A survey of competitive sports data visualization and visual analysis


Competitive sports data visualization is an increasingly important research direction in the field of information visualization. It is also an important basis for studying human behavioral pattern and activity habits. In this paper, we provide a taxonomy of sports data visualization and summarize the state-of-the-art research from four aspects of data types, main tasks and visualization techniques and visual analysis. Specifically, we first put sports data into two categories: spatiotemporal information and statistical information. Then, we propose three main tasks for competitive sports data visualization: feature presentation, feature comparison and feature prediction. Furthermore, we classify competitive sports data visualization techniques based on data characteristics into five categories: high-dimensional data visualization, time-series visualization, graph (network) visualization, glyph visualization and other visualization, and we analyze the relationship between major tasks and visualization techniques. We also introduce visual analysis research work of competitive sports, propose the features and limitations of competitive sports data, summarize multimedia visualization in competitive sports and finally discuss visual analysis evaluation. In this survey, we attempt to help readers to find appropriate techniques for different data types and different tasks. Our paper also intends to provide guidelines and references for future researchers when they study human behavior and moving patterns.

Graphic abstract

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. Albinsson PA, Andersson D (2008) Extending the attribute explorer to support professional team-sport analysis. Inf Vis 7(2):163–169

    Article  Google Scholar 

  2. Andrienko G, Andrienko N, Budziak G, Dykes J, Fuchs G, von Landesberger T, Weber H (2017) Visual analysis of pressure in football. Data Min Knowl Discov 31(6):1793–1839

    MathSciNet  Article  Google Scholar 

  3. Bialkowski A, Lucey P, Carr P, Matthews I, Sridharan S, Fookes C (2016) Discovering team structures in soccer from spatiotemporal data. IEEE Trans Knowl Data Eng 28(10):2596–2605

    Article  Google Scholar 

  4. Borgo R, Chen M, Daubney B, Grundy E, Heidemann G, Höferlin B, Höferlin M, Jänicke H, Weiskopf D, Xie X (2011) A survey on video-based graphics and video visualization. In: Eurographics (STARs), pp 1–23

  5. Cava R, Freitas CDS (2013) Glyphs in matrix representation of graphs for displaying soccer games results. In: SportVIS-workshop on sports data visualization. Atlanta, Georgia, USA: IEEE VIS

  6. Chen W, Lao T, Xia J, Huang X, Zhu B, Hu W, Guan H (2016) Gameflow: narrative visualization of NBA basketball games. IEEE Trans Multimed 18(11):2247–2256

    Article  Google Scholar 

  7. Chung DH, Legg P, Parry M, Griffiths I, Brown R, Laramee R, Chen M (2013) Visual analytics for multivariate sorting of sport event data. In: Workshop on sports data visualization, vol 3

  8. Chung DH, Parry ML, Griffiths IW, Laramee RS, Bown R, Legg PA, Chen M (2016) Knowledge-assisted ranking: a visual analytic application for sports event data. IEEE Comput Graph Appl 36(3):72–82

    Article  Google Scholar 

  9. Cox A, Stasko J (2006) Sportsvis: discovering meaning in sports statistics through information visualization. In: Compendium of symposium on information visualization, pp 114–115

  10. Dietrich C, Koop D, Vo HT, Silva CT (2014) Baseball4d: A tool for baseball game reconstruction & visualization. In: 2014 IEEE conference on visual analytics science and technology (VAST), IEEE, pp 23–32

  11. Du M, Chou JK, Ma C, Chandrasegaran S, Ma KL (2018) Exploring the role of sound in augmenting visualization to enhance user engagement. In: Pacific visualization symposium (PacificVis), IEEE

  12. Goldman M, Rao JM (2012) Effort vs. concentration: the asymmetric impact of pressure on nba performance. In: Proceedings of the MIT sloan sports analytics conference, pp 1–10

  13. Goldman M, Rao JM (2013) Live by the three, die by the three? The price of risk in the NBA. In: Submission to the MIT sloan sports analytics conference

  14. Goldsberry K (2012) Courtvision: new visual and spatial analytics for the NBA. In: 2012 MIT sloan sports analytics conference

