A frequency distribution can be defined in betting terms as a grouping of data into intervals or categories that show the number of observations for each category. The total number of observations in each interval are called absolute frequencies. If you want to measure the proportion of each category or interval, you will use relative frequencies by dividing the absolute frequency by the number of total observations.

**Example**

Let’s assume that your sample consists of 50 football matches. There is no significant difference between the playing style and performance of the teams involved. If you find the absolute frequency for internal draw 18, for away 12 and for home 20 the percentage would be 18% for draws, 12% for away and 20% for home wins.

**Application**

The frequency distribution is used to simplify the data gathering process and to make the final product easily quantifiable. Moreover, the bettor will be able to see the trends and outcomes more clearly. It is a great tool for those who want to transform soccer stats to percentages and then the percentages to odds. Not only you can apply the frequency distribution to soccer stats, you can also apply it to any other sports and even to financial data. The possibilities are endless, you just have to decide how to set the categories, intervals and how to define your set of data. Once you define the data set, for example soccer teams that are likely to have similar results in the next seasons, you can come up with some unexpected results. However, this is not as simple as it sounds and you need to invest time into it.

We will use the example mentioned above to explain how to isolate cumulative relative frequency, the summation of the relative frequencies. If you want to find out a HU scenario and how often a team will meet the criteria, you can use this formula: (20%+18%)/50% = 76% chances for a home win or a draw. The relative frequencies are represented by 20% and 18%, and if we divide their sum on the total, the result is the bet scenario.

**The frequency distribution construction**

When you want to construct a distribution, the first step is to set up the categories or intervals. In case you have to work with percentage based intervals, allocate more time for data analysis so you can set the lowest interval a bit lower than your findings and the highest a bit higher. Make sure your categories or intervals are all mutually exclusive, so each data point fits one interval and the endpoints do not overlie. In order to do it correctly, do not include an upper limit equal to the lower limit on the interval.