New MLB 2025 thread after completing the first Doubling the Bankroll Cycle

@ChatBIN

I’m in Missouri… cardinal fan for life… played football game at Busch stadium when in college… was cool as fook for a 20 year old.  We had baseball locker room… I had Ozzie smith locker… it took everything I had not to take his mail… even one piece would have been cool as heck for us lifetime fans.    But I did not.  We lost game.   I say all this to say … I have friend that follows cards every move.. watches every game… and he told me earlier this year… cards gonna suck this year.    I played Mets here too… parlayed with Cleveland cavs and Celtics to win series.    

CIN Reds - WASH Nationals (being delayed and can be wagered for another 15-330 minutes at the least)  Under 8.5  +100  $150

TEX Rangers ML +105   $150

CIN Reds - WASH Nationals  Under 8.5  +100  $150   +$150

TEX Rangers ML +105   $150 -$150

STL Cardinals - NY Mets     NY Mets to win the series -140   $630   Pending. The Mets are ahead 1-0

PIT Pirates - SD Padres   SD Padres to win the series -150  $225    Pending. The Padres are ahead 1-0

1-1  (2 series bets are pending our picks leading in both 1-0 and 1-0)

67-76 overall

Current bankroll $7,079 - $855 (pending bets) = $6,224

This is a bankroll after doubling $5K into $10,391 and withdrawing $2,500.

 Later

TEX Rangers +115  $450      Under 8.5  -111   $167

LA Angels +150   $150     U 8.5  -115  $173

NY Yankees - TB Rays Game 2 Saga  :)

Based on the pitching matchup, bullpen strength, recent team performance, and betting lines for the May 3, 2025 game between the Tampa Bay Rays and New York Yankees, I’ll run a probabilistic simulation model (like a Monte Carlo simulation) approximating 10,000 games using the key data. Here's a summarized version of the most relevant data before simulating:

# Reuse team run distributions (from earlier simulations)

rays_probs = np.array([0.0109, 0.0383, 0.0759, 0.1256, 0.1622, 0.1667, 0.1463, 0.1034, 0.0585, 0.0316, 0.0177, 0.0101, 0.0061, 0.0037, 0.0023, 0.0014, 0.0008, 0.0005, 0.0003, 0.0002, 0.0001])

yankees_probs = np.array([0.0039, 0.0152, 0.0422, 0.0854, 0.1305, 0.1576, 0.1554, 0.1314, 0.0926, 0.0585, 0.0351, 0.0205, 0.0121, 0.0071, 0.0043, 0.0026, 0.0016, 0.0009, 0.0005, 0.0003, 0.0002])

import numpy as np

# Reuse team run distributions (from earlier simulations)
rays_probs = np.array([0.0109, 0.0383, 0.0759, 0.1256, 0.1622, 0.1667, 0.1463, 0.1034, 0.0585, 0.0316, 0.0177, 0.0101, 0.0061, 0.0037, 0.0023, 0.0014, 0.0008, 0.0005, 0.0003, 0.0002, 0.0001])
yankees_probs = np.array([0.0039, 0.0152, 0.0422, 0.0854, 0.1305, 0.1576, 0.1554, 0.1314, 0.0926, 0.0585, 0.0351, 0.0205, 0.0121, 0.0071, 0.0043, 0.0026, 0.0016, 0.0009, 0.0005, 0.0003, 0.0002])

# Possible run values from 0 to 20
runs = np.arange(21)

# Create full 10,000-game score simulation
simulations = 10000
rays_scores = np.random.choice(runs, size=simulations, p=rays_probs/rays_probs.sum())
yankees_scores = np.random.choice(runs, size=simulations, p=yankees_probs/yankees_probs.sum())

# Calculate runline outcomes
nyy_minus_1_5 = np.mean((yankees_scores - rays_scores) > 1)
rays_plus_1_5 = np.mean((rays_scores - yankees_scores) >= -1)

# Calculate average score
avg_rays = np.mean(rays_scores)
avg_yanks = np.mean(yankees_scores)

nyy_minus_1_5, rays_plus_1_5, avg_rays, avg_yanks

# Possible run values from 0 to 20

runs = np.arange(21)

# Create full 10,000-game score simulation

simulations = 10000

rays_scores = np.random.choice(runs, size=simulations, p=rays_probs/rays_probs.sum())

yankees_scores = np.random.choice(runs, size=simulations, p=yankees_probs/yankees_probs.sum())

# Calculate runline outcomes

nyy_minus_1_5 = np.mean((yankees_scores - rays_scores) > 1)

rays_plus_1_5 = np.mean((rays_scores - yankees_scores) >= -1)

# Calculate average score

avg_rays = np.mean(rays_scores)

avg_yanks = np.mean(yankees_scores)

nyy_minus_1_5, rays_plus_1_5, avg_rays, avg_yanks

Result

(0.4291, 0.5709, 5.1032, 6.0334)

Based on 10,000 simulated outcomes using team scoring distributions:

• Runline Bets:
• Yankees -1.5: Wins 42.91% of the time
• Rays +1.5: Wins 57.09% of the time
• Average Simulated Score:
• Rays: 5.10 runs
• Yankees: 6.03 runs

Here are the results from the 10,000 simulations based on your provided data:

? Moneyline Probabilities

• Yankees win (ML -185): 56.57%
• Rays win (ML +155): 43.43%

Implied odds suggest value might exist on the Rays, since +155 implies ~39.22%, but simulations show 43.43%.

Total Runs (Over/Under 9.5)

• Over 9.5 (-110): 54.94%
• Under 9.5 (-110): 45.06%

Simulated average total runs: 11.13, which favors the Over on current lines.

NY Yankees - TB Rays Game 2  Over 9.5  -111  $167

HOU Astros - CHI White Sox  Under 8  -111   $167

HOU Astros -1.5  -140   $210
BOS Red Sox-1.5  +110  $150
ATL Braves ML  +100  $150

TEX Rangers +115  $450      Under 8.5  -111   $167

LA Angels +150   $150     U 8.5  -115  $173

NY Yankees - TB Rays Game 2  Over 9.5  -111  $167

HOU Astros - CHI White Sox  Under 8  -111   $167

HOU Astros -1.5  -140   $210

BOS Red Sox-1.5  +110  $150

ATL Braves ML  +100  $150

Mods, please. This is very annoying: I am posting and nothing. Then I am reposting and I get double posts. Please fix

I posted all the picks together to avoid confusion  /\

CIN Reds -1.5  +105  $150
SF Giants -1.5 -130 $212

Started 0-3 I think.   I tailed …  typical my luck .. I r robin them all fer a few hundred total.   Go late games.  

TEX Rangers +115  $450      Under 8.5  -111   $167

LA Angels +150   $150     U 8.5  -115  $173

NY Yankees - TB Rays Game 2  Over 9.5  -111  $167 

HOU Astros - CHI White Sox  Under 8  -111   $167 

HOU Astros -1.5  -140   $210 

BOS Red Sox-1.5  +110  $150 

ATL Braves ML  +100  $150