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An Assessment of Football Through the Lens of Data Science

Author

Listed:
  • Poojan Thakkar

    (Indus University)

  • Manan Shah

    (Pandit Deendayal Petroleum University)

Abstract

The rise of Data Science and related fields of Big Data, Machine Learning, and Deep Learning has transformed the industrial landscape. The areas of sports and sports analytics are no exception. While to the layman, its influence may not be evident, but they have changed the way various sports are played up to different degrees. Hence, in recent times, sports institutions and clubs have given increased importance to such research that will ultimately help them have a competitive edge over rivals. The effects of these institutions incorporating these researches into their ways of competing have had impacts on and off the playing field. These effects aren’t only in terms of physiological enhancements of the athletes, but also socio-political and economic impacts as well. Out of the various sports implementing these techniques, we will focus on the effects mentioned above of Data Science on Football (“Soccer” in the USA). The following is a detailed review of the concepts as mentioned earlier.

Suggested Citation

  • Poojan Thakkar & Manan Shah, 2021. "An Assessment of Football Through the Lens of Data Science," Annals of Data Science, Springer, vol. 8(4), pages 823-836, December.
  • Handle: RePEc:spr:aodasc:v:8:y:2021:i:4:d:10.1007_s40745-021-00323-2
    DOI: 10.1007/s40745-021-00323-2
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    References listed on IDEAS

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    1. P. D. Jones & N. James & S. D. Mellalieu, 2004. "Possession as a performance indicator in soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 4(1), pages 98-102, August.
    2. Manish Sharma & Shikha N. Khera & Pritam B. Sharma, 2019. "Applicability of Machine Learning in the Measurement of Emotional Intelligence," Annals of Data Science, Springer, vol. 6(1), pages 179-187, March.
    3. Fadi Thabtah & Li Zhang & Neda Abdelhamid, 2019. "NBA Game Result Prediction Using Feature Analysis and Machine Learning," Annals of Data Science, Springer, vol. 6(1), pages 103-116, March.
    4. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    5. Aaryan Gupta & Vinya Dengre & Hamza Abubakar Kheruwala & Manan Shah, 2020. "Comprehensive review of text-mining applications in finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    6. José Gama & Pedro Passos & Keith Davids & Hugo Relvas & João Ribeiro & Vasco Vaz & Gonçalo Dias, 2014. "Network analysis and intra-team activity in attacking phases of professional football," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 14(3), pages 692-708, December.
    7. A. Yiannakos & V. Armatas, 2006. "Evaluation of the goal scoring patterns in European Championship in Portugal 2004," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 6(1), pages 178-188, June.
    8. Devansh Patel & Dhwanil Shah & Manan Shah, 2020. "The Intertwine of Brain and Body: A Quantitative Analysis on How Big Data Influences the System of Sports," Annals of Data Science, Springer, vol. 7(1), pages 1-16, March.
    9. N Hirotsu & M Wright, 2003. "Determining the best strategy for changing the configuration of a football team," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 878-887, August.
    10. Lars Magnus Hvattum, 2013. "Analyzing Information Efficiency In The Betting Market For Association Football League Winners," Journal of Prediction Markets, University of Buckingham Press, vol. 7(2), pages 55-70.
    11. Alrababa’H, Ala’ & Marble, William & Mousa, Salma & Siegel, Alexandra A., 2021. "Can Exposure to Celebrities Reduce Prejudice? The Effect of Mohamed Salah on Islamophobic Behaviors and Attitudes," American Political Science Review, Cambridge University Press, vol. 115(4), pages 1111-1128, November.
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