Introduction to sports betting stats

Sports betting stats come in many shapes, ways and forms. Most of us who are interested in sports betting have spent countless hours grinding football stats to find information hidden in those numbers. This article series is all about presenting a set of tools, enabling you to read sports betting stats and make sense of it. As we have mentioned in numerous other write ups, statistics is one of the most important skills you need to master in order to bet on sports with any kind of success.

What do we need sports betting stats for?

We need sports betting stats to summarize or forecast betting odds. The bookmakers often present their early odds pretty much based on statistics alone. These betting odds are the bookmaker’s current best attempt at forecasting the future.

In general statistics theory we also want to be able to look at a set of data and make meaningful conclusions about where the data is centered (central tendency). We want to make conclusions about how dispersed from the center the data is (dispersion).  Furthermore we want to find out if the distribution of data is lopsided or symmetrically shaped, and finally we would like to know if extreme outcomes are likely (kurtosis).

Types of betting stats

In general statistics theory we operate with two types of categories of statistics. These are; descriptive and inferential.

Descriptive sports betting stats

Descriptive sports betting stats provide summarized information about a set of data. When you work with a large set of data, you want to get to the important information and describe it. The way to do that is to consolidate all the numeric data into readable and understandable information. That is what descriptive statistics aims to do.

Example of descriptive sports betting stats

One example of descriptive sports betting stats would be taking soccer stats available and finding the average number of home wins in European soccer leagues for instance.

Inferential statistics

Inferential sports betting stats are used to draw conclusions of a larger data set based on the qualities of a smaller sample group.  In simple words: inferential statistics is all about making forecasts, estimates or judgments about a larger set of data through use of a smaller sample of that large data set. The foundation of inferential betting stats is probability theory. We cover probability in our betting probability article series. We urge you to read those as well.

Inferential soccer stats example

Perhaps you want to start betting on Spanish soccer. In order to do that you need to know a thing or twenty-three about the general probabilities and the odds offered. Let’s for arguments sake assume you want to find out what the odds on the top 2 in the Spanish Primera Division should be when they face any team placed between number 3 and number 17 in the league. You would probably need to go through 15-20 years of soccer stats to get anywhere near a meaningful data set providing enough data points (the more data the more reliable usually). If you take the short cut of taking the last 3 years’ worth of data, this would be a sample of the larger data set. In essence this is you forecasting the odds or the betting probability on the future games using empiric probability based on statistics derived from the past.

What’s next?

Our next article on statistics covers more of the basics that you need to know. We will define Populations and samples as well as walk you through frequency distribution. If this sounds scary to you, don’t be afraid. We will break it down in easy to understand write-ups and use descriptive examples taken from sports betting. You will get the hang of it sooner rather than later.

Other sports betting stats articles here:

• Sports betting stats - Part 2 - Defining population and sample