A methodology for the analysis of soccer matches based on pagerank centrality
Date
2017
Type:
Book chapter
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Abstract
Data analysis in sports has adopted many different approaches given its usefulness in quantitative and objective management. Several advances have been made considering the researches and technologies that have been developed up until now. It is possible to find many complex methodologies of sport performance analysis in order to have as much as information as possible to achieve success. Therefore, a wide variety of options are available for sport managers, coaches or anyone interested, including advances on information systems, data mining, machine learning and motion analysis. However, the cost of these powerful methodologies induces the search of cheaper techniques based on basic but proper notation methodology. The aim of this chapter is to provide an observational methodology for soccer match analysis. When paired with PageRank as the main indicator of performance, it allows for a deep analysis of the data and better decision-making and performance analysis in soccer. To show some insights about the proposed model, real data from past matches are presented and discussed. Results show graph visualization that sum up the whole match in terms of the flows of a network modelled with passes and recoveries from the players as weights of its edges. One implication of our research is to be a first approach in generalizing the PageRank algorithm to soccer team’s management, which could be extrapolated to other disciplines. It also points to the feasibility of making a quantitative analysis for sport managers with a reasonable cost-benefit ratio. This analysis opens the paths to further analysis that could include spatiotemporal variables.
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Citation
Rojas-Mora, J., Chávez-Bustamante, F., Río-Andrade, J.D., & Medina-Valdebenito, N. (2017). A Methodology for the Analysis of Soccer Matches Based on PageRank Centrality. En: Sports Management as an Emerging Economic Activity: Trends and Best Practices. Springer, Cap. 16, pp. 257-272
Keywords
PageRank, Graph theory, Social network, Observational methodology, Centrality