2019
DOI: 10.2478/ijcss-2019-0017
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Analyzing passing networks in association football based on the difficulty, risk, and potential of passes

Abstract: This paper investigates the use of network analysis to identify key players on teams, and patterns of passing within teams, in association football. Networks are constructed based on passes made between players, and several centrality measures are investigated in combination with three different methods for evaluating individual passes. Four seasons of data from the Norwegian top division are used to identify key players and analyze matches from a selected team. The networks examined in this work have weights … Show more

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Cited by 6 publications
(13 citation statements)
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“…Own goals are considered as shots by the team being awarded the goal. Most of the explanatory variables used in the GAM are based on the results from passing ability models in (Håland et al 2020) and network metrics to analyze key players in (Wiig et al 2019). Explanations of these models and metrics are therefore given first.…”
Section: Model Descriptionmentioning
confidence: 99%
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“…Own goals are considered as shots by the team being awarded the goal. Most of the explanatory variables used in the GAM are based on the results from passing ability models in (Håland et al 2020) and network metrics to analyze key players in (Wiig et al 2019). Explanations of these models and metrics are therefore given first.…”
Section: Model Descriptionmentioning
confidence: 99%
“…Following Wiig et al (2019), passing networks are built using outputs from the three GAMMs of (Håland et al 2020), where connections are formed between players if they have made passes between each other, and where the weights of these connections depend on the associated values of Y P 1 , Y P 2 , and Y P 3 for the passes made. These networks are occasionally referred to as network 1, 2, and 3, to indicate that they are calculated based on weights derived Table : Summary of explanatory variables used in models by Håland et al ().…”
Section: Model Descriptionmentioning
confidence: 99%
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