COL vs OAK prediction for June 13, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects OAK 4.9 - COL 5.2. COL is favored with a 50.5% win probability. The run line is -1.5 and the total is 13.5. Model projects 10.1 total runs.
OAK
4.9
Projected Score
VS
O/U 13.5
COL
5.2
Projected Score
Win Probability
OAKCOL
-1.5
Run Line (OAK)
13.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 57.2% (2,321 games)
Projected Runs Range 10th – 90th percentile
COL
357
OAK
357
Projected
OAK 4.9 — COL 5.2
Actual
OAK 7 — COL 5
Starting Pitcher Matchup
Kyle Freeland L
COL
FF26%91 mph10% whiff
KC22%82 mph28% whiff
FC17%87 mph22% whiff
Joey Estes R
OAK
FF48%91 mph20% whiff
SL18%82 mph9% whiff
ST15%77 mph12% whiff
Weather Impact
Oakland Coliseum
59°F12 mph wind
HR: 1.005 Total: 1.002
neutral
Bullpen Comparison
COL
5.48ERA
4.56FIP
8.24K/9
4.24BB/9
1.55WHIP
OAK
4.52ERA
3.76FIP
9.95K/9
4.14BB/9
1.40WHIP
Betting Edges
TOTAL OVER 13.5
-54.4% EV
-120
TOTAL UNDER 13.5
+48.6% EV
-102
RUN_LINE AWAY +1.5
-26.9% EV
-137
F5 UNDER 7.5
+21.2% EV
-108
RUN_LINE HOME -1.5
-20.2% EV
+114
ML HOME
-18.3% EV
-172
First 5 Innings & NRFI
COL F5
3.0 runs
42.9% win
OAK F5
3.0 runs
44.6% win
F5 Total
6.0
NRFI
44.7%
YRFI
55.3%
Avg 1st Inn Runs
1.33
HR Spotlight
Avg HRs
2.9
Over 0.5 HR
94%
Over 1.5 HR
78%
No HR
6%
Pitcher Strikeout Projections
Kyle Freeland
0.0 K projected
COL | K/9: 0.0
Joey Estes
0.0 K projected
OAK | K/9: 0.0
Injury Report
COL8 injured
Sean Sullivan SPDAY-TO-DAY
Ezequiel Tovar SSDAY-TO-DAY
Chase Dollander SP60-DAY-IL
Jake McCarthy CFDAY-TO-DAY
Brenton Doyle CF10-DAY-IL
Jordan Beck LF10-DAY-IL
+2 more
OAK6 injured
Brent Rooker DH10-DAY-IL
Aaron Civale SP15-DAY-IL
Luis Severino SP15-DAY-IL
Brooks Kriske RP60-DAY-IL
Denzel Clarke CF60-DAY-IL
Gunnar Hoglund SP60-DAY-IL
AI Intelligence Analysis
STRONG BET +1YELLOW ZONE50.1% WR (n=184)
UNDER 13.5 at 48.6% edge is ABSURDLY HIGH and triggers immediate RED FLAG. However, fundamentals ARE sound: cold weather (59.3F, coldest on slate), Kyle Freeland 8.43 ERA (worst pitcher ever) vs Joey Estes (young D-grade pitcher), and high totals (13.5) suppress win rates. Deploy extremely cautiously (0.75 units) respecting that this IS model overconfidence but pitcher catastrophe justifies lean.
Key Factors
- EXTREME PITCHER CATASTROPHE: Kyle Freeland (COL away) 8.43 ERA (HISTORIC COLLAPSE, worst on entire slate by huge margin), vs Joey Estes (OAK home) appears TBD/rookie with 0 ERA in data (likely very limited sample, 2.4 IP). Both are basement-tier pitchers, but Freeland's 8.43 is catastrophic.
- Temperature 59.3F is COLDEST on entire slate (second-coldest TEX 89.7F) = major run suppression (~1.0-1.5 runs below normal). Density altitude 97 ft (extremely low) = balls don't carry, making park conditions even more suppressive.
- Model 10.09 total vs market 13.5 = 3.41 RUN GAP (largest on slate by far). This gap is COLD WEATHER + PITCHER CATASTROPHE combined.
- NRFI edge 8.4% (model 41.4%) is moderate; first inning suppressed by cold
- 48.6% edge is BEYOND OVERCONFIDENCE—this is model literally confident in own forecast without bounds
Risk Factors
- 48.6% edge is a WORLD-CLASS RED FLAG. No single game should have 48% edge. Our calibration system caps totals at 20% edge max. This violates every bound. HIGHEST ALERT.
- Joey Estes data quality UNKNOWN—appears to be TBD/young pitcher with 0 ERA in system (0 IP). If he's actually good, edge compresses 10-15 points.
- Cold weather 59.3F is transient; if game played later in day (unlikely at 2:06 AM UTC but possible), temperature could rise 10-15F and add 0.5-0.8 runs back to total.
HIGH EDGE WARNINGWEATHER IMPACTPITCHER QUALITYTOTALS VALUEDATA INTEGRITY
Edge Analysis
Moneyline
COL 50.5%
-20.2 pts
Run Line
-1.5
-20.2 pts
Total
13.5
+48.6 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 →