Who can replace Xavi? A passing motif analysis of football players
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Abstract (arXiv)
Traditionally, most of football statistical and media coverage has been focused almost exclusively on goals and (ocassionally) shots. However, most of the duration of a football game is spent away from the boxes, passing the ball around. The way teams pass the ball around is the most characteristic measurement of what a team's "unique style" is. In the present work we analyse passing sequences at the player level, using the different passing frequencies as a "digital fingerprint" of a player's style. The resulting numbers provide an adequate feature set which can be used in order to construct a measure of similarity between players. Armed with such a similarity tool, one can try to answer the question: Who might possibly replace Xavi at FC Barcelona?
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Jun 2015.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 20216MapNet: Representing soccer players from tracking data by a triplet networkcited by 1
- 2022Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super Leaguecited by 1
- 2024Predicting soccer matches with complex networks and machine learningcited by 0
- 2024Predicting soccer matches with complex networks and machine learning19 Sep 2024
- 2022Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League5 May 2022
- 20216MapNet: Representing soccer players from tracking data by a triplet network10 Sep 2021