Graph Data Science

Analyze the Graph Structure of Soccer you own this product

This free project is part of the liveProject series Hands-on Graph Data Science with LynxKite
prerequisites
basic SQL • familiarity with tables as CSV files • basic data science and machine learning
skills learned
defining graphs based on event sequences • defining a pass graph among players • defining a graph on areas of the pitch
Andras Nemeth
1 week · 6-8 hours per week · INTERMEDIATE
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This free project is part of the liveProject series Hands-on Graph Data Science with LynxKite explore series
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Look inside
In this liveProject, you’ll construct event sequence graphs to reveal interesting information about soccer games. You’ll work to find longest pass sequences, most important players, and to understand the spatial structure of the game. You’ll define and visualize these graphs, and use connected components to find interesting event subsequences. You’ll quickly be able to uncover insights such as the most important players and the spatial structuring of the playing pitch.

project author

Andras Nemeth
András Németh is the CTO of Lynx Analytics, with significant experience (as the top technology executive on client projects) in using graphs in real-world consultancy projects. He also led the development of LynxKite, a Graph Data Science Platform in the past six years. He has an MsC in Software Engineering and an MsC in Mathematics. Before Lynx Analytics, he worked for Google where he was the tech lead on a project aiming to annotate web pages with entities from Google's Knowledge Graph.

prerequisites

This liveProject is for data scientists interested in the basic techniques of graph data science. This project is suitable for a large range of expertise levels from beginners to experienced practitioners. To begin this liveProject, you will need to be familiar with:

  • Basic SQL
  • Tables, their representation as CSV files
  • Basic data science and machine learning

you will learn

In this liveProject, you’ll learn the basics of graph data structures and how to define relationships between data.

  • Defining graphs based on event sequences
  • Defining a pass graph among players
  • Defining a graph on areas of the pitch
  • Various ways of visualizing these graphs
  • Using connected components to find interesting event subsequences
  • Applying pagerank and reverse pagerank to gain different importance metrics for players
  • Working with LynxKite for efficient graph data science

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