John Ryan
MLB | May 16
New York Mets vs. San Francisco Giants San Francisco Giants +150
Ai Simulator 3* graded play on the SF Giants over the NY Mets slated to start at 4:10 EST. Supporting this graded play is a strong system that has posted a 30-11 making 26.2 units since 2003. Play on home dogs with a money line of +100 or higher starting a pitcher who did not walk a hitter last outing facing an opponent with a hot starting pitcher WHIP <= 1.000 over his last 5 starts. SF is a solid 119-102 (+49.3 Units) against the money line vs. an NL starting pitcher whose WHIP is 1.150 or better since 1997. Here again, this angle exemplifies the principals of the AiS neural network methodology. It is just 17 games over 500 win percentage, but has made 49.3 units in profits. I often use the Black Jack analogy for these powerful reinforcing systems and angles. Imagine playing BJ and getting paid $1.40 for every $1.00 winning wager. We would not sleep if that were the case, but it is a reality with my research. Take SF.
MLB | May 16
New York Mets vs. San Francisco Giants San Francisco Giants +150
Ai Simulator 3* graded play on the SF Giants over the NY Mets slated to start at 4:10 EST. Supporting this graded play is a strong system that has posted a 30-11 making 26.2 units since 2003. Play on home dogs with a money line of +100 or higher starting a pitcher who did not walk a hitter last outing facing an opponent with a hot starting pitcher WHIP <= 1.000 over his last 5 starts. SF is a solid 119-102 (+49.3 Units) against the money line vs. an NL starting pitcher whose WHIP is 1.150 or better since 1997. Here again, this angle exemplifies the principals of the AiS neural network methodology. It is just 17 games over 500 win percentage, but has made 49.3 units in profits. I often use the Black Jack analogy for these powerful reinforcing systems and angles. Imagine playing BJ and getting paid $1.40 for every $1.00 winning wager. We would not sleep if that were the case, but it is a reality with my research. Take SF.
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