Quantifying the relation between performance and success in soccer
Pappalardo L., Cintia P.
Machine Learning (stat.ML) Statistics - Machine Learning Data science Sports analytics Predictive analytics Complex systems J.3 Control and Systems Engineering FOS: Computer and information sciences Sports science Statistics - Applications H.2.8 Applications (stat.AP)
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.
Source: Advances in Complex Systems 21 (2018). doi:10.1142/S021952591750014X
Publisher: World Scientific Publishing, Singapore, Singapore
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@article{oai:it.cnr:prodotti:385725,
title = {Quantifying the relation between performance and success in soccer},
author = {Pappalardo L. and Cintia P.},
publisher = {World Scientific Publishing, Singapore, Singapore},
doi = {10.1142/s021952591750014x and 10.48550/arxiv.1705.00885},
journal = {Advances in Complex Systems},
volume = {21},
year = {2018}
}
0000-0002-8085-9338ISTI Repository
DOI
10.1142/s021952591750014x
10.48550/arxiv.1705.00885
arXiv.org e-Print Archive
Advances in Complex Systems
www.worldscientific.com
