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Sunday’s Best MLB Algorithm Prediction, Odds: Best Value Edge on Sept. 4 Is for Nationals vs. Mets

Sunday’s Best MLB Algorithm Prediction, Odds: Best Value Edge on Sept. 4 Is for Nationals vs. Mets article feature image
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Photo by Mark Goldman/Icon Sportswire via Getty Images. Pictured: Washington Nationals third baseman Ildemaro Vargas

Washington Nationals vs. New York Mets Odds

Nationals Odds +250
Mets Odds -310
Over/Under 8.5 (-106 / -114)
Date Sunday, September 4
Time 1:40 p.m. ET
Channel MLB.tv

*Odds via FanDuel as of Sunday.

There is a historically profitable betting algorithm developed by the Action Network that has a substantial betting edge for today’s game between the Nationals and Mets.

The algorithm targets the moneyline and factors in injuries, matchups, conditions and more.

This edge provides about a 6% edge in betting value, which indicates the pick has implied odds that are more than 6% higher than the odds available to you in the markets.

Essentially, the line is currently at a 6% discount across the market.

Over the long run, if you only bet on lines with at least an 6% betting edge, you’re almost assured to make a profit. In the short term, of course, variance is a constraint.

This moneyline pick is the best MLB pick of the day, according to our models.

MLB Algorithm Prediction for Sunday

Odds and betting data updated as of 10 a.m. ET.

The models are rolling heavily with the Nationals moneyline as the best pick of the day.

The algorithm thinks their moneyline should be closer to +203. Meanwhile, the best price you can get on the market is with BetRivers at +270.

Remember, though, even small differences in lines make a big difference in the long run. The market-worst price of Nationals +240, for instance, saps about 2.4% of value from this betting edge — enough to downgrade this pick severely.

So make sure to line shop properly before placing a bet using our algorithms.

As aforementioned, this line gives you $6 of expected value for every $100 wagered over the long run, according to our algorithms.

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