"Does 4-4-2 exist?" -- An Analytics Approach to Understand and Classify Football Team Formations in Single Match Situations
Eric M\"uller-Budack, Jonas Theiner, Robert Rein, Ralph Ewerth

TL;DR
This paper presents an automated analytics method to classify and visualize football team formations in single match situations, enhancing understanding and analysis of tactical patterns like 4-4-2.
Contribution
It introduces a novel automated classification and visualization approach for team formations based on position data, focusing on detailed single match situations.
Findings
The approach outperforms existing formation classification methods.
Visualization aids in understanding complex team formations.
Insights into limitations of pattern-based formation descriptions.
Abstract
The chances to win a football match can be significantly increased if the right tactic is chosen and the behavior of the opposite team is well anticipated. For this reason, every professional football club employs a team of game analysts. However, at present game performance analysis is done manually and therefore highly time-consuming. Consequently, automated tools to support the analysis process are required. In this context, one of the main tasks is to summarize team formations by patterns such as 4-4-2. In this paper, we introduce an analytics approach that automatically classifies and visualizes the team formation based on the players' position data. We focus on single match situations instead of complete halftimes or matches to provide a more detailed analysis. A detailed analysis of individual match situations depending on ball possession and match segment length is provided. For…
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Taxonomy
TopicsSports Analytics and Performance · Sports Performance and Training · Data Visualization and Analytics
