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The Impact of Big Data and Sports Analytics on Professional Football: A Systematic Literature Review

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Digitalization, Digital Transformation and Sustainability in the Global Economy

Abstract

Big data and data analytics are two buzzwords that not only are frequently heard in the context of the digital transformation of society but also are becoming increasingly common in sports. This study demonstrates the changes caused by the use of technologies in the context of big data and sports analytics on the basis of a systematic literature review (SLR) in professional football. Moreover, we analyze to what extent their use has changed and will continue to change the strategies of professional football clubs and their stakeholders. Our results show that big data and sports analytics have become important tools in professional football and can increase the competitiveness of professional football clubs. Nevertheless, our SLR also shows that new technologies have risk potentials for different stakeholder groups.

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    For further information about third-party ownership arrangements see Herberger et al. (2018) and Herberger et al. (2019).

References

  • Ashton, K. (2009). That ‘Internet of Things’ Thing: In the real world, things matter more than ideas. Retrieved February 28, 2021, http://www.rfidjournal.com/articles/view?4986

  • Azuma, R. T. (1997). A survey of augmented reality. Presence: Teleoperators and Virtual Environments, 6(4), 355–385.

    Google Scholar 

  • Baca, A. (2015). Computer science in sport. Research and practice. Routledge.

    Google Scholar 

  • Baerg, A. (2017). Big data, sport, and the digital divide: Theorizing how athletes might respond to big data monitoring. Journal of Sport and Social Issues, 41(1), 3–20.

    Article  Google Scholar 

  • BaFin. (2018). Big Data trifft auf Künstliche Intelligenz. Bonn, Frankfurt am Main: Bundesanstalt für Finanzdienstleistungsaufsicht.

    Google Scholar 

  • Bardley, J., Reberger, C., Dixit, A., & Gubta, V. (2013). Internet of everything: A $4.6 trillion public-sector opportunity: More relevant, valuable connections will boost productivity, revenue, and citizen experience, while cutting costs. Retrieved February 28, 2021, https://www.cisco.com/c/dam/en_us/about/business-insights/docs/ioe-public-sector-vas-white-paper.pdf

  • Beetz, M., Hoyningen-Huene, N., Kirchlechner, B., Gedikli, S., Siles, F., Durus, M., & Lames, M. (2009). ASPOGAMO: Automated sports game analysis models. International Journal of Computer Science in Sport, 8(1), 1–21.

    Google Scholar 

  • Biermann, C. (2016). Moneyball im Niemandsland. 11 Freunde. Retrieved February 28, 2021, https://www.11freunde.de/artikel/midtjyllands-revolution

  • Biermann, C. (2018a). Ohne Mathe geht es nicht. Wie die digitale Datenanalyse den Fußball verändert. Rotary Magazin, 2018(6).

    Google Scholar 

  • Biermann, C. (2018b). Matchplan: Die Neue Fußballmatrix. Köln: Kiepenheuer & Witch.

    Google Scholar 

  • Bukstein, S. (2016). Evolution and impact of business analytics in sport. Sport Business Analytics, 25–46.

    Google Scholar 

  • Burns, E. (2020). Predictive analytics. Retrieved February 02, 2021, https://www.searchenterprisesoftware.de/definition/Predictive-Analytics

  • Bush, M., Barnes, C., Archer, D. T., Hogg, B., & Bradley, P. S. (2015). Evolution of match performance parameters for various playing positions in the English Premier League. Human Movement Science, 39, 1–11.

    Article  Google Scholar 

  • Carling, C., Wells, S., & Lawlor, J. (2018). Performance analysis in the professional football club environment. In W. Gregson & Littlewood (Eds.), Science in soccer: Translating theory into practice. Bloomsbury Publishing PLC.

    Google Scholar 

  • Carling, C., Wright, C., Nelson, L. J., & Bradley, P. S. (2014). Comment on performance analysis in football: A critical review and implications for future research. Journal of Sports Sciences, 32(1), 2–7.

    Article  Google Scholar 

  • Castellano, J., Alvarez-Pastor, D., & Bradley, P. S. (2014). Evaluation of research using computerised tracking systems (Amisco® and Prozone®) to analyse physical performance in elite soccer: A systematic review. Sports Medicine, 44(5), 701–712.

    Article  Google Scholar 

  • Cooper, H. M. (2010). Research synthesis and meta-analysis: A step-by-step approach. SAGE.

