Best Bets Today: How to Use Our Dimers Pro Data

Whether you’re new to Dimers Pro or have been a longtime user, you’ve likely spent some time in our Best Bets and Best Props pages looking for insights. Within these pages, as well as our game-by-game predictions for every sport, you’ll find three main datapoints for evaluating bets – Probability, Edge and Projection.

Each mark represents a different aspect of the results predicted by our analytics models and all three have a different function in identifying worthwhile betting opportunities across Dimers and at the best sportsbooks available to you.

Understanding how and when to use these different datapoints is just as important as having access to the hundreds of bets, projections and edges identified by Dimers Pro. A bettor’s tools are only as useful as their understanding of those tools, just like any statistic or metric used to analyze any given wager.

For that reason, we’ve compiled this brief guide with a look at each of the primary metrics curated by our in-house predictive analytics models.

1) Understanding Probability

The probability found in our best bets, props futures and other various predictions across Dimers Pro represents the likelihood of that particular result occurring.

Example: The top prop in our page is for Jayson Tatum of the Celtics to go Over his 24.5 Points line. You see that the probability for this particular prop reads 62.9% - this means that after running 1000s of simulations on this game, our model known as the DimersBOT sees Jayson Tatum clearing this line in 62.9% of those simulations.

Because this prop is an over/under with only two possible results, that also means Tatum has a 37.1% probability of finishing with the opposite result and going under 24.5 points.

As probability represents the likelihood of a particular result occurring, we can use this datapoint to find the most likely bets that our model predicts will hit on any given night. This is a good way to identify legs for parlays, as you’re less concerned with getting value and are more concerned with a high probability.

It’s important to note that higher probabilities are not guarantees – even a 70% probability means that 3 in 10 times, the other results occurs.

2) Understanding the Edge

The second datapoint we use that is represented by a percentage is our “edge.” Even though the higher an edge, the better that is, this is still different from probability.

While probability is a reflection of the chance a result occurs, the edge is used to represent the value in any given bet, meaning is the bet worth taking at the odds available?

Also known as “expected value” or “EV,” this measure the gap between our probability and the sportsbooks’ probability, which in turn is represented by the odds.

Odds are just a different way of displaying probability; odds of +100 mean 50%, while odds of -200 mean 66.6% and odds of +200 represent a 33.3% implied probability. Every bet’s odds represents a probability and the longer the odds (higher plus-money number), the smaller the probability.

So what’s the point of an edge? Well, to find out where the sportsbooks are wrong!

Example: Matt Olson of the Braves is +375 to hit a home which implies a 21% probability, however, the DimersBOT has found that Olson in fact has a 27% probability to hit one out of the park, meaning he shouldn’t be any better than +270, and thereby identifying a profitable opportunity and an edge of about 6%!

Conversely, you wouldn’t pay $100 for the exact same item that should only cost $75, so why would you do it when betting? If the odds for Matt Olson to hit a home run are +300 (25%) but we say he has just a 20% probability of hitting a home run, then that means he should actually be +400 and you should look for those odds.

When betting on single bets, finding higher edges is crucial to turning a long-term profit as the sportsbooks will overcharge you (unfair odds) at every opportunity.

3) Understanding Projections

The final datapoint we use is our model’s projections for individual stats and props. Projections are the average end-of-game statistics calculated by our models’ simulations.

If a player is projected for 24.0 points, that’s what they averaged for that individual stats over total of all our simulations; sometimes they may go over and sometimes under, but his is their average (think points per game).

Projections can be used in tandem with our probability to find the strongest betting opportunities amidst the hundreds of bets you'll find inside Dimers Pro.

Example: Two players have a similar probability to go over their assists prop, like Tyrese Haliburton (62%) and Luka Doncic (64%). They might even both have an edge, meaning they’re strong bets. But if you’re trying to pick just one, you can then take a look at their projections for a deeper look.

Let’s say Haliburton’s line is 10.5 and he’s projected for 13.7 assists, while Luka’s line is 9.5 and he’s projected for 11.7. While they both have a similar probability, Haliburton’s projection is stronger, a full 3 assists over his line.

Not only does this reveal that Haliburton is likely the better bet as he has a higher ceiling, but also identifies potential alternate lines, like betting on his 12+ assists at longer odds for plus-money.

We have projections for most stats that accumulate – points, rebounds, assists for NBA for example, and passing, receiving and rushing yards for the NFL. For prop markets that are primarily a singular over/under like hits and home runs in MLB, we do not have projections at this time.

Using All Three Datapoints

As explained, each of our three datapoints has a different use and each can identify various opportunities. Finding a bet that lands in all three or at least hits on 2/3 categories typically means you’ve found what our model would identify as a “best bet.”

Nothing is ever a sure thing in betting, but by using multiple sources and cross-referenced metrics like Dimers Pro probabilities, projections and edges, you can ensure you’re making the most educated bets, which can lead to a long-term winning formula.