How To Predict Losses When Playing Casino Games

We cannot deny the fact that online casino games and all gambling-related games are a gamble. There is always the chance that you will either win or lose the game. Most of us usually wait for that to happen and we play. But today we have a simple way on how you can calculate your loses when you play online casino games. Don’t worry we promise that there is not is too much mathematics involved. Now just give us a few minutes as we give you a simple method on how to predict loses when playing online casino games.

 Know the RTP and the House Edge

So, in order to know how to predict losses when playing casino games, you will need to know the RTP or the house edge. These re opposite terms but they are

How To Predict Losses When Playing Casino Games

How To Predict Losses When Playing Casino Games

closely related. The RTP is the return to player percentage, the amount of real money that the casino pays back to the players when they win. And then the house edge is the casinos’ way of making money.

Basically, if you come across an online casino with an RTP of 96.54% this means that the house edge 3.46% percentage. Therefore to get the house edge, you simply need to subtract the RTP from 100%.

How to Predict The Losses

Now that we have these two, the next step that we want is the one that involves predicting the looses. Therefore, what you need to do in order to predict the losses is simple. You have to multiply the size of your bet by the number of bets that you are going to make.

If it was a formula it would look like this:

House edge percentage X average bet size X number of bets = Expected looses.

Let’s put that in a more practical way.

If you are playing online blackjacks with n RTP of 95%, this makes the house edge 5%. You place a bet size of $10 and you place 15 bets every hour for at least 4 hours. Your expected loses would look like this:

5% x $10x (15×4) =$30


There you have it, a simple way that you can use to predict your loses when playing casino games.

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