Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Visual analysis of pressure in football

  • Published:
Data Mining and Knowledge Discovery Aims and scope Submit manuscript

Abstract

Modern movement tracking technologies enable acquisition of high quality data about movements of the players and the ball in the course of a football match. However, there is a big difference between the raw data and the insights into team behaviors that analysts would like to gain. To enable such insights, it is necessary first to establish relationships between the concepts characterizing behaviors and what can be extracted from data. This task is challenging since the concepts are not strictly defined. We propose a computational approach to detecting and quantifying the relationships of pressure emerging during a game. Pressure is exerted by defending players upon the ball and the opponents. Pressing behavior of a team consists of multiple instances of pressure exerted by the team members. The extracted pressure relationships can be analyzed in detailed and summarized forms with the use of static and dynamic visualizations and interactive query tools. To support examination of team tactics in different situations, we have designed and implemented a novel interactive visual tool “time mask”. It enables selection of multiple disjoint time intervals in which given conditions are fulfilled. Thus, it is possible to select game situations according to ball possession, ball distance to the goal, time that has passed since the last ball possession change or remaining time before the next change, density of players’ positions, or various other conditions. In response to a query, the analyst receives visual and statistical summaries of the set of selected situations and can thus perform joint analysis of these situations. We give examples of applying the proposed combination of computational, visual, and interactive techniques to real data from games in the German Bundesliga, where the teams actively used pressing in their defense tactics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. http://www.optasports.com.

  2. http://prozonesports.stats.com.

  3. http://www.fourfourtwo.com/statszone.

  4. http://www.bundesliga.de.

  5. https://en.wikipedia.org/wiki/Charles_Reep.

References

  • Aigner W, Miksch S, Schumann H, Tominski C (2011) Visualization of time-oriented data. Springer Science & Business Media, Berlin

    Book  Google Scholar 

  • Andrienko G, Andrienko N (2010) A general framework for using aggregation in visual exploration of movement data. Cartogr J 47(1):22–40

    Article  MATH  Google Scholar 

  • Andrienko G, Andrienko N, Bremm S, Schreck T, Von Landesberger T, Bak P, Keim D (2010) Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Comput Graph Forum 29(3):913–922

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Heurich M (2011) An event-based conceptual model for context-aware movement analysis. Int J Geogr Inf Sci 25(9):1347–1370

    Article  Google Scholar 

  • Andrienko N, Andrienko G, Stange H, Liebig T, Hecker D (2012) Visual analytics for understanding spatial situations from episodic movement data. KI-Künstliche Intell 26(3):241–251

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Bak P, Keim D, Wrobel S (2013a) Visual analytics of movement. Springer Science & Business Media, Berlin

    Book  Google Scholar 

  • Andrienko G, Andrienko N, Hurter C, Rinzivillo S, Wrobel S (2013b) Scalable analysis of movement data for extracting and exploring significant places. IEEE Trans Vis Comput Graph 19(7):1078–1094

    Article  Google Scholar 

  • Andrienko N, Andrienko G, Barrett L, Dostie M, Henzi P (2013c) Space transformation for understanding group movement. IEEE Trans Vis Comput Graph 19(12):2169–2178

    Article  Google Scholar 

  • Bak P, Marder M, Harary S, Yaeli A, Ship HJ (2012) Scalable detection of spatiotemporal encounters in historical movement data. Comput Graph Forum 31(3pt1):915–924

    Article  Google Scholar 

  • Bialkowski A, Lucey P, Carr GPK, Yue Y, Sridharan S, Matthews IA (2014a) Identifying team style in soccer using formations learned from spatiotemporal tracking data. In: Zhou Z, Wang W, Kumar R, Toivonen H, Pei J, Huang JZ, Wu X (eds) 2014 IEEE international conference on data mining workshops, ICDM workshops 2014, Shenzhen, China, 14 Dec 2014. IEEE, pp 9–14

  • Bialkowski A, Lucey P, Carr P, Yue Y, Matthews I (2014b) Win at home and draw away: automatic formation analysis highlighting the differences in home and away team behaviors. In: Proceedings of MIT sloan sports analytics

  • Bialkowski A, Lucey P, Carr P, Yue Y, Sridharan S, Matthews IA (2014c) Large-scale analysis of soccer matches using spatiotemporal tracking data. In: Kumar R, Toivonen H, Pei J, Huang JZ, Wu X, (eds) IEEE international conference on data mining, ICDM 2014, Shenzhen, China, 14–17 Dec 2014. IEEE, pp 725–730

  • Bojinov I, Bornn L (2016) The pressing game: optimal defensive disruption in soccer. http://www.sloansportsconference.com/content/the-pressing-game-optimal-defensive-disruption-in-soccer/

