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Published Jun 5 8:46am ET
Moneyline

Blue Jays To Win -125

Lost: 4-11

Astros at Blue Jays

Mon Jun 5 • 7:07pm ET

More info

How it wins: Toronto wins the game against Houston on Monday.

Staff notes:

  • This is a playable model pick and one of our top plays for Monday by our Similar Games Model.
  • The pitching matchup is Alek Manoah of Toronto and Brandon Bielak of Houston. 
  • Over the last three years, our top Similar Games Model picks have been our most profitable model plays, returning solid ROIs in each season, and we will be highlighting those in Staff Picks. 

Pick published: Jun 5 8:46am ET, available at that time at BetMGM.

Rot# 906

About Our Staff Picks

We created the Staff Betting Picks feature to address several opportunities to provide more value to our subscribers:

  • BetIQ and TeamRankings offer a LOT of predictions and data, but it’s not fast or easy to parse through it all. Some subscribers just want to see a short list of our top/favorite picks.
  • Many bettors enjoy reading the rationale behind a recommended pick, as opposed to blindly trusting “because the model said so” as the reason.
  • Our algorithmic models for NFL and college football make predictions for full-game point spread, over/under, and moneyline bets. However, there’s a lot more to bet on than that.
  • Although bet size limits tend to be lower for markets like props and futures, those types of markets sometimes offer some of the biggest edges.
  • Our model predictions often change as they digest new data such as betting line movement and new game results. That approach has a lot of benefits, because the predictions shown always reflect the most up-to-date data we have. However, some subscribers just want to see a pick that doesn’t change.
  • As we do research on teams and players, sometimes we see a situational or one-time angle on a bet that we are confident provides expected value, and that angle may not be something that our models are well trained to pick up. Models typically need a lot of historical data to work well, and deep historical data simply doesn’t exist for situations that are less common (e.g. quirky injuries or weather or another more creative angle).

Some (and potentially the majority) of our Staff Picks will be drawn from top-rated model picks, but we’ll explain the data angle(s) that our models are likely seeing. Other Staff Picks may not be even be favored by our models, but we’re making a judgment call to overrule them.

Finally, some Staff Picks will be bets like player props and futures that our game models don’t currently cover, or more market-based value opportunities that we see (e.g. an off-market line offered by a particular sportsbook).

For each pick we make, we will note the sportsbook that offered it, and the associated line/payout odds at the time when we published it.