Using Graph Theory and KNIME Analytics Platform to Predict the EURO 2024 Outcome - Martin D Aus A

... All joking aside — over the past years I’ve used opportunities like this to learn about approaches to predict outcomes and tried them out eventually with more successes than fails when it comes to my placement in different leagues.

This time it is no different — what is different though is the approach I am taking. I came across this blog post on the usage of graph theory to make 2022 World Cup predictions.

The blog post shows how the connections between different football clubs and national teams (in terms of how many players of certain clubs are selected for different national teams) was used to create a network graph. For the relationships in that network graph the “eigenvector centrality” was determined for every national team to establish a ranking. For the 2018 World cup, applying this method, out of the top 5 teams with the highest eigenvector centrality, 3 ultimately made it into the semi-finals, and France as the highest ranked team won the tournament. For 2022, out of the predicted top 4 teams, three made it to the semi-finals and the highest ranked team, again France, ended up loosing to Argentina in a penalty shootout.

A pretty impressive accuracy for a method that doesn’t involve analyzing teams previous match results etc.

In this article I’m exploring if and how this can be done using a low-code approach in KNIME Analytics Platform.

Read more:

Se utilizzate Knime vi consiglio vivamente la lettura di questo tutorial e lo studio del relativo workflow, anche nell'ipotesi che il gioco del calcio non vi appassioni minimamente. Affronta infatti diversi argomenti utili, in particolare:

  • l'accesso e il download di informazioni presenti sul web (in questo caso sull'enciclopedia online Wikipedia)
  • l'utilizzo dei nodi dell'estensione Network Mining per un primo approccio all'analisi delle reti


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