  • Carling C, Bloomfield J, Nelsen L, Reilly T (2008) The role of motion analysis in elite soccer. Sports Med 38(10):839–862

    Article  Google Scholar 

  • Chung DH, Legg PA, Parry ML, Bown R, Griffiths IW, Laramee RS, Chen M (2015) Glyph sorting: interactive visualization for multi-dimensional data. Inf Vis 14(1):76–90

    Article  Google Scholar 

  • Cintia P, Giannotti F, Pappalardo L, Pedreschi D, Malvaldi M (2015a) The harsh rule of the goals: data-driven performance indicators for football teams. In: IEEE international conference on data science and advanced analytics (DSAA), 2015. 36678 2015. IEEE, pp 1–10

  • Cintia P, Rinzivillo S, Pappalardo L (2015b) A network-based approach to evaluate the performance of football teams. In: Machine learning and data mining for sports analytics workshop, Porto, Portugal

  • Clemente FM, Couceiro MS, Martins FML, Mendes RS (2015) Using network metrics in soccer: a macro-analysis. J Hum Kinet 45(1):123–134

    Article  Google Scholar 

  • Crnovrsanin T, Muelder C, Correa C, Ma K-L (2009) Proximity-based visualization of movement trace data. In: IEEE symposium on visual analytics science and technology, 2009. VAST 2009. IEEE, pp 11–18

  • Di Salvo V, Baron R, Tschan H, Calderon Montero F, Bachl N, Pigozzi F (2007) Performance characteristics according to playing position in elite soccer. Int J Sports Med 28(3):222–227

    Article  Google Scholar 

  • Duarte R, Araújo D, Folgado H, Esteves P, Marques P, Davids K (2013) Capturing complex, non-linear team behaviours during competitive football performance. J Syst Sci Complex 26(1):62–72

    Article  Google Scholar 

  • Duch J, Waitzman JS, Amaral LAN (2010) Quantifying the performance of individual players in a team activity. PLoS ONE 5(6):e10937, 06

    Article  Google Scholar 

  • Dykes JA, Mountain DM (2003) Seeking structure in records of spatio-temporal behaviour: visualization issues, efforts and applications. Comput Stat Data Anal 43(4):581–603

    Article  MathSciNet  MATH  Google Scholar 

  • Franks A, Miller A, Bornn L, Goldsberry K et al (2015) Characterizing the spatial structure of defensive skill in professional basketball. Ann Appl Stat 9(1):94–121

    Article  MathSciNet  MATH  Google Scholar 

  • Frencken W (2012) H. d. Poel, C. Visscher, and K. Lemmink. Variability of inter-team distances associated with match events in elite-standard soccer. J Sports Sci 30(12):1207–1213

    Article  Google Scholar 

  • Gadia SK (1988) A homogeneous relational model and query languages for temporal databases. ACM Trans Database Syst (TODS) 13(4):418–448

    Article  MathSciNet  MATH  Google Scholar 

  • Giannotti F, Pedreschi D (2008) Mobility, data mining and privacy: geographic knowledge discovery. Springer Science & Business Media, Berlin

    Book  Google Scholar 

  • Grunz A, Memmert D, Perl J (2012) Tactical pattern recognition in soccer games by means of special self-organizing maps. Hum Mov Sci 31(2):334–343 (special issue on network approaches in complex environments)

    Article  Google Scholar 

  • Gudmundsson J, Wolle T (2014) Football analysis using spatio-temporal tools. Comput Environ Urban Syst 47:16–27

    Article  Google Scholar 

  • Gudmundsson J, Horton M (2016) Spatio-temporal analysis of team sports—a survey. CoRR, abs/1602.06994

  • Gudmundsson J, Laube P, Wolle T (2011) Computational movement analysis. In: Kresse W, Danko DM (eds) Springer handbook of geographic information. Springer, Berlin, pp 423–438

  • Guo H, Wang Z, Yu B, Zhao H, Yuan X (2011) Tripvista: triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection. In: Visualization symposium (PacificVis), 2011 IEEE Pacific. IEEE, pp 163–170

  • Güting RH, Schneider M (2005) Moving objects databases. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Gyarmati L, Kwak H, Rodriguez P (2014) Searching for a unique style in soccer. arXiv:1409.0308

  • Harrower M, Griffin AL, MacEachren A (1999) Temporal focusing and temporal brushing: assessing their impact in geographic visualization. In: Proceedings of 19th international cartographic conference, Ottawa, Canada, vol 1, pp 729–738

  • Harrower M, MacEachren A, Griffin AL (2000) Developing a geographic visualization tool to support earth science learning. Cartogr Geogr Inf Sci 27(4):279–293

