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A Variety of Visualisations created and analysed
The visualisations I find most informative are shown below, with examples and a brief description of how they display data.
More tailor-made visualisations can be found under articles.
The data used for all these example Visualisations can be found here:
The code used to make these Visualisations can be found here:
Marginal Plots
Similar to a generic scatter plot, marginal plots show the comparison of two sets of data - one on each axis. However, rather than a scatter, this plot shows the correlation between the two data sets by creating a topographic style density plot with the distribution of data on its margins.
Radar Charts
These charts compare the percentile rank of multiple players for different sets of data in a circular shaped diagram.
Finding Spurs a new centre back
Intro Machine Learning
These graphs utilize the method of K-means clustering to group midfielders by type, something that could be useful when scouting specific roles. In the plot, each colour of datapoint on the scatter represents a different cluster, or "playstyle."
The k-means clustering was performed in R.
Beeswarm Plots
These plots show the general distribution of a specific set of data by plotting all the values on a single horizontal axis.
For example, the visualisation shows the spread of data for all premier league midfielders will a minimum of 1000 minutes played.
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