PHI vs SD prediction for May 25, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects SD 3.2 - PHI 3.8. PHI is favored with a 55.9% win probability. The run line is 1.5 and the total is 7.5. Model projects 7.0 total runs.
SD
3.2
Projected Score
VS
O/U 7.5
PHI
3.8
Projected Score
Win Probability
SDPHI
+1.5
Run Line (SD)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.9% (2,300 games)
Projected Runs Range 10th – 90th percentile
PHI
246
SD
135
Projected
SD 3.2 — PHI 3.8
Actual
SD 0 — PHI 3
Starting Pitcher Matchup
Jesús Luzardo L
PHI
ST36%86 mph47% whiff
FF26%97 mph13% whiff
CH22%86 mph40% whiff
Griffin Canning R
SD
CH33%90 mph33% whiff
FF24%95 mph26% whiff
SL22%88 mph43% whiff
Weather Impact
PETCO Park
70°F11 mph wind
HR: 0.980 Total: 0.987
7mph in
Bullpen Comparison
PHI
4.48ERA
3.35FIP
10.21K/9
3.24BB/9
1.37WHIP
SD
3.26ERA
3.48FIP
8.35K/9
3.50BB/9
1.22WHIP
Betting Edges
RUN_LINE HOME +1.5
-43.0% EV
-161
TOTAL OVER 7.5
-18.5% EV
-104
TOTAL UNDER 7.5
+8.1% EV
-118
RUN_LINE AWAY -1.5
-7.7% EV
+132
ML HOME
-6.8% EV
+110
F5 UNDER 4.5
+6.4% EV
-141
First 5 Innings & NRFI
PHI F5
2.1 runs
46.4% win
SD F5
1.7 runs
34.8% win
F5 Total
3.9
NRFI
63.8%
YRFI
36.2%
Avg 1st Inn Runs
0.70
HR Spotlight
Avg HRs
1.5
Over 0.5 HR
78%
Over 1.5 HR
44%
No HR
22%
Kyle Schwarber PHI29.0%
ISO: 0.387 | Barrel: 20.0% | vs Griffin Canning | Park: 0.90x Platoon: 1.12x
Bryce Harper PHI26.1%
ISO: 0.300 | Barrel: 14.5% | vs Griffin Canning | Park: 0.90x Platoon: 1.12x
Bryson Stott PHI17.1%
ISO: 0.168 | Barrel: 8.3% | vs Griffin Canning | Park: 0.90x Platoon: 1.12x
Pitcher Strikeout Projections
Jesús Luzardo
0.0 K projected
PHI | K/9: 0.0
Griffin Canning
0.0 K projected
SD | K/9: 0.0
Injury Report
PHI8 injured
Kyle Backhus RP15-DAY-IL
Zach Pop RP15-DAY-IL
Keaton Anthony 1BDAY-TO-DAY
Johan Rojas CFSUSPENSION
Rene Pinto CDAY-TO-DAY
Daniel Robert RPDAY-TO-DAY
+2 more
SD8 injured
Matt Waldron SP15-DAY-IL
German Marquez SP15-DAY-IL
Luis Campusano C10-DAY-IL
Jhony Brito RP60-DAY-IL
Yu Darvish SPOUT
Blake Hunt CDAY-TO-DAY
+2 more
AI Intelligence Analysis
LEANYELLOW ZONE50.0% WR (n=285)
Model projects 7.02 total runs (UNDER 7.5, +8.1% edge, 58.4% model prob). Pitcher matchup slightly favors PHI (Luzardo B, 0.607 grade vs Canning B-, 0.456 grade), but both are solid arms (both ~10.7-10.8 K/9). Edge comes from weather: Petco Park (0.9 park factor, -10% run suppression) + wind 11.2 mph IN (additional suppression) + temp 69.6F (slightly cool). Model projects 7.02 runs; market at 7.5 = modest undervalue of run environment (0.48 run gap). UNDER bet requires discipline (totals disabled, grade F), but weather + park factor stack supports play.
Key Factors
- Park factor + weather stack: Petco (0.9 multiplier) + wind 11.2 mph IN (0.987 multiplier per system) + temp 69.6F (slightly cool, minor suppression) = cumulative ~0.89 run multiplier vs neutral baseline. Model 7.02 baseline vs market 7.5 = 0.48 run undervalue.
- Pitcher profile: Luzardo (B, 0.607 grade, 10.8 K/9) is solid premium arm; Canning (B-, 0.456 grade, 10.7 K/9) is comparable. Both have excellent K-rates; likely low-run environment per league trends. No dramatic SP-driven scoring advantage either direction.
- NRFI probability 61.0% (model) — elevated first-inning scoring resistance supports under lean.
- Historical: Both teams play in/near Petco and Oracle (marine layer parks). Expect lower-scoring matches in NL West coastal matchups.
Risk Factors
- TOTALS DISABLED (grade F, 44.9% WR). Extreme caution on ALL total edges, even 8.1%. This is hard filter; consider skipping due to calibration risk alone.
- Edge 8.1% is modest; combined with disabled totals status, risk/reward is poor. Market may be accurately pricing Petco run environment despite park factor.
- Underdog PHI value play (+129 away) may be more interesting (55.6% model prob) than chasing total edge.
WEATHER IMPACTPARK FACTORTOTALS VALUE
Edge Analysis
Moneyline
PHI 55.9%
-43.0 pts
Run Line
+1.5
-43.0 pts
Total
7.5
+8.1 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 →