Judging Messi’s and Ronaldo’s scoring ability in different situations according to the model

Research Article
Open access

Judging Messi’s and Ronaldo’s scoring ability in different situations according to the model

Xinrui Chen 1* , Yufei Tang 2
  • 1 Univerity of California Davis    
  • 2 Stony Brook University    
  • *corresponding author cxrchen@ucdavis.edu
Published on 26 December 2023 | https://doi.org/10.54254/2753-8818/28/20230374
TNS Vol.28
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-261-9
ISBN (Online): 978-1-83558-262-6

Abstract

In the past decade, two players have overshadowed others in soccer. Who is better, Lionel Messi or Cristiano Ronaldo, has been debated for over a decade. Unlike basketball, the low-scoring nature of soccer dictates that one usually cannot visually conclude the game. Most people discuss who shines in terms of statistics, but there is no way to know the goal-scoring preferences of either man. This paper explores the goal-scoring ability of the two men in different situations to prove who is more complete based on the goal-scoring records of the 2020-2021 season and the data required for expected goals (xG). The study results prove that Messi is more dominant with long-range shots, and Ronaldo scores goals in all visible ranges. This paper introduces a new method of comparing Messi and Ronaldo and uses it as an example to develop a comparison that applies to all players.

Keywords:

Lionel Messi, Cristiano Ronaldo, Soccer, Numerical model

Chen,X.;Tang,Y. (2023). Judging Messi’s and Ronaldo’s scoring ability in different situations according to the model. Theoretical and Natural Science,28,123-128.
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References

[1]. Baker, M. (2021). The impact of Video Assistant Referee (VAR) on the fairness of soccer. Journal of Sports Sciences, 39(5), 483-490.

[2]. Kang, S. K., & Lee, S. Y. (2020). The impact of technology on soccer performance analysis. Journal of Physical Education and Sport, 20(1), 95-100.

[3]. Rathke, A. (2017). An examination of expected goals and shot efficiency in soccer. J. Hum. Sport Exer. 12, 514–529. doi: 10.14198/jhse.2017.12. Proc2.05

[4]. Alexander, Duncan. How Soccer Analytics Works. Penguin Random House, 2021.

[5]. Fernández, Javier, and Luke Bornn. “Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer.” Journal of Quantitative Analysis in Sports, vol. 12, no. 3, 2016, pp. 139-150.

[6]. Ismael Gómez, et al. “Fitting Your Own Football XG Model · Dato Futbol.” DATO FUTBOL, 14 Apr. 2020, https://www.datofutbol.cl/xg-model/.

[7]. Lucey, Patrick, et al. “A Multi-Scale Approach to Predicting Goals in Soccer.” Journal of Quantitative Analysis in Sports, vol. 12, no. 4, 2016, pp. 159-168.

[8]. Xu, Qingyang, et al. “Learning to Score in the Wild.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5455-5463.

[9]. Wunderlich, F., & Memmert, D. (2019). Data science and soccer: identifying interesting variables through machine learning techniques. Current Opinion in Psychology, 34, 155-159.

[10]. Bialkowski, A., Lucey, P., Carr, P., & Matthews, I. (2014). Probabilistic event forecasting in soccer. In Proceedings of the 23rd international conference on World Wide Web (pp. 557-562). https://www.mathworks.com/help/stats/ksdensity.html

Cite this article

Chen,X.;Tang,Y. (2023). Judging Messi’s and Ronaldo’s scoring ability in different situations according to the model. Theoretical and Natural Science,28,123-128.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation

ISBN：978-1-83558-261-9(Print) / 978-1-83558-262-6(Online)
Editor：Roman Bauer
Conference website: https://www.confmpcs.org/
Conference date: 12 August 2023
Series: Theoretical and Natural Science
Volume number: Vol.28
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. Baker, M. (2021). The impact of Video Assistant Referee (VAR) on the fairness of soccer. Journal of Sports Sciences, 39(5), 483-490.

[2]. Kang, S. K., & Lee, S. Y. (2020). The impact of technology on soccer performance analysis. Journal of Physical Education and Sport, 20(1), 95-100.

[3]. Rathke, A. (2017). An examination of expected goals and shot efficiency in soccer. J. Hum. Sport Exer. 12, 514–529. doi: 10.14198/jhse.2017.12. Proc2.05

[4]. Alexander, Duncan. How Soccer Analytics Works. Penguin Random House, 2021.

[5]. Fernández, Javier, and Luke Bornn. “Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer.” Journal of Quantitative Analysis in Sports, vol. 12, no. 3, 2016, pp. 139-150.

[6]. Ismael Gómez, et al. “Fitting Your Own Football XG Model · Dato Futbol.” DATO FUTBOL, 14 Apr. 2020, https://www.datofutbol.cl/xg-model/.

[7]. Lucey, Patrick, et al. “A Multi-Scale Approach to Predicting Goals in Soccer.” Journal of Quantitative Analysis in Sports, vol. 12, no. 4, 2016, pp. 159-168.

[8]. Xu, Qingyang, et al. “Learning to Score in the Wild.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5455-5463.

[9]. Wunderlich, F., & Memmert, D. (2019). Data science and soccer: identifying interesting variables through machine learning techniques. Current Opinion in Psychology, 34, 155-159.

[10]. Bialkowski, A., Lucey, P., Carr, P., & Matthews, I. (2014). Probabilistic event forecasting in soccer. In Proceedings of the 23rd international conference on World Wide Web (pp. 557-562). https://www.mathworks.com/help/stats/ksdensity.html

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