  15. Gudmundsson J, Horton M (2017) Spatio-temporal analysis of team sports. ACM Comput Surv (CSUR) 50(2):22

    Article  Google Scholar 

  16. Hervieu A, Bouthemy P, Cadre JPL (2009) Trajectory-based handball video understanding. In: Proceedings of the ACM international conference on image and video retrieval, ACM, p 43

  17. Höferlin M, Grundy E, Borgo R, Weiskopf D, Chen M, Griffiths IW, Griffiths W (2010) Video visualization for snooker skill training. In: Computer graphics forum, Wiley Online Library, vol 29, pp 1053–1062

  18. Ishikawa Y, Fujishiro I (2018) Tidegrapher: visual analytics of tactical situations for rugby matches. Vis Inform 2(1):60–70. https://doi.org/10.1016/j.visinf.2018.04.007

    Article  Google Scholar 

  19. Janetzko H, Sacha D, Stein M, Schreck T, Keim DA, Deussen O, et al. (2014) Feature-driven visual analytics of soccer data. In: 2014 IEEE conference on visual analytics science and technology (VAST), IEEE, pp 13–22

  20. Janetzko H, Stein M, Sacha D, Schreck T (2016) Enhancing parallel coordinates: statistical visualizations for analyzing soccer data. Electron Imaging 1:1–8

    Article  Google Scholar 

  21. Jin L, Banks DC (1996) Visualizing a tennis match. In: Proceedings IEEE symposium on information visualization’96, IEEE, pp 108–114

  22. Jin L, Banks DC (1997) Tennisviewer: a browser for competition trees. IEEE Comput Graph Appl 17(4):63–65

    Article  Google Scholar 

  23. Kahn LM (1991) Discrimination in professional sports: a survey of the literature. ILR Rev 44(3):395–418

    Article  Google Scholar 

  24. Keim DA, Kriegel HP (1996) Visualization techniques for mining large databases: a comparison. IEEE Trans Knowl Data Eng 8(6):923–938

    Article  Google Scholar 

  25. Lage M, Ono JP, Cervone D, Chiang J, Dietrich C, Silva CT (2016) Statcast dashboard: exploration of spatiotemporal baseball data. IEEE Comput Graph Appl 36(5):28–37

    Article  Google Scholar 

  26. Larsen T, Price J, Wolfers J (2008) Racial bias in the NBA: implications in betting markets. J Quant Anal Sports 4(2):7

    MathSciNet  Google Scholar 

  27. Lazarus RS (2000) How emotions influence performance in competitive sports. Sport Psychol 14(3):229–252

    Article  Google Scholar 

  28. Legg PA, Chung DH, Parry ML, Jones MW, Long R, Griffiths IW, Chen M (2012) Matchpad: Interactive glyph-based visualization for real-time sports performance analysis. In: Computer graphics forum, Wiley Online Library, vol 31, pp 1255–1264

  29. Legg PA, Chung DH, Parry ML, Bown R, Jones MW, Griffiths IW, Chen M (2013) Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop. IEEE Trans Vis Comput Graph 19(12):2109–2118

    Article  Google Scholar 

  30. Lei H, Lao T, Liu Z, Zuo W, Chen W (2015) Sports data visualization survey. J Comput Aided Des Comput Graph 9(27):1605–1616

    Google Scholar 

  31. Liao Z, Li Y, Peng Y, Zhao Y, Zhou F, Liao Z, Dudley S, Ghavami M (2015) A semantic-enhanced trajectory visual analytics for digital forensic. J Vis 18(2):173–184

    Article  Google Scholar 

  32. LLC S (2014) SportVu data visualization suite. http://www.sportvu.com/. Accessed 16 April 2018

  33. Losada AG, Therón R, Benito A (2016) Bkviz: a basketball visual analysis tool. IEEE Comput Graph Appl 36(6):58–68

    Article  Google Scholar 

  34. Maheswaran R, Chang YH, Henehan A, Danesis S (2012) Deconstructing the rebound with optical tracking data. In: Proceedings of the 6th annual MIT SLOAN sports analytics conference

