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Big Data in Sports: A Bibliometric and Topic Study

Author

Listed:
  • Šuštaršič Ana

    (Faculty of Sport, University of Ljubljana, Slovenia)

  • Videmšek Mateja

    (Faculty of Sport, University of Ljubljana, Slovenia)

  • Karpljuk Damir

    (Faculty of Sport, University of Ljubljana, Slovenia)

  • Miloloža Ivan

    (Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, Croatia)

  • Meško Maja

    (University of Maribor, Faculty of Organizational Sciences, Slovenia)

Abstract

Background: The development of the sports industry was impacted by the era of Big Data due to the rapid growth of information technology. Unfortunately, that has become an increasingly challenging Issue. Objectives: The purpose of the research was to analyze the scientific production of Big Data in sports and sports-related activities in two databases, Web of Science and Scopus. Methods/Approach: Bibliometric analysis and topic mining were done on 51 articles selected after four exclusion criteria (written in English, journal articles, the final stage of publication, and a detailed review of all full texts). The software tool used was Statistica Data Miner. Results: We found that the first articles appeared in Scopus in 2013 and WoS in 2014. USA and China are countries which produced the most articles. The most common research areas in WoS and Scopus are Public environmental and occupational health, Medicine, Environmental science ecology, and Engineering. Conclusions: We conducted that further research and literature review will be required as this is a broad and new topic.

Suggested Citation

  • Šuštaršič Ana & Videmšek Mateja & Karpljuk Damir & Miloloža Ivan & Meško Maja, 2022. "Big Data in Sports: A Bibliometric and Topic Study," Business Systems Research, Sciendo, vol. 13(1), pages 19-34, June.
    • Handle: RePEc:bit:bsrysr:v:13:y:2022:i:1:p:19-34:n:9
    DOI: 10.2478/bsrj-2022-0002
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    References listed on IDEAS

    as
    1. Jianxin Li & Ke Deng & Xin Huang & Jiajie Xu, 2019. "Analysis and Applications of Location-Aware Big Complex Network Data," Complexity, Hindawi, vol. 2019, pages 1-2, July.
    2. Thorsten Emig & Jussi Peltonen, 2020. "Human running performance from real-world big data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. 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.
    4. Lynn Phan & Weijun Yu & Jessica M. Keralis & Krishay Mukhija & Pallavi Dwivedi & Kimberly D. Brunisholz & Mehran Javanmardi & Tolga Tasdizen & Quynh C. Nguyen, 2020. "Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States," IJERPH, MDPI, vol. 17(10), pages 1-10, May.
    5. Sung-Un Park & Hyunkyun Ahn & Dong-Kyu Kim & Wi-Young So, 2020. "Big Data Analysis of Sports and Physical Activities among Korean Adolescents," IJERPH, MDPI, vol. 17(15), pages 1-11, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Big Data; sport; bibliometric study; topic study; health care management; services; decision making;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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