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A Guide To Sports Betting Stats

Most sport bettors spend countless hours crunching betting stats so they find the secret information hidden behind the numbers. Sports betting stats come in numerous forms, ways and shapes. If you want to be able to read and make sense of them, you will need a set of tools. Statistics is among the most important skills you need in order to be a successful bettor. This article will introduce you to sport betting stats and give you some useful tips.

The importance of sports betting stats

The main reasons why we need sports betting stats is to forecast and summarize the bookmaker’s odds. The bookies present the early odds based on statistics, as an attempt to predict the future. Based on the general statistics theory, we must be able to analyze a set of data and come to a meaningful conclusion about the central tendency (where the data is centered). Moreover, we want to make conclusions about the dispersion (how dispersed from the center the data is), to find out if the distribution is symmetrical or lopsided and to discover the likelihood of an extreme outcome (kurtosis).

Types of stats

According to the general statistics theory, there are two categories of statistics: inferential statistics and descriptive statistics. Descriptive sport betting stats are used to analyze the data in order to show, summarize or describe it in a meaningful way. If you are working with a large set of data, it is important to discover the important information and describe it by consolidating the numerical data into readable info. Descriptive statistics allows a simpler, more meaningful interpretation. For example, we can use it to find the average number of home wins in a soccer league by taking the available soccer stats.

However, descriptive stats do not allow us to draw conclusions beyond the data we have analyzed. In order to reach a conclusion, we must use inferential sports betting stats that analyze a larger set of data based on the traits of a smaller group. This way bettors can make estimates, judgments and forecasts about a whole population they want to investigate with only a limited number of data. For example, inferential stats can be used if you want to bet on Spanish soccer and you need to find out what the odds are on the top 2 teams in the Primera Division when they play against a team placed between number 3 and 17 in the league. Instead of analyzing 20 years of soccer stats, you can take the data from the last 3 years.

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