    Google Scholar 

  • Craig, L. (2018). Sports analytics: How data gives teams the edge. Retrieved February 02, 2021, https://www.techerati.com/features-hub/opinions/sports-analytics-how-data-gives-teams-the-edge/

  • Davenport, T. H. (2018). The AI advantage. How to put artificial intelligence revolution to work. MIT Press.

    Book  Google Scholar 

  • Davenport, T. H. (2014). Analytics in sports: The new science of winning 2. International Institute for Analytics, 1–28.

    Google Scholar 

  • Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Press.

    Google Scholar 

  • De Hoog, M. (2015). How data, not people, call the shots in Denmark. Retrieved February 02, 2021, https://thecorrespondent.com/2607/how-data-not-people-call-the-shots-in-denmark/230219386155-d2948861

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

    Article  Google Scholar 

  • Dumay, J. (2014). 15 years of the Journal of Intellectual Capital and counting–A manifesto for transformational IC research. Journal of Intellectual Capital, 15(1), 2–37.

    Article  Google Scholar 

  • Fasel, D., & Meier, A. (2016). Was versteht man unter Big Data und NoSQL? In D. Fasel & A. Meier (Eds.), Big Data. Grundlagen, Systeme und Nutzungspotenziale. Springer Fachmedien.

    Google Scholar 

  • Fiedler, H. (2018). Der Einsatz von KI in der Personalauswahl - Erkenntnisse aus dem “Footbonaut”. In R. Lanwehr & J. Mayer (Eds.), People Analytics im Profifußball. Springer Fachmedien GmbH.

    Google Scholar 

  • Fraunhofer. (2018). Maschinelles Lernen - Kompetenzen Anwendungen und Forschungsbedarf. Retrieved February 02, 2021, https://www.bigdata.fraunhofer.de/content/dam/bigdata/de/documents/Publikationen/BMBF_Fraunhofer_ML-Ergebnisbericht_Gesamt.pdf

  • Freiknecht, J., & Rapp, S. (2018). Big Data in der Praxis. Lösungen mit Hadoop, Spark, HBase und Hive. Daten Speichern, Aufbereiten, Visualisieren (2nd ed.). Carl Hanser Verlag.

    Google Scholar 

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

    Article  Google Scholar 

  • Fried, G., & Mumcu, C. (2017). Sport Analytics. A Data-Driven Approach to Sport Business and Management. Routledge.

    Google Scholar 

  • Frick, B. (2004). Die Voraussetzungen sportlichen und wirtschaftlichen Erfolges in der Fußball-Bundesliga. In M. Bieling, M. Eschweiler, & J. Hardenacke (Eds.), Business-To-Business-Marketing im Profifußball (pp. 71–93). Deutscher Universitätsverlag.

    Chapter  Google Scholar 

  • Gabbett, T. J. (2016). The training—injury prevention paradox: Should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273–280.

    Article  Google Scholar 

  • Gassmann, O., & Perez-Freije, J. (2011). Eingangs-, Prozess- und Ausgangskennzahlen im Innovationscontrolling. Controlling & Management Review., 55(6), 394–396.

    Article  Google Scholar 

  • Gehrmann, S. (2017). Die Vermessung Des Sports. Retrieved March 03, 2021, https://www.bi-scout.com/die-vermessung-des-sports

  • Gerrard, B. (2016). Understanding sports analytics. Retrieved February 28, 2021, https://winningwithanalytics.com/2016/06/22/first-blog-post/.

  • Görlich, P., & Mayer, J. (2018). Falldarstellung: TSG 1899 Hoffenheim-Herkunft und Strategie. In R. Lanwehr & J Mayer (Eds.), People Analytics im Profifußball. Springer Fachmedien GmbH.

    Google Scholar 

  • Gould, S. J. (1996). Das Missverständnis Des Menschen. WW Norton & Company.

    Google Scholar 

  • Gowda, M., Dhekne, A., Shen, S., Choudhury, R. R., Yang, X., Yang, L., Golwalker, S., & Essanian, A. (2017). Bringing IoT to sports analytics. In 14th USENIX Symposium on Networked Systems Design and Implementation. Boston, MA, USA.

    Google Scholar 

  • Gramlich, D. (2018). Technische Grundlagen der Datensammlung und -analyse und ihre Auswirkungen auf den Fußball. Big Data im Fußball. Studylab 2018. Norderstedt: Books on Demand GmbH.

