Data visualization of structural and temporal trends in national strategies for Olympic team and individual events
Research Article
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Data visualization of structural and temporal trends in national strategies for Olympic team and individual events

Shuyu Meng 1*
1 Faculty of Engineering, The University of Sydney, Sydney, NSW 2006 Australia
*Corresponding author: shuyu_meng26@163.com
Published on 5 September 2025
Journal Cover
AORPM Vol.4 Issue 2
ISSN (Print): 3029-0899
ISSN (Online): 3029-0880
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Abstract

Understanding how countries allocate resources and formulate strategies between team and individual sports in the Olympic Games is crucial for uncovering broader institutional and cultural dynamics. This study, based on a visual analytics framework, explores the structural patterns of Olympic medal distribution over a century. Leveraging a large-scale athlete-event dataset, we construct three interrelated visualizations: 1) a symmetrical bar chart comparing national performance in team and individual sports; 2) a structural clustering model based on medal distribution, combining principal component analysis and K-means clustering to identify pattern types; and 3) a dynamic timeline visualization of the evolution of Australia's performance in team sports. The results reveal systematic differences in national strategic preferences, ranging from "team dominance" to "balanced" performance, and identify four structural archetypes of team sports success. Time series analysis further demonstrates how individual countries adjust their strategic priorities across Olympic cycles. The research suggests that medal structure is not simply a result of competitive performance but is also influenced by long-term strategic planning and institutional configurations. This study offers a new data-driven perspective for cross-national sports comparative analysis and demonstrates the unique value of visual analytics in revealing the underlying structure of global competitive systems.

Keywords:

visual analytics, Olympic Games, team vs individual events, sports strategy, medal distribution

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Meng,S. (2025). Data visualization of structural and temporal trends in national strategies for Olympic team and individual events. Advances in Operation Research and Production Management,4(2),6-18.

References

[1]. Petrovic, M., & Stanojevic, M. (2022). Socio-economic determinants of success in Olympic sports: Evidence from Tokyo 2020.Sport Soc., 25 (10), 1812–1830. https: //doi.org/10.1080/17430437.2021.2007385

[2]. O’Connor, F., & Byrne, P. (2022). Strategic investment patterns in elite sports: Lessons from Olympic success.Eur. Sport Manage. Q., 22 (7), 1056–1078. https: //doi.org/10.1080/16184742.2021.1907763

[3]. Nguyen, T., & Hoang, M. (2023). Visual analytics for longitudinal sports performance evaluation.IEEE Trans. Visual. Comput. Graphics, 29 (8), 3412–3426. https: //doi.org/10.1109/TVCG.2023.3245678

[4]. García, J., & López, R. (2022). Dimensionality reduction and clustering in sports analytics: Applications to Olympic performance.Appl. Sci., 12 (14), 6987. https: //doi.org/10.3390/app12146987

[5]. Li, S., Liang, Y., & Zhang, L. (2024). A data-driven approach to predicting Olympic medal outcomes: Integrating socio-economic and performance indicators.J. Sports Anal., 10 (2), 101–118. https: //doi.org/10.3233/JSA-230095

[6]. Brown, K., & White, D. (2022). Temporal dynamics of national performance in Olympic sports.Int.J. Perform. Anal. Sport, 22 (5), 687–703. https: //doi.org/10.1080/24748668.2022.2100559

[7]. Fang, J., Wang, H., & Chen, Y. (2022). Uncovering structural patterns in Olympic sports through network analysis.Social Network Anal. Min., 12 (1), 87–99. https: //doi.org/10.1007/s13278-022-00940-0

[8]. Huang, X., & Li, J. (2022). Interactive visualization for exploring patterns in sports competition results.Inf. Visualization, 21 (3), 451–468. https: //doi.org/10.1177/14738716211073243

[9]. Anderson, T., & Miller, P. (2023). Clustering national sports strategies using Olympic medal data.Data Min. Knowl. Discovery, 37 (6), 2456–2475. https: //doi.org/10.1007/s10618-023-00988-5

[10]. Kumar, R., & Singh, A. (2023). Comparative performance analysis of team and individual events in multi-sport competitions.Int. J. Sports Sci. Coaching, 18 (4), 765–780. https: //doi.org/10.1177/17479541231168391

