Using Averages in Sports Betting

It is very common to use averages in sports betting.  The idea of an average is very simple and straightforward, and provides a useful quantification for performance statistics.  But what type of average do you use when you are doing your analysis?  Have you considered the shape of the distribution data?  Or are you only looking at the overall picture while ignoring the details?

There are several types of averages you can use to analyze data.  Think back to your high-school statistics class (if you took one):

• Mean:  This is the most common type people are referring to when they talk about finding an average.  To find the mean for a set of data, you simply add up all the data points, and then divide by the total number of data points. 

• Median:  This is the middle number in your data set when ordered least to greatest. 

• Mode:  The mode is the number which is repeated most often.

• Range:  This is not a type of average, but it is an important thing to take note of when you are looking at data.  The range is the span of the data from the lowest to highest number.

Each of these types of averages can be useful, but taken in isolation, may not give you a comprehensive picture.  Say for example that you find the mean for a set of data comparing the performance of two different teams across the same season.  When you compare the means, you see that Team A outperformed Team B over the course of the season.  This would lead you to think that Team A is superior and worth betting on in a game where the two teams face off.

But is this necessarily true?  That depends on the distribution of the data.  What if you were to chart the numbers and discover that Team A was doing better than Team B at the start of the season, but Team B has gradually started outperforming Team A in the latter part of the season?  Looking at the range and median would have helped you to notice this change, as would actually visually plotting the data on a graph.  It turns out you might be better off betting on Team B after all.  Even though Team A may have outperformed them overall, it makes sense to give more weight to recent performance data.

Another detail which can get lost when you are looking at averages is outliers.  Sometimes outliers in the data set can throw off the total mean so that a team’s performance appears much better or much worse than it actually was.  Plotting the data can help you see if these outliers unnaturally extended the range, throwing off the average. 

Learning about averages is easy, and once you do some practice plotting charts and analyzing data sets with more than just the mean, you will start spotting these potential problems and learning how to steer around them.  It is well worth it to do more in-depth analysis of data sets and not rely too much on the mean.