    Google Scholar 

  • Groll, A., Ley, C., Schauberger, G., & van Eetvelde, H. (2018). Prediction of the FIFA World Cup. A random forest approach with an emphasis on estimated team ability parameters. Retrieved February 28, 2021, https://arxiv.org/pdf/1806.03208.pdf2018

  • Gudmundsson, J., & Wolle, T. (2013). Computers, Environment and Urban Systems Football analysis using spatio-temporal tools. Computers, Environment and Urban Systems. Retrieved February 28, 2021, https://thomaswolle.net/article-resources/GudmundssonWolle_FootballAnalysisUsingSpatioTemporalToolsEA.pdf

  • Herberger, T. A., Oehler, A., & Wedlich, F. (2018). Third-Party-Ownership-Konstruktionen im Profifußball: Finanzwirtschaftliche Einordnung, kritische Bestandsaufnahme und Implikationen. In T. Herberger (Ed.), Sportökonomie im Kontext Von Governance & Gesellschaft (pp. 129–158). Verlag Dr. Kovač.

    Google Scholar 

  • Herberger, T. A., Oehler, A., & Wedlich, F. (2019). Third party ownership arrangements: Is a ban in football really appropriate? Journal of Governance & Regulation, 8(3), 47–57.

    Article  Google Scholar 

  • Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis. Journal of Sports Sciences, 20(10), 739–754.

    Article  Google Scholar 

  • IEEE. (2015). Towards a definition of the Internet of Things (IoT). Retrieved February 28, 2021, https://iot.ieee.org/images/files/pdf/IEEE_IoT_Towards_Definition_Internet_of_Things_Issue1_14MAY15.pdf(1)

  • Ikram, M. A., Alshehri, M. D., & Hussain, F. K. (2015). Architecture of an IoT-based system for football supervision (IoT Football) 2nd World Forum on Internet of Things (WF-IoT) 2015 (pp. 69–74). IEEE.

    Google Scholar 

  • Karkazis, K., & Fishman, J. R. (2017). Tracking US professional athletes: The ethics of biometric technologies. American Journal of Bioethics, 17(1), 45–60.

    Article  Google Scholar 

  • Kannekens, R., Elferink, T., & Visscher, C. (2011). Positioning and deciding: Key factors for talent development in soccer. Scandinavian Journal of Medicine and Science in Sports, 21(6), 846–852.

    Article  Google Scholar 

  • Kempe, M., Grunz, A., & Memmert, D. (2015). Detecting tactical patterns in basketball. Comparison of merge self-organising maps and dynamic controlled neural networks. European Journal of Sport Science, 15(4), 249–255.

    Google Scholar 

  • King, N. M. P., & Robeson, R. (2007). Athlete or guinea pig? Sports and enhancement research. Studies in Ethics, Law, and Technology, 1(1).

    Google Scholar 

  • King, N. M., & Robeson, R. (2013). Athletes are guinea pigs. American Journal of Bioethics, 13(10), 13–14.

    Article  Google Scholar 

  • Kipper, G., & Rampolla, J. (2013). Augmented reality. Elsevier Inc.

    Google Scholar 

  • Kobielus, J. (2018). Wikibon’s 2018 big data analytics trends and forecast. Retrieved February 28, 2021, https://wikibon.com/wikibons-2018-big-data-analytics-trends-forecast/

  • Laukenmann, J. (2017). Forscher entschlüsseln den Erfolg im Fußball. Retrieved February 28, 2021, https://www.tagesanzeiger.ch/wissen/technik/entschluesselung-des-fussballs-dank-big-data/story/30186152?track

  • Lewanczik, N. (2018): Was wird wirklich relevant, was bleibt Zukunftsmusik?. Retrieved February 28, 2021, https://onlinemarketing.de/news/technologie-trends-2018-relevant-zukunft. Technologie. Trends.

  • Lewis, M. (2004). Moneyball: The art of winning an unfair game. W. W. Norton, & Company Inc.

    Google Scholar 

  • Link, D. (2018a). Data analytics in professional soccer. Performance analysis based on spatio-temporal tracking data. Springer Fachmedien GmbH.