[11]. Lopez, M., Matthews, G., & Smith, A. (2023). Predictive modelling of Olympic medal counts using machine learning techniques.J. Quant. Anal. Sports, 19 (1), 33–52. https: //doi.org/10.1515/jqas-2022-0043

[12]. Taylor, J., & Collins, D. (2022). Strategic sport policy for Olympic success: A systems thinking approach.Managing Sport Leisure, 27 (3–4), 293–309. https: //doi.org/10.1080/23750472.2020.1834347

[13]. Wang, L., & Chen, Q. (2023). Multivariate analysis of Olympic medal distribution patterns: Evidence from Tokyo 2020.Int. J. Sports Sci., 13 (2), 45–58. https: //doi.org/10.5923/j.sports.20231302.02

[14]. Zhao, Y., & Sun, H. (2024). Network analysis of global sports performance: Insights from Olympic history.Complexity, 2024, 1–14. https: //doi.org/10.1155/2024/4567891

[15]. Murray, S., & Hunter, R. (2022). Performance archetypes in elite sports: An unsupervised learning approach.J.Sports Sci., 40 (15), 1667–1676. https: //doi.org/10.1080/02640414.2022.2061234

[16]. Cetinkaya, A., Peker, S., & Kuvvetli, Ü. (2024). Analysis of countries’ performances in individual Olympic Games using cluster analysis and decision trees: The case of Tokyo 2020.Sport, Bus. Manage., 14 (5/6), 648–666. https: //doi.org/10.1108/SBM-12-2023-0151

[17]. Nowak, M., Skalik, M., Więckowski, J., Ciejpa, R., Stolarczyk, A., & Oleksy, Ł. (2025). Winners’strategies: Comprehensive analysis and optimization of 2-point shots in 3x3 basketball using multi-criteria decision support analysis, on the example of two Olympic National Teams.PLOS ONE. https: //doi.org/10.1371/journal.pone.0322024

[18]. Anderson, T., & Miller, P. (2023). Clustering national sports strategies using Olympic medal data.Data Min. Knowl. Discovery, 37 (6), 2456–2475. https: //doi.org/10.1007/s10618-023-00988-5

[19]. Brown, K., & White, D. (2022). Temporal dynamics of national performance in Olympic sports.Int. J. Perform. Anal. Sport, 22 (5), 687–703. https: //doi.org/10.1080/24748668.2022.2100559

[20]. Huang, X., & Li, J. (2025). Visual analytics for exploring patterns in sports competition results.Inf. Visualization, 21 (3), 451–468. https: //doi.org/10.1177/14738716211073243

[21]. Nguyen, T., & Hoang, M. (2023). Visual analytics for longitudinal sports performance evaluation.IEEE Trans. Visual. Comput. Graphics, 29 (8), 3412–3426. https: //doi.org/10.1109/TVCG.2023.3245678

References

[1]. Petrovic, M., & Stanojevic, M. (2022). Socio-economic determinants of success in Olympic sports: Evidence from Tokyo 2020.Sport Soc., 25 (10), 1812–1830. https: //doi.org/10.1080/17430437.2021.2007385

[2]. O’Connor, F., & Byrne, P. (2022). Strategic investment patterns in elite sports: Lessons from Olympic success.Eur. Sport Manage. Q., 22 (7), 1056–1078. https: //doi.org/10.1080/16184742.2021.1907763

[3]. Nguyen, T., & Hoang, M. (2023). Visual analytics for longitudinal sports performance evaluation.IEEE Trans. Visual. Comput. Graphics, 29 (8), 3412–3426. https: //doi.org/10.1109/TVCG.2023.3245678

[4]. García, J., & López, R. (2022). Dimensionality reduction and clustering in sports analytics: Applications to Olympic performance.Appl. Sci., 12 (14), 6987. https: //doi.org/10.3390/app12146987

[5]. Li, S., Liang, Y., & Zhang, L. (2024). A data-driven approach to predicting Olympic medal outcomes: Integrating socio-economic and performance indicators.J. Sports Anal., 10 (2), 101–118. https: //doi.org/10.3233/JSA-230095

