Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks.
With very few exceptions, projection techniques are designed to map data from a high-dimensional space to a visual space so as to preserve some dissimilarity (similarity) measure, such as the Euclidean distance for example.
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering.
In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines.
We show that the gradient dynamics of such networks are determined by the gradient flow in a non-redundant parameterization of the network function.
Automatic machine learning is an important problem in the forefront of machine learning.
The large variety and quantity of data available should be explored but this brings important challenges.