Did you know that as a bettor standard deviation can be used to predict the outcome of bets? Well it can, but you will need to do a little more research on the standard deviation and how it’s calculated and then attempt to apply it to your betting methods. It is also important for bettors not to rely on the average only due to its inability when showing dispersions within the set of numbers. As mentioned on Wikipedia, measured betting is applying mathematics to gambling and that can be beneficial in the long run.
There are different ways to bet on rugby for example, some players are known to use the Poisson method, which is used to predict the amount of goals that were scored for each team in a game. This distribution however will provide only one input parameter, which is the average with the discrete distribution that will give whole numbers for produced outputs. Choosing a good site online is another critical element if pokies are a area of interest. PokieCasino.com is one of those and have taken time to put together a list of Rugby and Pokie games that are based on the sport.
The model with Poisson distribution can provide the possibilities of scoring one goal in the game instead of measuring what the chances are of scoring a goal during a certain period in the game like between the 20th and 25th minutes of the game. The method will however be able to derive these as well. Gaussian or bell distribution is known as the normal distribution, which is also very popular. This method provides continuous distribution, which makes it very different to the Poisson method mentioned above. There are many reason that make the 2 different as it’s based on 2 parameters consisting of the standard and average deviations.
Let’s have a look at how the prediction for goals spread would work in a Premier League. As the test we will look at the goal difference in a soccer game. The goal difference for each match can be seen as normal distribution, which is the different between the amounts of goals scored by the home team, subtracting the amount of goals scored by the visitors. Should the amount be zero it will be considered a draw. The data used has been captured over the 2013/2014 matches where Man City obtained a record of 7-0 against Norwich for the biggest home win. The biggest away score over this period was a 5-0 win to Liverpool against Tottenham. This means a difference of 0.3789 will be the average when you measure the mode and median, which leaves the standard deviation at 1.9188.
To calculate the standard deviation you will need to use the 2 parameters standard and average curve, which will provide a standardised curve. This will show that in the standard deviation holds around 68% of the distributions, which is away from the mean, and within the 2 standard deviations 95% can be found. With this we are expecting 68% of the games will come to an end between -1.5399 & 2, 2977 goals scored. There are not limitations with the curve due to the continuous nature; a difference of -1.5399 is not possible.
To work out the estimate the difference by 1 for a home game, the 1 will need to be moved from a whole value of 1, which will then provide the continuous ranging from 0.5 to 1.5.