    Google Scholar 

  • Link, D. (2018b). Sports analytics: Wie aus (kommerziellen) Sportdaten neue Möglichkeiten für die Sportwissenschaft entstehen. German Journal of Exercise and Sport Research. Deutschland: Springer-Verlag GmbH, 48(1), 13–25.

    Google Scholar 

  • Lucey, P., Bialkowski, A., Carr, P., Morgan, S., Matthews, I., & Sheikh, Y. (2013). Representing and discovering adversarial team behaviors using player roles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2706–2713).

    Google Scholar 

  • Mackenzie, R., & Cushion, C. (2013). Performance analysis in football. A critical review and implications for future research. Journal of Sports Sciences, 31(6), 639–676.

    Google Scholar 

  • Marr, B. (2015). Big data: Using SMART big data, analytics and metrics to make better decisions and improve performance. Wiley.

    Google Scholar 

  • McCall, A., Davison, M., Andersen, T. E., Beasley, I., Bizzini, M., Dupont, G., Duffield, R., Carling, C., & Dvorak, J. (2015). Injury prevention strategies at the FIFA 2014 World Cup: Perceptions and practices of the physicians from the 32 participating national teams. British Journal of Sports Medicine, 49(9), 603–608.

    Article  Google Scholar 

  • McHale, I. G., & Relton, S. D. (2018). Identifying key players in soccer teams using network analysis and pass difficulty. European Journal of Operational Research, 268(1), 339–347.

    Article  Google Scholar 

  • McKenna, B. (2017). TSG 1899 Hoffenheim gets “faster in the Head” with SAP analytics. Retrieved February 28, 2021, https://www.computerweekly.com/news/450423973/TSG-1899-Hoffenheim-gets-faster-in-the-head-with-SAP-analytics

  • Mangold, M. (2017). Unterschied virtual reality (VR) und augmented reality (AR). Retrieved February 28, 2021, https://magic-holo.com/unterschied-virtual-reality-vr-und-augmented-reality-ar/

  • Massaro, M., Dumay, J. C., & Guthrie, J. (2016). On the shoulders of giants: Undertaking a structured literature review in accounting. Accounting, Auditing and Accountability Journal, 29(5), 767–801.

    Article  Google Scholar 

  • Memmert, D., & Raabe, D. (2017). Revolution im Profifußball. Mit Big Data zur Spielanalyse 4.0. Deutschland: Springer-Verlag GmbH.

    Google Scholar 

  • Memmert, D., & Raabe, D. (2018). Data analytics in football: Positional data collection, modelling and analysis. Routledge.

    Book  Google Scholar 

  • Meyer, J.-U. (2017). 2.0. Die Digitalisierung der Sportbranche. E. Sport-1st Aufl. Göttingen: BusinessVillage GmbH.

    Google Scholar 

  • Millington, B., & Millington, R. (2015). The datafication of everything: Toward a sociology of sport and big data. Sociology of Sport Journal, 32(2), 140–160, Sects. 140–160.

    Google Scholar 

  • Möller, K., & Schönefeld, C. (2011). Innovation Performance Measurement Framework - Ein Referenzmodell zur Analyse der Innovationssteuerung. Controlling & Management, 55(6), 367–371.

    Article  Google Scholar 

  • Nafus, D., & Sherman, J. (2014). Big data, big questions|This one does not go up to 11: The quantified self-movement as an alternative big data practice. International Journal of Communications, 8, 1784–1794.

    Google Scholar 

  • O’Donoghue, P. (2005). Normative profiles of sports performance. International Journal of Performance Analysis in Sport, 5(1), 104–119.

    Article  Google Scholar 

  • Perl, J., & Memmert, D. (2017). A Pilot study on offensive success in soccer based on space and ball control-key performance indicators and key to understand game dynamics. International Journal of Computer Science in Sport, 16(1), 65–75.

    Article  Google Scholar 

  • Pickup, O. (2018). Three major ways cloud is transforming sport. Retrieved February 28, 2021, https://www.raconteur.net/technology/three-major-ways-cloud-transforming-sport

  • Quirling, C., Kainz, F., & Haupt, T. (2017). Sportmanagement. Verlag Franz Vahlen GmbH.

    Google Scholar 

  • Reichmann, T., Kißler, M., & Baumöl, U. (2017). Controlling mit Kennzahlen. Die systemgestützte Controlling-Konzeption (9th ed.). Verlag Franz-Vahlen GmbH.

    Google Scholar 

  • Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. Springerplus, 5(1), 1410.