  35. Moon B, Brath R (2013) Bloomberg sports visualization for pitch analysis. In: Workshop on sports data visualization

  36. Okamoto DM (2011) Stratified odds ratios for evaluating NBA players based on their plus/minus statistics. J Quant Anal Sports 7(2):5

    Google Scholar 

  37. Owens SG, Jankun-Kelly T (2013) Visualizations for exploration of American football season and play data. In: The 1st workshop on sports data visualization. IEEE

  38. Page M, Moere AV (2006) Towards classifying visualization in team sports. In: 2006 International conference on computer graphics, imaging and visualisation, IEEE, pp 24–29

  39. Parry ML, Legg PA, Chung DH, Griffiths IW, Chen M (2011) Hierarchical event selection for video storyboards with a case study on snooker video visualization. IEEE Trans Vis Comput Graph 17(12):1747–1756

    Article  Google Scholar 

  40. Perin C, Vuillemot R, Fekete JD (2013) Soccerstories: a kick-off for visual soccer analysis. IEEE Trans Vis Comput Graph 19(12):2506–2515

    Article  Google Scholar 

  41. Perin C, Boy J, Vernier F (2016) Using gap charts to visualize the temporal evolution of ranks and scores. IEEE Comput Graph Appl 36(5):38–49

    Article  Google Scholar 

  42. Perin C, Vuillemot R, Stolper CD, Stasko JT, Wood J, Carpendale S (2018) State of the art of sports data visualization. In: Computer graphics forum, Wiley Online Library, vol 37, pp 663–686

  43. Pileggi H, Stolper CD, Boyle JM, Stasko JT (2012) Snapshot: visualization to propel ice hockey analytics. IEEE Trans Vis Comput Graph 18(12):2819–2828

    Article  Google Scholar 

  44. Pingali G, Opalach A, Jean Y, Carlbom I (2001) Visualization of sports using motion trajectories: providing insights into performance, style, and strategy. In: Proceedings of the conference on Visualization’01, IEEE Computer Society, pp 75–82

  45. Polk T, Yang J, Hu Y, Zhao Y (2014) Tennivis: visualization for tennis match analysis. IEEE Trans Vis Comput Graph 20(12):2339–2348

    Article  Google Scholar 

  46. Ren L, Du Y, Ma S, Zhang X, Dai G et al (2014) Visual analytics towards big data. J Softw 25(9):1909–1936

    Google Scholar 

  47. Rusu A, Stoica D, Burns E, Hample B, McGarry K, Russell R (2010) Dynamic visualizations for soccer statistical analysis. In: 2010 14th International conference on information visualisation (IV), IEEE, pp 207–212

  48. Rusu A, Stoica D, Burns E (2011) Analyzing soccer goalkeeper performance using a metaphor-based visualization. In: 2011 15th International conference on information visualisation (IV), IEEE, pp 194–199

  49. Sacha D, Al-Masoudi F, Stein M, Schreck T, Keim DA, Andrienko G, Janetzko H (2017) Dynamic visual abstraction of soccer movement. In: Computer graphics forum, Wiley Online Library, vol 36, pp 305–315

  50. Saito H, Inamoto N, Iwase S (2004) Sports scene analysis and visualization from multiple-view video. In: IEEE international conference on multimedia and expo, 2004. ICME’04. IEEE, vol 2, pp 1395–1398

  51. Sisneros R, Van Moer M (2013) Expanding plus-minus for visual and statistical analysis of NBA box-score data. In: The 1st workshop on sports data visualization. IEEE

  52. Stein M, Häußler J, Jäckle D, Janetzko H, Schreck T, Keim DA (2015) Visual soccer analytics: understanding the characteristics of collective team movement based on feature-driven analysis and abstraction. ISPRS Int J Geo-Inf 4(4):2159–2184

    Article  Google Scholar 

  53. Stein M, Janetzko H, Breitkreutz T, Seebacher D, Schreck T, Grossniklaus M, Couzin ID, Keim DA (2016a) Director’s cut: analysis and annotation of soccer matches. IEEE Comput Graph Appl 36(5):50–60

