EPL Player Heights and Weights

Here are the opening paragraphs of the PDF file relating to this page that you can download from the link that follows. You can also download the Excel file I have created.

Introduction

I have played around with the heights and weights of professional footballers for many years and here we are again. This time I managed to get a larger database of players to work from than before: http://www.footballsquads.co.uk/eng/2016-2017/faprem.htm Although I have only analysed the English Premier League (EPL) it is easy to find data on other English leagues and, indeed, from around the world.

In summary, what I wanted to do was to

Find some data and clean it as appropriate
Create an overview of the data such as all heights v all weights and to create the regression equation
Set out descriptive statistics
Create some graphs
Do all of the above for
- Individual clubs
- By players’ nationality
- By players’ position on the field

I was also interested in sharing my methods since there are some things that people are doing with their analysis and dashboards that are either overdesigned or are more difficult/complex than they ought to be. I use the very effective and efficient DATABASE functions, for example, while others will use INDEX() and MATCH() or similar combinations.

By the way, I don’t really draw any firm conclusions about this topic since I think you should draw your own conclusions and let the data speak to you! Similarly, I end this case by encouraging you to create your own dashboard out of my work and, of course, any additional work you do yourself.

The Data

Using the link I gave in the introduction, I found the data I was looking for although I had to scrape every page to get what I wanted. Still, I did get what I wanted, the heights and weights of 633 EPL players. The database I used contains 1,107 named players but the heights and weights are not collected for everyone for some reason. In the database there is also a section showing some players who are no longer at the club … I found it odd that they would provde these extra few players and ignored them: after all I would be double counting without a doubt in some cases if I did include them.

The analysis concentrates on the 633 players for whom I got full data, therefore.

The following screenshot shows that data I used although you will see in the file that I have also left in the columns containing dates of birth, birth place and previous club. In this version of this case, I have not done anything with dates of birth/ages: feel free to work on this by yourself!

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