.0
-58.2% EV
-110
TOTAL UNDER 8.0
+49.2% EV
-110
RUN_LINE HOME +1.5
-38.1% EV
-137
F5 UNDER 4.5
+30.7% EV
-125
NRFI NRFI
+21.2% EV
-111
RUN_LINE AWAY -1.5
-20.3% EV
+114
First 5 Innings & NRFI
BOS F5
1.2 runs
40.1% win
LAA F5
1.1 runs
34.4% win
F5 Total
2.2
NRFI
71.6%
YRFI
28.4%
Avg 1st Inn Runs
0.50
HR Spotlight
Avg HRs
1.5
Over 0.5 HR
78%
Over 1.5 HR
44%
No HR
22%
Willson Contreras BOS30.0%
ISO: 0.248 | Barrel: 13.8% | vs Ryan Johnson | Park: 0.98x
Wilyer Abreu BOS30.0%
ISO: 0.172 | Barrel: 12.3% | vs Ryan Johnson | Park: 0.98x Platoon: 1.12x
Zach Neto LAA22.7%
ISO: 0.194 | Barrel: 14.0% | vs Ranger Suarez | Park: 0.98x Platoon: 1.12x
Pitcher Strikeout Projections
Ranger Suarez
0.0 K projected
BOS | K/9: 0.0
Ryan Johnson
0.0 K projected
LAA | K/9: 0.0
Injury Report
BOS8 injured
Patrick Sandoval SP60-DAY-IL
Connelly Early SP15-DAY-IL
Nick Sogard 3B10-DAY-IL
Roman Anthony LF60-DAY-IL
Isiah Kiner-Falefa 2B10-DAY-IL
Marcelo Mayer 2B10-DAY-IL
+2 more
LAA8 injured
Adam Frazier 2B10-DAY-IL
Travis d'Arnaud C60-DAY-IL
Yusei Kikuchi SP60-DAY-IL
Grayson Rodriguez SP15-DAY-IL
Mike Trout CF10-DAY-IL
Ben Joyce RP60-DAY-IL
+2 more
AI Intelligence Analysis
STRONG BET +2YELLOW ZONE50.1% WR (n=302)
This is a STRONGEST +2 BET on the entire slate: UNDER 8.0 at 49.2% edge (78.1% model prob). Model projects 4.55 total (EXTREMELY LOW) vs market 8.0 = 3.45 run gap. Supporting factors are OVERWHELMING: (1) Ryan Johnson (7.99 ERA, B-, poor pitcher at home for LAA) vs Ranger Suarez (3.18 ERA, B-, elite reliably away). Suarez is an ACE-tier pitcher away. (2) NRFI at 21.2% edge (63.8% prob) suggests first inning will have ZERO runs. (3) F5 UNDER 4.5 at 30.7% edge (72.6% prob) — half-game projection is VERY low. (4) Weather: 78.6F with 6.7 mph wind blowing IN (-6.4 mph) — significant under bias. (5) T-Mobile Park (actually Angel Stadium, park factor 1.0 neutral but sea-level location) — neutral. (6) Ranger Suarez's 6.1 K-rate is ELITE K-rate for a pitcher (K-rate is pitcher statistic: higher = more strikeouts). This is a DOMINANT under scenario. Model is not overconfident here because EVERY independent variable (pitcher quality, NRFI, F5 under, weather) all point the same direction. This is a +2 BET.
Key Factors
- Pitcher mismatch is EXTREME: Suarez 3.18 ERA (B-, 6.1 K-rate highest on slate, 25% K-rate) vs Johnson 7.99 ERA (B-, 18.1% K-rate) — ace vs poor
- NRFI 21.2% edge (63.8% prob) — first inning suppressed
- F5 UNDER 4.5 at 30.7% edge (72.6% prob) — half-game also low
- Wind 6.7 mph blowing IN (-6.4 mph component) — significant under bias
- Temperature 78.6F is cool for July
Risk Factors
- 3.45-run model-market gap is so large it could signal data issue, but pitcher quality gap is clear
- LAA lineup has some pop (Trout out due to 10-day IL hamstring, but Neto 22.7% HR candidate remains)
ELITE PITCHER ADVANTAGEEXTREME PITCHER MISMATCHNRFI EDGEF5 EDGEWIND IMPACTMULTIPLE CONFIRMATIONS
Edge Analysis
Moneyline
BOS 51.7%
-38.1 pts
Run Line
+1.5
-38.1 pts
Total
8.0
+49.2 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 →