SD vs CHC prediction for June 30, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects CHC 7.0 - SD 4.9. CHC is favored with a 68.3% win probability. The run line is -1.5 and the total is 11.5. Model projects 11.9 total runs.
CHC
7.0
Projected Score
VS
O/U 11.5
SD
4.9
Projected Score
Win Probability
CHCSD
-1.5
Run Line (CHC)
11.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 64.7% (2,559 games)
Projected Runs Range 10th – 90th percentile
SD
357
CHC
579
Projected
CHC 7.0 — SD 4.9
Actual
CHC 9 — SD 7
Starting Pitcher Matchup
JP Sears L
SD
FF40%92 mph16% whiff
ST27%79 mph23% whiff
CH15%84 mph18% whiff
Matthew Boyd L
CHC
FF50%93 mph20% whiff
CH28%79 mph33% whiff
SL13%84 mph48% whiff
Weather Impact
Wrigley Field
90°F14 mph wind
HR: 0.989 Total: 0.990
thin air, 12mph in
Bullpen Comparison
SD
3.15ERA
3.66FIP
8.41K/9
3.44BB/9
1.23WHIP
CHC
4.04ERA
5.13FIP
8.17K/9
4.04BB/9
1.34WHIP
Betting Edges
RUN_LINE AWAY +1.5
-48.9% EV
-156
ML AWAY
-22.4% EV
+126
RUN_LINE HOME -1.5
+17.8% EV
+130
F5_ML AWAY
-14.4% EV
+128
ML HOME
+10.3% EV
-147
TOTAL UNDER 11.5
-6.0% EV
-120
First 5 Innings & NRFI
SD F5
3.0 runs
32.6% win
CHC F5
4.3 runs
57.1% win
F5 Total
7.3
NRFI
41.2%
YRFI
58.8%
Avg 1st Inn Runs
1.42
HR Spotlight
Avg HRs
3.7
Over 0.5 HR
97%
Over 1.5 HR
88%
No HR
3%
Ian Happ CHC30.0%
ISO: 0.192 | Barrel: 14.4% | vs JP Sears | Park: 1.03x Platoon: 1.12x
Pete Crow-Armstrong CHC30.0%
ISO: 0.138 | Barrel: 10.8% | vs JP Sears | Park: 1.03x
Manny Machado SD23.5%
ISO: 0.226 | Barrel: 9.0% | vs Matthew Boyd | Park: 1.03x Platoon: 1.12x
Pitcher Strikeout Projections
JP Sears
0.0 K projected
SD | K/9: 0.0
Matthew Boyd
0.0 K projected
CHC | K/9: 0.0
Injury Report
SD8 injured
David Morgan RP15-DAY-IL
Luis Campusano C10-DAY-IL
Nick Pivetta SP60-DAY-IL
German Marquez SP15-DAY-IL
Matt Waldron SP15-DAY-IL
Lucas Giolito SP15-DAY-IL
+2 more
CHC8 injured
Ethan Roberts RP15-DAY-IL
Matt Shaw RF10-DAY-IL
Daniel Palencia RP15-DAY-IL
Hoby Milner RP15-DAY-IL
Jameson Taillon SP15-DAY-IL
Phil Maton RP15-DAY-IL
+2 more
AI Intelligence Analysis
STRONG BET +1YELLOW ZONE69.9% WR (n=7)
Matthew Boyd (CHC home, 11.6 K/9 elite, 27.4% K rate, B 56.9%) vs JP Sears (SD away, 0 K/9 data error, 19.5% K rate, B- 47%). DATA INTEGRITY ISSUE: Sears shows 'K/9': 0 in pitcher profile — likely data error. Use other metrics: 19.5% K rate (poor), B- 47 overall score (mediocre), 2.93 Bayesian ERA (respectable but conservative estimate). Boyd is clear ace: 11.6 K/9 elite, 27.4% K rate, B 56.9% score. MODEL heavily favors CHC at 68.3% (10.3% ML edge vs -147 market, 59.5% implied CHC). Zone: Home ML YELLOW (69.9% WR, n=7 in 10-15% edge bucket) — this is the BEST zone historically for this edge range. Combo: home ML GREEN (59.3% WR, n=82). Wind: 14.2 mph in (-11.8 tail wind) significantly suppresses runs. Park factor 1.03 neutral. 90.3°F warm but wind negates. Boyd's elite stuff + CHC home + RED zone on away ML (40.7% WR, n=82) = strong lean CHC home. NRFI edge +1.2% (62.5% model vs 61.5% implied) — Boyd first-inning strikeouts common with elite stuff.
Key Factors
- Boyd (CHC, 11.6 K/9 ELITE, 27.4% K rate, B 56.9%) is ace-level pitcher — rare elite stuff
- Sears (SD, K/9 DATA ERROR, 19.5% K rate, B- 47%) is mediocre — DATA INTEGRITY FLAG
- Pitcher mismatch: 11.6 vs unknown K/9 = Boyd dominates regardless of Sears true K/9
- CHC home field (neutral park 1.03) + wind 14.2 mph IN (-11.8 tail) = 0.42 run suppression
- Zone: Home ML 10-15% edge bucket 69.9% WR (n=7) — BEST zone historically
Risk Factors
- SEARS DATA ERROR: K/9 shows 0 — likely system failure. Real K/9 unknown, using 19.5% K rate as proxy
- Small sample zone (n=7) on 10-15% edge bucket — possible noise
- Wind suppresses runs (UNDER value on 11.5 total), but NRFI value (+1.2% edge) available instead
PITCHER MISMATCH ELITE VS MEDIOCREDATA INTEGRITY SEARS K9 ZEROHOME ML COMBO GREENZONE BEST HISTOGRAMNRFI VALUE AVAILABLEHIGH CONVICTION LEAN
Edge Analysis
Moneyline
CHC 68.3%
+17.8 pts
Run Line
-1.5
+17.8 pts
Total
11.5
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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 →