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Systematic Analysis of Position-Data-based Key Performance Indicators Cover

Systematic Analysis of Position-Data-based Key Performance Indicators

Open Access
|Jun 2023

Abstract

In the past 20 years, performance analysis in soccer has accumulated a wide variety of key performance indicators (KPI’s) aimed at reflecting a team’s strength and success. Thanks to rapidly advancing technologies and data analytics more sophisticated metrics, requiring high resolution data acquisition and big data methods, are developed. This includes many position-data-based KPI’s, which incorporate precise spatial and temporal information about every player and the ball on the field.

The present study contributes to this research by performing a large-scale comparison of several metrics mainly based on player positions and passing events. Their association with team’s success (derived from goals scored) and team’s strength (estimated from pre-game betting odds) is analysed.

The systematic analysis revealed relevant results for further KPI research: First, the magnitude of overall correlation coefficients was higher for relative metrics than for absolute metrics. Second, the correlation of metrics with the strength of a team is stronger than the correlation with the game success of a team. Third, correlation analysis with team strength indicated more positive associations, while correlation analysis with success is most likely confounded by the intermediate score line of a game and revealed more negative associations.

Language: English
Page range: 80 - 101
Published on: Jun 16, 2023
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2023 Justus Schlenger, Fabian Wunderlich, Dominik Raabe, Daniel Memmert, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.