    Article  Google Scholar 

  • Research and Markets. (2016). Worldwide sports analytics market (2016–2022). Retrieved February 28, 2021, https://www.researchandmarkets.com/research/gpr3fw/worldwide_sports

  • Ribeiro, J., Silva, P., Duarte, R., Davids, K., & Garganta, J. (2017). Team sports performance analysed through the lens of social network theory. Implications for research and practice. Sports Medicine, 47(9), 1689–1696.

    Google Scholar 

  • Ross, W. J., Beath, C. M., & Quaadgras, A. (2013). You may not need Big Data after all. Retrieved February 28, 2021, https://hbr.org/2013/12/you-may-not-need-big-data-after-all

  • Saam, S. (2017). DFB-Akademie: ‘Silicon Valley’ des Fußballs. Retrieved February 28, 2021, https://www.dw.com/de/dfb-akademie-silicon-valley-des-fu%C3%9Fballs/a-41669374

  • Schauberger, G., & Groll, A. (2018). Predicting matches in international football tournaments with random forests. Statistical Modelling, 18(5–6), 460–482. Retrieved February 28, 2021, https://journals.sagepub.com/doi/full/10.1177/1471082X18799934

  • Schoop, M., & Brauchle, A. (2016). Anwendung von Data-Mining-Technologien zu Statistischen Auswertungen und Vorhersagen Im Fußball. Universität Hohenheim.

    Google Scholar 

  • Söhnlein, K., & Borgmann, S. (2018). Diagnostik von Exekutivfunktionen im Fußball. In R. Lanwehr & J. Mayer (Hrsg.). People Analytics im Profifußball. Springer Fachmedien GmbH.

    Google Scholar 

  • SportHeads. (2018). Sport professionals 2018. Eine Studie zum Arbeitsumfeld des Teams Hinter dem Team. München.

    Google Scholar 

  • SportTechie. (2018). Techie and N3XT sports state of soccer technology & innovation. Sport. Retrieved February 28, 2021, https://www.sporttechie.com/sporttechie-and-n3xt-sports-state-of-soccer-technology-innovation/

  • Thite, M. (2018). e-HRM: Digital approaches, directions & applications. Taylor & Francis Group.

    Google Scholar 

  • Thür, H. (2015). Die Privatsphäre im Zeitalter von big Data. Jusletter IT, 21.

    Google Scholar 

  • Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence—Informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.

    Article  Google Scholar 

  • van der Westhuizen, E. J., & van der Haar, D. T. (2018). A wearable device-based framework for determining player effectiveness on the football pitch. ACM International Conference Proceeding Series, 226–231.

    Google Scholar 

  • Vanini, U., & Rieg, R. (2017). Effects of voluntary intellectual capital disclosure—a structured literature review. University of Applied Sciences.

    Google Scholar 

  • Weichert, T. (2013). Big data. Eine Herausforderung für den Datenschutz. In: Geiselberger, H., & Moorstedt, T. (Eds.). Big Data. Das Neue Versprechen der Allwissenheit. Suhrkamp Verlag.

    Google Scholar 

  • Werner, H. (2017). Performance-Messung in Forschung und Entwicklung. Controlling & management review. 3/2017, 16–26.

    Google Scholar 

  • Wired (2018). The unlikely secret behind Benfica’s fourth consecutive Primeira Liga title. Retrieved February 28, 2021, https://www.wired.co.uk/article/bc/microsoft-sl-benfica

  • Zobel, B., Werning, S., Metzger, D., & Thomas, O. (2018). Augmented und Virtual Reality: Stand der Technik, Nutzenpotenziale und Einsatzgebiete. In: de Witt, C., & Gloerfeld, C. (Hrsg.). Handbuch Mobile Learning. Wiesbaden: Springer Fachmedien GmbH.

    Google Scholar 

  • Zoph, B., Vasudevan, J., Shlens, J., & Le, Q. (2017). AutoML for large scale image classification and object detection. Retrieved February 28, 2021, https://ai.googleblog.com/2017/11/automl-for-large-scale-image.html

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Herberger, T.A., Litke, C. (2021). The Impact of Big Data and Sports Analytics on Professional Football: A Systematic Literature Review. In: Herberger, T.A., Dötsch, J.J. (eds) Digitalization, Digital Transformation and Sustainability in the Global Economy. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-77340-3_12

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