MLB Baseball

LAA vs TB Prediction

May 30, 2026

10,000 Monte Carlo simulations

LAA vs TB prediction for May 30, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects TB 4.5 - LAA 4.1. TB is favored with a 55.8% win probability. The run line is -1.5 and the total is 7.0. Model projects 8.6 total runs.

TB
4.5
Projected Score
VS O/U 7.0
LAA
4.1
Projected Score
Win Probability
55.8%
44.2%
TBLAA
-1.5
Run Line (TB)
7.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.9% (2,300 games)

Projected Runs Range 10th – 90th percentile

LAA
246
TB
346
FINALTB 3 — LAA 14
Projected
TB 4.5 — LAA 4.1
Actual
TB 3 — LAA 14

Starting Pitcher Matchup

Reid Detmers L
LAA
FF43%94 mph18% whiff
SL31%86 mph32% whiff
CH12%83 mph29% whiff
Drew Rasmussen R
TB
FC35%90 mph21% whiff
FF27%96 mph21% whiff
SI20%96 mph13% whiff

Weather Impact

Tropicana Field
86°F11 mph windRoof: closed
HR: 1.000 Total: 1.000
Indoor (neutral)

Bullpen Comparison

LAA
4.50ERA
4.77FIP
9.00K/9
5.13BB/9
1.44WHIP
TB
4.13ERA
3.82FIP
8.29K/9
3.40BB/9
1.30WHIP

Betting Edges

RUN_LINE AWAY +1.5
-39.9% EV
-169
TOTAL UNDER 7.0
-26.6% EV
-106
F5_ML HOME
-9.8% EV
-156
ML HOME
-8.4% EV
-161
RUN_LINE HOME -1.5
-5.8% EV
+140
F5 OVER 3.5
+5.3% EV
-110

First 5 Innings & NRFI

LAA F5
2.1 runs
38.6% win
TB F5
2.4 runs
45.2% win
F5 Total
4.6
NRFI
52.4%
YRFI
47.6%
Avg 1st Inn Runs
0.96

HR Spotlight

Avg HRs
2.3
Over 0.5 HR
90%
Over 1.5 HR
68%
No HR
10%
Zach Neto LAA28.6%
ISO: 0.214 | Barrel: 10.2% | vs Drew Rasmussen | Park: 0.92x
Mike Trout LAA27.6%
ISO: 0.247 | Barrel: 14.1% | vs Drew Rasmussen | Park: 0.92x
Wade Meckler LAA22.1%
ISO: 0.157 | Barrel: 15.7% | vs Drew Rasmussen | Park: 0.92x Platoon: 1.12x

Pitcher Strikeout Projections

Reid Detmers
0.0 K projected
LAA | K/9: 0.0
Drew Rasmussen
0.0 K projected
TB | K/9: 0.0

Injury Report

LAA7 injured
Nolan Schanuel 1B10-DAY-IL
Ben Joyce RP60-DAY-IL
Yusei Kikuchi SP60-DAY-IL
Yoan Moncada 3B10-DAY-IL
Travis d'Arnaud C10-DAY-IL
Robert Stephenson RP60-DAY-IL
+1 more
TB8 injured
Manuel Rodriguez RP60-DAY-IL
Steven Wilson RP60-DAY-IL
Ben Williamson 2BDAY-TO-DAY
Edwin Uceta RP60-DAY-IL
Michael Grove RP60-DAY-IL
Gavin Lux LF60-DAY-IL
+2 more

AI Intelligence Analysis

NEUTRALYELLOW ZONE56.6% WR (n=173)
Detmers (B grade, 10.7 K/9) significantly outpitches Rasmussen (B-, 8.3 K/9), creating pitcher mismatch to LAA. Market overprices TB at -161 (61.7% vs 56.5% true). However, LAA as away dog is in RED zone (43.4% WR). Model edge (~3.5%) is insufficient to overcome away RED zone bias.

Key Factors

  • Starting pitchers: Drew Rasmussen (TB, B-, 8.3 K/9, excellent command 0.678 grade) vs Reid Detmers (LAA, B, 10.7 K/9, 0.586 command). DETMERS HAS SUPERIOR K RATE (+2.4 K/9) and B grade overall. This is MISMATCH TO LAA (away). Detmers is elite K pitcher.
  • Market has TB at -161 (61.7%) vs model 56.5%. Model shows LAA away underdog at 43.5% but edge actually favors LAA by 3.5pp on model (56.5 vs 61.7). Sharp money against TB.
  • Tropicana Field is DOMED (neutral climate), park factor 1.0. No weather advantage. Temperature irrelevant (indoor 72°F controlled).
  • LAA is strong team (33-27, 3rd AL West), TB is solid (32-28, 1st AL East). LAA lineup: Trout, Neto, Rendon (out on IL). TB lineup: Strong defensively.
  • Over edge only 1.1% — minimal totals value.

Risk Factors

  • LAA as away dog at -151 (market) is in RED zone historically (43.4% WR). Even with pitcher advantage (Detmers 10.7 K/9), away RED zone blocks edge.
  • Market is overpricing TB (-161) vs model (56.5%). If sharp money is right, TB may be legitimate favorite despite pitching disadvantage.
  • Model shows LAA edge but not enough to overcome away RED zone bias historically (would need >10% true edge to justify away dog).

Edge Analysis

Moneyline
TB 55.8%
-5.8 pts
Run Line
-1.5
-5.8 pts
Total
7.0
+1.1 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 →

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