Delivered to Novice, Secondary Education audience (classes of 20x students in Year 10/aged 14-15)
Click on the link below to access the custom jupyterlite as the computational environment alongside the workshop code notebooks and data, or copy https://sportspython.github.io/SouthfieldsDec22/ into an appropriate browser:
- In theory, the custom jupyterlite should work on any device which meets the Browser Requirements, i.e. including tablets and mobiles. A laptop or desktop, where possible, is recommended however, for both performance and user experience.
JupyterLite requires one of the following modern web browsers:
- Firefox 90+
- Chromium 89+
Chrome has worked for a very limited sample of users, but is not the preferred choice.
Use the normal window, i.e. New Window not a New Private Window or other anonymous window (known performance issues e.g. jupyterlite/jupyterlite#679).
%matplotlib inline needed to display matplotlib graphs in the jupyter notebooks.
(Note: you are expected to know how to/be able to upskill yourself to be able to operate a Jupyter Notebook and JupyterLab environment)
- Use shift+enter to run code cells
- Use tab for auto-completion options
- Try shift+tab whilst writing a function to bring up it's documentation
- FIFA's new Football Intelligence available from their Training Centre website.
- The data and generated Football Intelligence is based on the 21/22 English Premier League match between Arsenal and Man U on 23/4/22, at the time seen as a battle for 4th/the final Champion's League spot. The BBC's match report can be found here.
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The dataset coordinates are based on a 2D pitch positioned landscape with length 105 units (x-axis) by height 68 units (y-axis), and a coordinate system where (0,0) is bottom-right. The data shows Arsenal and Man U events as if they were attacking opposite goals, and attacking these same goals for the whole match/i.e. no change between the two halves. Arsenal data reflects them attacking the goal to the right i.e. at 105 units/maximum of the x-axis, with Man U data conversely reflecting them attacking the goal to the left, i.e. at 0 units/minimum of the x-axis. This is for consistency across both halves of the match to remove the need to re-calculate coordinates to account for a team's change of ends. You may still want to align the coordinates for both teams for adjusting one team's data by replacing it with the inverse values, for example if wanting to compare positions between the two teams' attacks on goal etc.
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The version of the data,
match_data.csv, is designed for novice introduction to Python Data Science tailored to the specific workshop content, namely the warmup exercise, the Forced Turnovers workflow, Passing Network workflow, and Goalie Distribution workflow. Other uses of this data have not been tested so lack any guarantee of success.