MLB Baseball

SD vs PIT Prediction

April 8, 2026

10,000 Monte Carlo simulations

FINAL: PIT 2 — SD 8. Our Monte Carlo simulation projected PIT 4.3 - SD 3.2 (PIT at 62.3% win probability). The run line is 1.5 and the total is 7.0. Model projects 7.5 total runs.

PIT
4.3
Projected Score
VS O/U 7.0
SD
3.2
Projected Score
Win Probability
62.3%
37.7%
PITSD
+1.5
Run Line (PIT)
7.0
Total Line
10,000
Simulations
SD W4PIT
Calibrated accuracy at this confidence: 65.3% (2,040 games)

Projected Runs Range 10th – 90th percentile

SD
135
PIT
246
FINALPIT 2 — SD 8
Projected
PIT 4.3 — SD 3.2
Actual
PIT 2 — SD 8

Pick Results

PIT MLmlLOSS-1.00u

Starting Pitcher Matchup

Michael King R
SD
SI29%93 mph14% whiff
FF23%94 mph32% whiff
CH21%87 mph26% whiff
Mitch Keller R
PIT
FF31%93 mph3% whiff
SI28%92 mph3% whiff
ST22%82 mph26% whiff

Weather Impact

PNC Park
50°F8 mph wind
HR: 1.029 Total: 1.018
7mph out

Bullpen Comparison

SD
4.02ERA
2.95FIP
8.18K/9
3.09BB/9
1.29WHIP
PIT
3.29ERA
4.33FIP
12.14K/9
5.02BB/9
1.31WHIP

Betting Edges

RUN_LINE AWAY -1.5
-32.5% EV
+146
F5_ML AWAY
-25.9% EV
-130
ML AWAY
-23.5% EV
-122
RUN_LINE HOME +1.5
-22.4% EV
-179
F5_ML HOME
+21.4% EV
+104
ML HOME
+20.3% EV
+104

First 5 Innings & NRFI

SD F5
1.7 runs
30.6% win
PIT F5
2.6 runs
52.1% win
F5 Total
4.2
NRFI
54.6%
YRFI
45.4%
Avg 1st Inn Runs
0.90

HR Spotlight

Avg HRs
2.0
Over 0.5 HR
86%
Over 1.5 HR
59%
No HR
14%
Brandon Lowe PIT50.0%
ISO: 0.171 | Barrel: 17.1% | vs Michael King | Park: 0.95x Platoon: 1.12x
Ryan O'Hearn PIT32.4%
ISO: 0.175 | Barrel: 17.5% | vs Michael King | Park: 0.95x Platoon: 1.12x
Oneil Cruz PIT28.1%
ISO: 0.170 | Barrel: 17.0% | vs Michael King | Park: 0.95x Platoon: 1.12x

Pitcher Strikeout Projections

Michael King
0.0 K projected
SD | K/9: 0.0
Mitch Keller
0.0 K projected
PIT | K/9: 0.0

Injury Report

SD8 injured
Yuki Matsui RP15-DAY-IL
Griffin Canning SP15-DAY-IL
Jason Adam RP15-DAY-IL
Joe Musgrove SP15-DAY-IL
Sung-Mun Song 3B10-DAY-IL
Matt Waldron SP15-DAY-IL
+2 more
PIT5 injured
Chris Devenski RPDAY-TO-DAY
Jared Triolo SS10-DAY-IL
Oddanier Mosqueda RPDAY-TO-DAY
Jared Jones SP60-DAY-IL
Anthony Solometo SPDAY-TO-DAY

AI Intelligence Analysis

NEUTRAL -2GREEN ZONE55.1% WR (n=258)
Model predicted PIT (62.3% win prob) but SD won 8-2 decisively — DATA_INTEGRITY flag: Mitch Keller (1.62 ERA, 8.0 K/9, C grade stuff) should NOT be favored over Michael King (3.65 ERA, 8.0 K/9, B- grade) by 24.6 points. Post-game analysis reveals model failed to weight Keller's low ERA as outcome-dependent rather than predictive; SD's lineup exploited his poor stuff grade (0.079) in cold weather (49.5F, run-suppressing conditions).

Key Factors

  • Pitcher mismatch ignored: Keller 1.62 ERA but C grade stuff (0.079), King 3.65 ERA with B- overall (0.531) — stuff grade predicts performance better than ERA in cold conditions
  • Model overconfidence: 20.3% ML edge (59% vs 49% market) in unfamiliar pitcher pairing — high edge + unfamiliar combo = historical failure zone
  • Cold weather (49.5F) suppresses runs by ~0.5 per sim, but market totals at 7.0 vs model 7.49 — market was correct on run environment
  • Bullpen context: SD bullpen fresher after earlier win; PIT bullpen depleted after back-to-back road games
  • Result validation: SD won 8-2, total 10 runs — model predicted under but got over due to King's early exit and SD's sustained offense

Risk Factors

  • High-edge home favorites (20%+) in 55-60% prob range historically 55.1% WR — this loss was expected variance, not model failure (it fell in GREEN zone)
  • Pitcher career stats (Keller's 1.62 ERA) masked recent regression and poor stuff grades — model did not sufficiently discount small-sample ERA gains
  • Cold weather conditions (49.5F) typically suppress totals, but model still predicted 7.49 total when market set 7.0 — slight error in weather weighting
DATA INTEGRITYHIGH EDGE WARNINGPITCHER MISMATCHMODEL MARKET CONFLICT

Edge Analysis

Moneyline
PIT 62.3%
-22.4 pts
Run Line
+1.5
-22.4 pts
Total
7.0
--
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 →

Want Premium Predictions?

Get full access to all picks, detailed game-by-game analysis, and Kelly-optimized unit sizing across 9 leagues.

Go Premium Free Picks