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Parking the bus

  • Tianyu Guan , Jiguo Cao und Tim B. Swartz EMAIL logo
Veröffentlicht/Copyright: 30. Mai 2023
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Journal of Quantitative Analysis in Sports
Aus der Zeitschrift Journal of Quantitative Analysis in Sports Band 19 Heft 4

Abstract

This paper explores defensive play in soccer. The analysis is predicated on the assumption that the area of the convex hull formed by the players on a team provides a proxy for defensive style where small areas coincide with a greater defensive focus. With the availability of tracking data, the massive dataset considered in this paper consists of areas of convex hulls, related covariates and shots taken during matches. Whereas the pre-processing of the data is an exercise in data science, the statistical analysis is carried out using linear models. The resultant messages are nuanced but the primary message suggests that an extreme defensive style (defined by a small convex hull) is negatively associated with generating shots.

Keywords: betting odds; big data; convex hulls; defensive strategies; tracking data
Corresponding author: Tim B. Swartz, Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, Canada, E-mail:

Acknowledgement

All authors have been partially supported by the Natural Sciences and Engineering Research Council of Canada. The authors thank Daniel Stenz, Technical Director of Shandong Luneng Taishan FC who provided the tracking data used in this paper. The authors also thank two Reviewers, two Co-Editors and an Associate Editor whose comments improved the manuscript.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Albert, J. A., M. E. Glickman, T. B. Swartz, and R. H. Koning, eds. 2017. Handbook of Statistical Methods and Analyses in Sports. Boca Raton: Chapman & Hall/CRC Handbooks of Modern Statistical Methods.10.1201/9781315166070Suche in Google Scholar

Beaudoin, D., O. Schulte, and T. B. Swartz. 2016. “Biased Penalty Calls in the National Hockey League.” The ASA Data Science Journal 9 (5): 365–72. https://doi.org/10.1002/sam.11320.Suche in Google Scholar

Buldú, J. M., J. Busquets, J. H. Martínez, J. L. Herrera-Diestra, I. Echegoyen, J. Galeano, and J. Luque. 2018. “Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time and the Multilayer Nature of the Game.” Frontiers in Psychology 9: 1900. https://doi.org/10.3389/fpsyg.2018.01900.Suche in Google Scholar PubMed PubMed Central

Chen, T., and Q. Fan. 2018. “A Functional Data Approach to Model Score Difference Process in Professional Basketball Games.” Journal of Applied Statistics 45: 112–27. https://doi.org/10.1080/02664763.2016.1268106.Suche in Google Scholar

Diquigiovanni, J., and B. Scarpa. 2019. “Analysis of Association Football Playing Styles: An Innovative Method to Cluster Networks.” Statistical Modelling 19 (1): 28–54. https://doi.org/10.1177/1471082x18808628.Suche in Google Scholar

Fernandez-Navarro, J., L. Fradua, A. Zubillaga, P. R. Ford, and A. P. McRobert. 2016. “Attacking and Defensive Styles of Play in Soccer: Analysis of Spanish and English Elite Teams.” Journal of Sports Sciences 34 (24): 2195–204. https://doi.org/10.1080/02640414.2016.1169309.Suche in Google Scholar PubMed

Fernandez-Navarro, J., L. Fradua, A. Zubillaga, and A. P. McRobert. 2018. “Influence of Contextual Variables on Styles of Play in Soccer.” International Journal of Performance Analysis in Sport 18 (3): 423–36. https://doi.org/10.1080/24748668.2018.1479925.Suche in Google Scholar

Garrido, D., D. R. Antequera, J. Busquets, R. L. Del Campo, R. R. Serra, S. J. Vielcazat, and J. M. Buldú. 2020. “Consistency and Identifiability of Football Teams: A Network Science Perspective.” Scientific Reports 10: 19735. https://doi.org/10.1038/s41598-020-76835-3.Suche in Google Scholar PubMed PubMed Central

Gollan, S., C. Bellenger, and K. Norton. 2020. “Contextual Factors Impact Styles of Play in the English Premier League.” Journal of Sports Science and Medicine 19 (1): 78–83.Suche in Google Scholar

Gonçalves, B., D. Coutinho, S. Santos, C. Lago-Penas, S. Jiménez, and J. Sampaio. 2017. “Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football.” PLoS One 12 (1): e0171156. https://doi.org/10.1371/journal.pone.0171156.Suche in Google Scholar PubMed PubMed Central

Guan, T., R. Nguyen, J. Cao, and T. B. Swartz. 2020. “In-Game Win Probabilities for the National Rugby League.” The Annals of Applied Statistics 16 (1): 349–67.10.1214/21-AOAS1514Suche in Google Scholar

