When you want to learn how to interpret sports betting stats, you must start with the general terminology, move on to the differences between population and sample, and only then focus on the more complicated aspects such as frequency distribution. In this article, we will teach you the meaning of frequency distribution as well as its purpose.

Frequency Distribution in Sports Betting Stats Picture*Basic concept*

In the world of **sports betting stats**, frequency distribution is an organizing tool which allows you to group relevant information in categories so that you can highlight the important observations of each category. The categories must be mutually exclusive in order for the data to be relevant. There are two main types of frequency: absolute and relative. A relative frequency distribution measures the proportion of the categories or the intervals that you are considering.

*How to use it*

In order for you to understand the importance of frequency distribution, we will consider a simple example. Lets assume that you are using a sample of 100 soccer games of a few teams which you consider to be equally skilled and you have the following distribution: 37 home wins, 34 draws and 29 away wins. In this scenario, the absolute frequency for the home interval is 37, for the draws it is 34 and for the away games it is 29. On the other hand, the relative frequencies are 37%, 34% and 29%.

*Why should you use frequency distribution?*

The example presented above is very basic which is why you may not understand the purpose of frequency distribution since the data is quite relevant. However, if you are working with more complex statistics, you can use this tool in order to simplify the process of gathering information. Another important purpose of using frequency distribution is that you can make the results of your statistic analysts easier to measure and you will have a clearer perspective of the outcome or of certain trends. This tool can be used in all possible situations. It is the ultimate organizing tool and it is even used by financial analysts in order to determine market trends. All you have to do is put some effort into defining the groups of information. Usually, the bigger your sample or population is, the more complex the frequency distribution is going to be.

*Cumulative relative frequency*

This is a very simple concept in **sports betting stats** but it can be very useful in sports analyzes. In order to isolate the cumulative relative frequency, all you have to do calculate the total of all the relative frequencies. Lets say, that in the example presented above, we want to determine if a team meets our criteria. The formula we use is: (37%+34%)/100=71%. This is the chance for home, wing or draw. The 37% and the 34% represent the point to the relative frequencies of home wins and draws. Our purpose is to divide the sum of the relative frequencies on the total.

*Conclusion*

As we have already covered all the aspects of frequency distribution, we will make a résumé so that you can grasp the concept more easily. The first thing that you need to do is set up your categories or intervals. Next, you must check to see if the categories and intervals are mutually exclusive. This means that each piece of data must fit only one category or interval. Furthermore, the endpoints of the intervals mustn’t overlap each other. An upper limit should not be equal to the lower limit of the next interval.