    Article  Google Scholar 

  • Hirano S, Tsumoto S (2005) A clustering method for spatio-temporal data and its application to soccer game records. In: Kuznetsov SO, Ślęzak D, Hepting DH, Mirkin BG (eds) Rough sets, fuzzy sets, data mining, and granular computing. Springer, Berlin, pp 612–621

  • Horton M, Gudmundsson J, Chawla S, Estephan J (2015) Automated classification of passing in football. In: Cao T, Lim E-P, Zhou Z-H, Ho T-B, Cheung D, Motoda H (eds) Advances in knowledge discovery and data mining: 19th Pacific-Asia conference, PAKDD 2015, Ho Chi Minh City, Vietnam, 19–22 May 2015, proceedings, Part II. Springer International Publishing, pp 319–330

  • Hurter C, Tissoires B, Conversy S (2009) Fromdady: spreading aircraft trajectories across views to support iterative queries. IEEE Trans Vis Comput Graph 15(6):1017–1024

    Article  Google Scholar 

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

  • Jensen CS, Clifford J, Gadia SK, Segev A, Snodgrass RT (1992) A glossary of temporal database concepts. ACM Sigmod Rec 21(3):35–43

    Article  Google Scholar 

  • Kang C-H, Hwang J-R, Li K-J (2006) Trajectory analysis for soccer players. In: Sixth IEEE international conference on data mining-workshops (ICDMW’06). IEEE, pp 377–381

  • Kapler T, Wright W (2005) Geotime information visualization. Inf Vis 4(2):136–146

    Article  Google Scholar 

  • Kim S (2004) Voronoi analysis of a soccer game. Nonlinear Anal Model Control 9(3):233–240

    MATH  Google Scholar 

  • Kim H-C, Kwon O, Li K-J (2011) Spatial and spatiotemporal analysis of soccer. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’11. NY, USA, ACM, New York, pp 385–388

  • Knauf K, Memmert D, Brefeld U (2015) Spatio-temporal convolution kernels. Mach Learn 102(2):247–273

    Article  MathSciNet  MATH  Google Scholar 

  • Laube P, Imfeld S, Weibel R (2005) Discovering relative motion patterns in groups of moving point objects. Int J Geogr Inf Sci 19(6):639–668

    Article  Google Scholar 

  • Lucey P, Oliver D, Carr P, Roth J, Matthews I (2013) Assessing team strategy using spatiotemporal data. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’13, New York, NY, USA. ACM, pp 1366–1374

  • Lucey P, Bialkowski A, Monfort M, Carr P, Matthews I (2014) Quality vs quantity: improved shot prediction in soccer using strategic features from spatiotemporal data. In: MIT Sloan sports analytics conference. MIT Sloan

  • Lundblad P, Eurenius O, Heldring T (2009) Interactive visualization of weather and ship data. In: 2009 13th international conference on information visualisation. IEEE, pp 379–386

  • Mitchell-Taverner C (2005) Field hockey techniques and tactics. Human Kinetics, Champaign

    Google Scholar 

  • Mortensen A, Gaddam VR, Stensland HK, Griwodz C, Johansen D, Halvorsen P (2014) Automatic event extraction and video summaries from soccer games. In: Proceedings of the 5th ACM multimedia systems conference. ACM, pp 176–179

  • Moura FA, Martins LEB, Anido RO, Ruffino PRC, Barros RML, Cunha SA (2013) A spectral analysis of team dynamics and tactics in Brazilian football. J Sports Sci 31(14):1568–1577 (PMID: 23631771)

    Article  Google Scholar 

  • Orellana D, Wachowicz M, Andrienko N, Andrienko G (2009) Uncovering interaction patterns in mobile outdoor gaming. In: International conference on advanced geographic information systems and web services, 2009. GEOWS’09. IEEE, pp 177–182

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

  • Pena JL, Touchette H (2012) A network theory analysis of football strategies. arXiv:1206.6904

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

    Article  Google Scholar 

  • Perl J, Memmert D (2011) Net-based game analysis by means of the software tool soccer. Int J Comput Sci Sport 10(2):77–84

    Google Scholar 

  • Perl J, Grunz A, Memmert D (2013) Tactics analysis in soccer-an advanced approach. Int J Comput Sci Sport 12(1):33–44

    Google Scholar 

  • 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 

  • Reda K, Tantipathananandh C, Berger-Wolf T, Leigh J, Johnson A (2009) Socioscape—a tool for interactive exploration of spatio-temporal group dynamics in social networks. In: Proceedings of the IEEE information visualization conference (INFOVIS)

  • Reep C, Benjamin B (1968) Skill and chance in association football. J R Stat Soc Ser A (Gen) 131(4):581–585

