QUANTIFYING THE RELATION BETWEEN PERFORMANCE AND SUCCESS IN SOCCER

Luca Pappalardo and Paolo Cintia
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Luca Pappalardo: Department of Computer Science, University of Pisa, Italy2Institute of Information Sciences and Technologies (ISTI), CNR, Pisa, Italy
Paolo Cintia: Department of Computer Science, University of Pisa, Italy2Institute of Information Sciences and Technologies (ISTI), CNR, Pisa, Italy

Advances in Complex Systems (ACS), 2018, vol. 21, issue 03n04, 1-30

Abstract: The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team’s position in a competition’s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team’s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.

Keywords: Data science; science of success; sports analytics; soccer analytics; sports science; complex systems; machine learning; predictive analytics (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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DOI: 10.1142/S021952591750014X

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