    Article  Google Scholar 

  54. Stein M, Janetzko H, Lamprecht A, Seebacher D, Schreck T, Keim D, Grossniklaus M (2016b) From game events to team tactics: visual analysis of dangerous situations in multi-match data. In: International conference on technology and innovation in sports, health and wellbeing (TISHW), IEEE, pp 1–9

  55. Stein M, Janetzko H, Seebacher D, Jäger A, Nagel M, Hölsch J, Kosub S, Schreck T, Keim D, Grossniklaus M (2017) How to make sense of team sport data: from acquisition to data modeling and research aspects. Data 2(1):2

    Article  Google Scholar 

  56. Stein M, Janetzko H, Lamprecht A, Breitkreutz T, Zimmermann P, Goldlücke B, Schreck T, Andrienko G, Grossniklaus M, Keim DA (2018) Bring it to the pitch: combining video and movement data to enhance team sport analysis. IEEE Trans Vis Comput Graph 24(1):13–22

    Article  Google Scholar 

  57. Tan D, Smith G, Lee B, Robertson G (2007) Adaptivitree: adaptive tree visualization for tournament-style brackets. IEEE Trans Vis Comput Graph 13(6):1113–1120

    Article  Google Scholar 

  58. Tani T, Huang H, Kawagoe K (2015) Sports play visualization system for American football. In: Proceedings of the international multiconference of engineers and computer scientists, vol 1

  59. Turo D (1994) Hierarchical visualization with treemaps: making sense of pro basketball data. In: Conference companion on Human factors in computing systems, ACM, pp 441–442

  60. Villar JG, Guerrero PR et al (2009) Sports attendance: a survey of the literature 1973–2007. Rivista di Diritto e di Economia dello Sport 5(2):112–151

    Google Scholar 

  61. Vuillemot R, Perin C (2016) Sports tournament predictions using direct manipulation. IEEE Comput Graph Appl 36(5):62–71

    Article  Google Scholar 

  62. Wang J, Zhao K, Deng D, Cao A, Xie X, Zhou Z, Zhang H, Wu Y (2019) Tac-simur: tactic-based simulative visual analytics of table tennis. IEEE Trans Vis Comput Graph 26:407–417

    Article  Google Scholar 

  63. Wang JR, Parameswaran N (2004) Survey of sports video analysis: research issues and applications. In: Proceedings of the Pan-Sydney area workshop on Visual information processing, Australian Computer Society, Inc., pp 87–90

  64. Wongsuphasawat K (2013) A narrative display for sports tournament recap. In: Workshop on sports data visualization in conjunction with IEEE VIS

  65. Wongsuphasawat K, Gotz D (2012) Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. IEEE Trans Vis Comput Graph 18(12):2659–2668

    Article  Google Scholar 

  66. Wood J (2015) Visualizing personal progress in participatory sports cycling events. IEEE Comput Graph Appl 35(4):73–81

    Article  Google Scholar 

  67. Wu Y, Lan J, Shu X, Ji C, Zhao K, Wang J, Zhang H (2018a) ittvis: interactive visualization of table tennis data. IEEE Trans Vis Comput Graph 24(1):709–718

    Article  Google Scholar 

  68. Wu Y, Xie X, Wang J, Deng D, Liang H, Zhang H, Cheng S, Chen W (2018b) Forvizor: visualizing spatio-temporal team formations in soccer. IEEE Trans Vis Comput Graph 25(1):65–75

    Article  Google Scholar 

Download references


This work is supported by the National Key Research and Development Program of China (2016QY02D0304). We appreciate all the authors who gave us permission to reuse their images in this research. We would also like to thank all researchers for their contributions in competitive sports visualization field and the editors of this journal and the anonymous reviewers for their valuable suggestions and comments.

Author information



Corresponding author

Correspondence to Xiaoru Yuan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

M. Du: The presented work was done while Meng Du was a postdoctoral researcher at Peking University.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Du, M., Yuan, X. A survey of competitive sports data visualization and visual analysis. J Vis 24, 47–67 (2021). https://doi.org/10.1007/s12650-020-00687-2

Download citation


  • Competitive sports
  • Data visualization
  • Visual analysis