Gudmundsson, J., and M. Horton. 2017. “Spatio-Temporal Analysis of Team Sports.” ACM Computing Surveys 50 (2): 22. https://doi.org/10.1145/3054132.Suche in Google Scholar

Lago-Peñas, C., M. Á. Gómez-Ruano, and Y. Gai. 2017. “Styles of Play in Professional Soccer: An Approach of the Chinese Soccer Super League.” International Journal of Performance Analysis in Sport 17 (6): 1073–84. https://doi.org/10.1080/24748668.2018.1431857.Suche in Google Scholar

Manafifard, M., H. Ebadi, and H. Abrishami Moghaddam. 2017. “A Survey on Player Tracking in Soccer Videos.” Computer Vision and Image Understanding 159: 19–46. https://doi.org/10.1016/j.cviu.2017.02.002.Suche in Google Scholar

Metulini, R., M. Manisera, and P. Zuccolotto. 2017. “Sensor Analytics in Basketball.” In Proceedings of the 6th International Conference on Mathematics in Sport.Suche in Google Scholar

Mead, J., A. O’Hare, and P. McMenemy. 2023. “Expected Goals in Football: Improving Model Performance and Demonstrating Value.” PLoS One 18 (4): 1–29, https://doi.org/10.1371/journal.pone.0282295.Suche in Google Scholar PubMed PubMed Central

Miller, A., L. Bornn, R. P. Adams, and K. Goldsberry. 2014. “Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball.” In Proceedings of the 31st International Conference on Machine Learning - Volume 32, 235–43. Beijing: JMLR.org.Suche in Google Scholar

Ötting, M., R. Langrock, and A. Maruotti. 2023. “A Copula-Based Multivariate Hidden Markov Model for Modelling Momentum in Football.” AStA Advances in Statistical Analysis 107: 9–27, https://doi.org/10.1007/s10182-021-00395-8.Suche in Google Scholar

Pebesma, E. J., and R. S. Bivand. 2005. “Classes and Methods for Spatial Data in R.” R News 5 (2): 9–13.Suche in Google Scholar

R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org/.Suche in Google Scholar

Ramsay, J. O., G. Hooker, and S. Graves. 2009. Functional Data Analysis with R and Matlab. New York: Springer.10.1007/978-0-387-98185-7Suche in Google Scholar

Silva, R., and T. B. Swartz. 2016. “Analysis of Substitution Times in Soccer.” Journal of Quantitative Analysis in Sports 12 (3): 113–22. https://doi.org/10.1515/jqas-2015-0114.Suche in Google Scholar

Singh, K. 2018. Introducing Expected Threat (xT). https://karun.in/blog/expected-threat.html (accessed January 25, 2022).Suche in Google Scholar

Thomas, A. C. 2017. “Poisson/exponential Models for Scoring in Ice Hockey.” In Handbook of Statistical Methods and Analyses in Sports, edited by J. A. Albert, M. E. Glickman, T. B. Swartz, and R. H. Koning, 271–85. Boca Raton: Chapman & Hall/CRC.Suche in Google Scholar

Wu, L., and T. B. Swartz. 2023. A New Metric for Pitch Control Based on an Intuitive Motion Model. https://www.sfu.ca/tswartz/ (accessed May 5, 2023).10.1007/s00180-024-01512-2Suche in Google Scholar

Wu, Y., X. Xie, J. Wang, D. Deng, H. Liang, H. Zhang, S. Cheng, and W. Chen. 2019. “ForVizor: Visualizing Spatio-Temporal Team Formations in Soccer.” IEEE Transactions on Visualization and Computer Graphics 25 (1): 65–75. https://doi.org/10.1109/tvcg.2018.2865041.Suche in Google Scholar PubMed

Younggren, J., and L. Younggren. 2021. A New Expected Goals Model for Predicting Goals in the NHL. https://evolving-hockey.com/blog/a-new-expected-goals-model-for-predicting-goals-in-the-nhl/ (accessed May 5, 2023).Suche in Google Scholar
Received: 2021-07-02
Accepted: 2023-05-11
Published Online: 2023-05-30
Published in Print: 2023-12-27

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. A Bayesian analysis of the time through the order penalty in baseball
  4. Parking the bus
  5. Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage
  6. Simplified Kalman filter for on-line rating: one-fits-all approach
  7. The evolution of seeding systems and the impact of imbalanced groups in FIFA Men’s World Cup tournaments 1954–2022
https://doi.org/10.1515/jqas-2021-0059
Schlagwörter für diesen Artikel
betting odds; big data; convex hulls; defensive strategies; tracking data

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. A Bayesian analysis of the time through the order penalty in baseball
  4. Parking the bus
  5. Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage
  6. Simplified Kalman filter for on-line rating: one-fits-all approach
  7. The evolution of seeding systems and the impact of imbalanced groups in FIFA Men’s World Cup tournaments 1954–2022
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