    Article  Google Scholar 

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

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

  • Shao L, Sacha D, Neldner B, Stein M, Schreck T (2016) Visual-interactive search for soccer trajectories to identify interesting game situations. Electron Imaging 2016(1):1–10

    Article  Google Scholar 

  • Shneiderman B (1994) Dynamic queries for visual information seeking. IEEE Softw 11(6):70–77

    Article  Google Scholar 

  • Spretke D, Bak P, Janetzko H, Kranstauber B, Mansmann F, Davidson S (2011) Exploration through enrichment: a visual analytics approach for animal movement. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 421–424

  • 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 Geoinform 4(4):2159

    Article  Google Scholar 

  • Taki T, Hasegawa J-I (2000) Visualization of dominant region in team games and its application to teamwork analysis. In: Proceedings of the international conference on computer graphics, CGI ’00, Washington, DC, USA. IEEE Computer Society, pp 227–235

  • Taki T, Hasegawa J-i, Fukumura T (1996) Development of motion analysis system for quantitative evaluation of teamwork in soccer games. In: Proceedings international conference on image processing, vol 3, 1996. IEEE, pp 815–818

  • Tominski C, Schumann H, Andrienko G, Andrienko N (2012) Stacking-based visualization of trajectory attribute data. IEEE Trans Vis Comput Graph 18(12):2565–2574

    Article  Google Scholar 

  • von Landesberger T, Bremm S, Schreck T, Fellner DW (2014) Feature-based automatic identification of interesting data segments in group movement data. Inf Vis 13(3):190–212

    Article  Google Scholar 

  • Voronoï G (1908) Nouvelles applications des paramètres continus à la théorie des formes quadratiques. deuxième mémoire. recherches sur les parallélloèdres primitifs. Journal für die reine und angewandte Mathematik 134:198–287

    MathSciNet  MATH  Google Scholar 

  • Ware C, Arsenault R, Plumlee M, Wiley D (2006) Visualizing the underwater behavior of humpback whales. IEEE Comput Graph Appl 26(4):14–18

    Article  Google Scholar 

  • Weaver C (2010) Cross-filtered views for multidimensional visual analysis. IEEE Trans Vis Comput Graph 16(2):192–204

    Article  Google Scholar 

  • Wei X, Sha L, Lucey P, Morgan S, Sridharan S (2013) Large-scale analysis of formations in soccer. In: 2013 international conference on digital image computing: techniques and applications (DICTA), pp 1–8

  • Willems N, Van De Wetering H, Van Wijk JJ (2009) Visualization of vessel movements. Comput Graph Forum 28(3):959–966

    Article  Google Scholar 

  • Wood J, Dykes J, Slingsby A (2010) Visualisation of origins, destinations and flows with od maps. Cartogr J 47(2):117–129

    Article  Google Scholar 

  • Wörner M, Ertl T (2012) Visual analysis of public transport vehicle movement. In: 5th international EuroVis workshop on visual analytics (EuroVA’12), pp 79–83

  • Yue Z, Broich H, Seifriz F, Mester J (2008) Mathematical analysis of a soccer game. Part i: individual and collective behaviors. Stud Appl Math 121(3):223–243

    Article  MathSciNet  MATH  Google Scholar 

  • Zelentsov A, Lobanovsky V, Tkachuk V, Kondratjev A (1989) Tactics and strategy in football. Zdorovja (in Russian). Kyiv, Ukraine

  • Zheng Y, Zhou X (2011) Computing with spatial trajectories. Springer Science & Business Media, Berlin

    Book  Google Scholar 

  • Zhu G, Huang Q, Xu C, Rui Y, Jiang S, Gao W, Yao H (2007) Trajectory based event tactics analysis in broadcast sports video. In: Proceedings of the 15th international conference on multimedia. ACM, pp 58–67

Download references

Author information

Authors and Affiliations

  1. Fraunhofer Institute IAIS, Sankt Augustin, Germany

    Gennady Andrienko, Natalia Andrienko & Georg Fuchs

  2. City University London, London, UK

    Gennady Andrienko, Natalia Andrienko & Jason Dykes

  3. TU Eindhoven, Eindhoven, The Netherlands

    Guido Budziak

  4. TU Darmstadt, Darmstadt, Germany

    Tatiana von Landesberger

  5. DFL Deutsche Fussball Liga GmbH, Frankfurt, Germany

    Hendrik Weber

Authors
  1. Gennady Andrienko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natalia Andrienko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guido Budziak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jason Dykes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Georg Fuchs
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tatiana von Landesberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hendrik Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gennady Andrienko.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Andrienko, G., Andrienko, N., Budziak, G. et al. Visual analysis of pressure in football. Data Min Knowl Disc 31, 1793–1839 (2017). https://doi.org/10.1007/s10618-017-0513-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10618-017-0513-2

Keywords

Search

Navigation