MLB Baseball

CIN vs NYM Prediction

May 27, 2026

10,000 Monte Carlo simulations

CIN vs NYM prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects NYM 3.9 - CIN 4.2. CIN is favored with a 50.8% win probability. The run line is 1.5 and the total is 8.5. Model projects 8.0 total runs.

NYM
3.9
Projected Score
VS O/U 8.5
CIN
4.2
Projected Score
Win Probability
49.2%
50.8%
NYMCIN
+1.5
Run Line (NYM)
8.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.4% (2,300 games)

Projected Runs Range 10th – 90th percentile

CIN
246
NYM
246
FINALNYM 4 — CIN 2
Projected
NYM 3.9 — CIN 4.2
Actual
NYM 4 — CIN 2

Starting Pitcher Matchup

Andrew Abbott L
CIN
FF48%93 mph7% whiff
ST20%82 mph26% whiff
CH18%86 mph44% whiff
Huascar Brazobán R
NYM
SI49%96 mph21% whiff
CH41%90 mph31% whiff
FF5%96 mph11% whiff

Weather Impact

Citi Field
85°F9 mph wind
HR: 1.023 Total: 1.011
thin air

Bullpen Comparison

CIN
4.71ERA
5.30FIP
9.18K/9
5.98BB/9
1.49WHIP
NYM
3.72ERA
3.62FIP
9.39K/9
3.79BB/9
1.28WHIP

Betting Edges

RUN_LINE HOME +1.5
-39.6% EV
-204
F5 UNDER 4.5
+19.6% EV
-108
TOTAL OVER 8.5
-13.6% EV
+100
NRFI NRFI
+11.7% EV
-111
F5_ML AWAY
-10.9% EV
-106
ML HOME
-8.8% EV
-120

First 5 Innings & NRFI

CIN F5
1.7 runs
35.8% win
NYM F5
2.0 runs
43.8% win
F5 Total
3.7
NRFI
63.9%
YRFI
36.1%
Avg 1st Inn Runs
0.69

HR Spotlight

Avg HRs
1.7
Over 0.5 HR
82%
Over 1.5 HR
51%
No HR
18%
Juan Soto NYM22.3%
ISO: 0.164 | Barrel: 16.0% | vs Andrew Abbott | Park: 0.96x
Nathaniel Lowe CIN21.1%
ISO: 0.330 | Barrel: 16.6% | vs Huascar Brazobán | Park: 0.96x Platoon: 1.12x
Mark Vientos NYM17.7%
ISO: 0.187 | Barrel: 7.9% | vs Andrew Abbott | Park: 0.96x Platoon: 1.12x

Pitcher Strikeout Projections

Andrew Abbott
0.0 K projected
CIN | K/9: 0.0
Huascar Brazobán
0.0 K projected
NYM | K/9: 0.0

Injury Report

CIN8 injured
Rhett Lowder SP15-DAY-IL
Jose Trevino C10-DAY-IL
Emilio Pagan RP15-DAY-IL
Hunter Greene SP60-DAY-IL
Ke'Bryan Hayes 3B10-DAY-IL
Connor Burns CDAY-TO-DAY
+2 more
NYM8 injured
Luis Robert Jr. CF60-DAY-IL
Jorge Polanco 1B10-DAY-IL
Tyrone Taylor CF10-DAY-IL
Francisco Alvarez C10-DAY-IL
Francisco Lindor SS10-DAY-IL
Kodai Senga SP15-DAY-IL
+2 more

AI Intelligence Analysis

LEANYELLOW ZONE50.0% WR (n=289)
CIN @ NYM: Model shows UNDER 8.5 edge of 3.4% (56.8% UNDER), with minor ML edge +1.4% away. This is a low-edge game across all markets — essentially neutral. However, F5 UNDER shows +19.6% edge (62% prob UNDER 4.5), suggesting pitching-dominant early game. NRFI shows +11.7% edge (58.8% NRFI). Both starters have TBD/unknown ERA (Brazobán and Abbott), limiting pitcher quality assessment. The game has mild under bias but nothing extreme. LEAN justified on F5 UNDER edge (19.6% is real), not full game total. Unit reduced to 0.75 acknowledging TOTALS DISABLED warning.

Key Factors

  • SP TBD data: Brazobán and Abbott both have unknown ERA/pitch data. Cannot assess quality. Model baseline for both = moderate (C+/C grade from system).
  • F5 under edge strong: +19.6% edge on UNDER 4.5 (62% prob) is real and actionable. Early-inning pitching advantage is clear.
  • NRFI advantage: +11.7% edge (58.8% NRFI) suggests first inning is quiet. Consistent with early under thesis.
  • NYM lineup weakened: Multiple 10-day IL players limit offensive upside. Run production risk lower.
  • Full-game under modest: 3.4% edge on full-game UNDER is too small. F5 is where the edge is.

Risk Factors

  • TOTALS DISABLED: Full-game totals are disabled (45.6% WR). Cannot rely on totals edge.
  • F5 markets enabled: F5_total is enabled (54.5% WR), so F5 UNDER is preferred to full-game UNDER.
  • Pitcher uncertainty: If either SP is significantly weaker/stronger than estimated, game run expectation shifts.
TBD PITCHER DATAF5 UNDER STRONG EDGENRFI EDGETOTALS DISABLED CAUTIONLEAN F5 NOT FULL GAME

Edge Analysis

Moneyline
CIN 50.8%
-39.6 pts
Run Line
+1.5
-39.6 pts
Total
8.5
+3.4 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 →

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