Getting Started
===============

We recommend you start working 
through this material before the 
course starts.

### Setting up

The first step to getting going with football data 
is setting up a Python environment and to get some data to work with.
This video gently explains the tools you will need and 
the data you should get your hands on for the 
first part of the course. 


<iframe width="640" height="480" src="https://www.youtube.com/embed/FZ_XTfezCH4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

The video is slightly out of date (uses a slightly older version of Spyder than I currently use), 
but should give you a starting point for downloading [Anaconda](http://anaconda.com/), understanding which programs you need for running R and Python code,
orientating yourself within Spyder for Python. It also gives a "hello world" example, some advice about 
what to do if you are feeling stuck? and some points about the art of "copy and paste" programming 
(using [FCPython](https://fcpython.com/))

### Data 

The first half of this course uses the following datasources. Download the [Statsbomb data](https://github.com/statsbomb/open-data) here.
And download the [Wyscout data](https://figshare.com/collections/Soccer_match_event_dataset/4415000/2) here.
You should put these two directories in your working folder.

### MPL soccer library

In this version of the course we will use the MPL soccer library of functions. 
These are particularly useful for plotting pitches and other graphics. Once you have a Python environment running, 
follow the instructions [here](https://mplsoccer.readthedocs.io/en/latest/installation.html).

### Get inspired!

In April 2020, David interviewed four 
football data scientists (Pascal Bauer, 
Deutscher Fussball-Bund; Javier Fernandez, 
Barcelona; Suds Gopaladesikan, 
Benfica and Fran Peralta, Hammarby) 
about their role at their clubs and national 
teams. They talk about their routes in to working 
within clubs and what they do. 
This discussion offers a great starting 
point to finding out what you need to know 
about the profession of football data scientist. 

<iframe width="640" height="480" src="https://www.youtube.com/embed/cnEDCqmqCzw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

The next year, David and Friends of Tracking co-presenter Catherine Pfaff, caught up 
with more analysts and data scinetist: Ashwin Raman (Data scout Dundee United);
Hannah Roberts (Riptide Analytics/Blackburn);  Sam Gregory (Director of Analytics Inter Miami CF); and 
Rob Suddaby (Football Project Manager at FIFA. Previously 
Norwich City and Leicester).

<iframe width="640" height="480" src="https://www.youtube.com/embed/GLcGf-8oqO4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

More videos with interviews of club data scientists can be found on the [Friends of Tracking](https://www.youtube.com/channel/UCUBFJYcag8j2rm_9HkrrA7w) website.