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Quantifying The Relation Between Performance And Success In Soccer

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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)

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.

Suggested Citation

  • Luca Pappalardo & Paolo Cintia, 2018. "Quantifying The Relation Between Performance And Success In Soccer," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(03n04), pages 1-30, May.
    • Handle: RePEc:wsi:acsxxx:v:21:y:2018:i:03n04:n:s021952591750014x
    DOI: 10.1142/S021952591750014X
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    Cited by:

    1. Laura M S de Jong & Paul B Gastin & Maia Angelova & Lyndell Bruce & Dan B Dwyer, 2020. "Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-12, October.
    2. Galli, L. & Galvan, G. & Levato, T. & Liti, C. & Piccialli, V. & Sciandrone, M., 2021. "Football: Discovering elapsing-time bias in the science of success," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    3. Li, Yuesen & Ma, Runqing & Gonçalves, Bruno & Gong, Bingnan & Cui, Yixiong & Shen, Yanfei, 2020. "Data-driven team ranking and match performance analysis in Chinese Football Super League," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    4. Serafeim Moustakidis & Spyridon Plakias & Christos Kokkotis & Themistoklis Tsatalas & Dimitrios Tsaopoulos, 2023. "Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics," Future Internet, MDPI, vol. 15(5), pages 1-18, May.
    5. Julen Castellano & Miguel Pic, 2019. "Identification and Preference of Game Styles in LaLiga Associated with Match Outcomes," IJERPH, MDPI, vol. 16(24), pages 1-13, December.

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