CIN vs CLE prediction for May 15, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects CLE 3.9 - CIN 4.2. CIN is favored with a 51.8% win probability. The run line is -1.5 and the total is 8.0. Model projects 8.1 total runs.
CLE
3.9
Projected Score
VS
O/U 8.0
CIN
4.2
Projected Score
Win Probability
CLECIN
-1.5
Run Line (CLE)
8.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 52.0% (2,085 games)
Projected Runs Range 10th – 90th percentile
CIN
246
CLE
246
Projected
CLE 3.9 — CIN 4.2
Actual
CLE 6 — CIN 7
Starting Pitcher Matchup
Andrew Abbott L
CIN
FF47%92 mph6% whiff
ST20%82 mph27% whiff
CH20%86 mph47% whiff
Tanner Bibee R
CLE
FC28%86 mph37% whiff
FF24%94 mph10% whiff
CH19%81 mph40% whiff
Weather Impact
Progressive Field
65°F4 mph wind
HR: 1.041 Total: 1.022
neutral
Bullpen Comparison
CIN
4.35ERA
5.03FIP
9.32K/9
5.96BB/9
1.49WHIP
CLE
3.95ERA
3.93FIP
10.42K/9
3.76BB/9
1.28WHIP
Betting Edges
RUN_LINE AWAY +1.5
-33.8% EV
-192
TOTAL OVER 8.0
-14.3% EV
-108
F5_ML HOME
-13.7% EV
-132
RUN_LINE HOME -1.5
-13.4% EV
+160
ML HOME
-12.9% EV
-135
ML AWAY
+7.9% EV
+116
First 5 Innings & NRFI
CIN F5
2.2 runs
43.6% win
CLE F5
2.1 runs
39.2% win
F5 Total
4.3
NRFI
58.5%
YRFI
41.5%
Avg 1st Inn Runs
0.86
HR Spotlight
Avg HRs
2.0
Over 0.5 HR
86%
Over 1.5 HR
58%
No HR
14%
JJ Bleday CIN30.0%
ISO: 0.488 | Barrel: 20.0% | vs Tanner Bibee | Park: 0.97x Platoon: 1.12x
Chase DeLauter CLE26.1%
ISO: 0.190 | Barrel: 10.4% | vs Andrew Abbott | Park: 0.97x
Nathaniel Lowe CIN23.8%
ISO: 0.311 | Barrel: 15.4% | vs Tanner Bibee | Park: 0.97x Platoon: 1.12x
Pitcher Strikeout Projections
Andrew Abbott
0.0 K projected
CIN | K/9: 0.0
Tanner Bibee
0.0 K projected
CLE | K/9: 0.0
Injury Report
CIN8 injured
Eugenio Suarez 3B10-DAY-IL
Rhett Lowder SP15-DAY-IL
Hunter Greene SP60-DAY-IL
Caleb Ferguson RP15-DAY-IL
Connor Burns CDAY-TO-DAY
Josh Staumont RPDAY-TO-DAY
+2 more
CLE4 injured
Gabriel Arias SS10-DAY-IL
Shawn Armstrong RP15-DAY-IL
Andrew Walters RP15-DAY-IL
Carlos Hernandez RPDAY-TO-DAY
AI Intelligence Analysis
LEAN +1YELLOW ZONE43.7% WR (n=6)
Away underdog CIN has small 7.9% edge (49.9% model vs 46.3% market) in RED zone. Pitcher matchup slightly favors HOME (Tanner Bibee 4.50 ERA vs Andrew Abbott 4.83 ERA but Abbott is LHP). Market pricing is fairly tight but undervalues CIN as 7:2 dog. Edge is real but small and RED zone (45.5% WR on away ML) means this is a contrarian lean, not a confident bet.
Key Factors
- Away underdog in RED ZONE: CIN at +116 (46.3%) is in RED zone for away MLs (44.8% WR). This is a structural weakness — away dogs lose money historically. Do not overweight this edge.
- Pitcher quality near parity: Bibee (4.50 ERA, B- grade) vs Abbott (4.83 ERA, C+ grade LHP) is slight Bibee edge but not dominant. Bibee home advantage and park factor 1.022 helps.
- LHP context: Abbott is LHP to lineup that has some RHB power. CIN has decent LHP-hitting stats (would need lineup review). This edge is not captured in ERA alone.
- Weather cold (65.2°F) slightly suppressive but mild tailwind (3.7mph) adds ~0.1 runs. Minimal impact either way.
- Spread-ML signal: Model gives CLE only slight edge (-0.2 runs) but market gives CLE bigger edge (implied 53.7% vs 50.1% model). Market is skeptical of CIN value here.
Risk Factors
- RED ZONE away ML: 44.8% WR (n=152) on all away MLs historically. This specific 7.9% edge sits in worst historical zone for away teams. Even with edge, zone performance suggests regression.
- Small edge (7.9%) with big zone problem = not a great risk-reward. Would need additional sharp money signal or lineup data to upgrade from LEAN.
- Bibee home record likely strong; CLE is slight favorite for a reason. Model edge of 7.9% might be model noise, not real market inefficiency.
RED ZONEAWAY UNDERDOGSMALL EDGEZONE CAUTION
Edge Analysis
Moneyline
CIN 51.8%
-13.4 pts
Run Line
-1.5
-13.4 pts
Total
8.0
--
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 →