[6]. Brown, K., & White, D. (2022). Temporal dynamics of national performance in Olympic sports.Int.J. Perform. Anal. Sport, 22 (5), 687–703. https: //doi.org/10.1080/24748668.2022.2100559

[7]. Fang, J., Wang, H., & Chen, Y. (2022). Uncovering structural patterns in Olympic sports through network analysis.Social Network Anal. Min., 12 (1), 87–99. https: //doi.org/10.1007/s13278-022-00940-0

[8]. Huang, X., & Li, J. (2022). Interactive visualization for exploring patterns in sports competition results.Inf. Visualization, 21 (3), 451–468. https: //doi.org/10.1177/14738716211073243

[9]. Anderson, T., & Miller, P. (2023). Clustering national sports strategies using Olympic medal data.Data Min. Knowl. Discovery, 37 (6), 2456–2475. https: //doi.org/10.1007/s10618-023-00988-5

[10]. Kumar, R., & Singh, A. (2023). Comparative performance analysis of team and individual events in multi-sport competitions.Int. J. Sports Sci. Coaching, 18 (4), 765–780. https: //doi.org/10.1177/17479541231168391

[11]. Lopez, M., Matthews, G., & Smith, A. (2023). Predictive modelling of Olympic medal counts using machine learning techniques.J. Quant. Anal. Sports, 19 (1), 33–52. https: //doi.org/10.1515/jqas-2022-0043

[12]. Taylor, J., & Collins, D. (2022). Strategic sport policy for Olympic success: A systems thinking approach.Managing Sport Leisure, 27 (3–4), 293–309. https: //doi.org/10.1080/23750472.2020.1834347

[13]. Wang, L., & Chen, Q. (2023). Multivariate analysis of Olympic medal distribution patterns: Evidence from Tokyo 2020.Int. J. Sports Sci., 13 (2), 45–58. https: //doi.org/10.5923/j.sports.20231302.02

[14]. Zhao, Y., & Sun, H. (2024). Network analysis of global sports performance: Insights from Olympic history.Complexity, 2024, 1–14. https: //doi.org/10.1155/2024/4567891

[15]. Murray, S., & Hunter, R. (2022). Performance archetypes in elite sports: An unsupervised learning approach.J.Sports Sci., 40 (15), 1667–1676. https: //doi.org/10.1080/02640414.2022.2061234

[16]. Cetinkaya, A., Peker, S., & Kuvvetli, Ü. (2024). Analysis of countries’ performances in individual Olympic Games using cluster analysis and decision trees: The case of Tokyo 2020.Sport, Bus. Manage., 14 (5/6), 648–666. https: //doi.org/10.1108/SBM-12-2023-0151

[17]. Nowak, M., Skalik, M., Więckowski, J., Ciejpa, R., Stolarczyk, A., & Oleksy, Ł. (2025). Winners’strategies: Comprehensive analysis and optimization of 2-point shots in 3x3 basketball using multi-criteria decision support analysis, on the example of two Olympic National Teams.PLOS ONE. https: //doi.org/10.1371/journal.pone.0322024

[18]. Anderson, T., & Miller, P. (2023). Clustering national sports strategies using Olympic medal data.Data Min. Knowl. Discovery, 37 (6), 2456–2475. https: //doi.org/10.1007/s10618-023-00988-5

[19]. Brown, K., & White, D. (2022). Temporal dynamics of national performance in Olympic sports.Int. J. Perform. Anal. Sport, 22 (5), 687–703. https: //doi.org/10.1080/24748668.2022.2100559

[20]. Huang, X., & Li, J. (2025). Visual analytics for exploring patterns in sports competition results.Inf. Visualization, 21 (3), 451–468. https: //doi.org/10.1177/14738716211073243

[21]. Nguyen, T., & Hoang, M. (2023). Visual analytics for longitudinal sports performance evaluation.IEEE Trans. Visual. Comput. Graphics, 29 (8), 3412–3426. https: //doi.org/10.1109/TVCG.2023.3245678

Cite this article

Meng,S. (2025). Data visualization of structural and temporal trends in national strategies for Olympic team and individual events. Advances in Operation Research and Production Management,4(2),6-18.

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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Journal: Advances in Operation Research and Production Management

Volume number: Vol.4
Issue number: Issue 2
ISSN: 3029-0880(Print) / 3029-0899(Online)

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