Providing a Framework of Effective Components in Iranian Championship Sports with a Data Mining Approach

Document Type : Original Article

Authors

1 Ph.D. student in Department of Physical Education and Sport Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Professor of Department of Physical Education and Sport Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Associated Professor of Department of Physical Education and Sport Sciences, University of Isfahan, Isfahan Iran.

4 Professor of Department of Physical Education and Sport Sciences, Payame Noor University, Tehran, Iran

5 Professor of Department of Physical Education and Sport Sciences, Allameh Tabatabaei University, Tehran, Iran

Abstract

The main purpose of this study was to provide a framework of effective components in Iranian championship sports with a data mining approach. The research method was qualitative. The advanced search was performed in the general framework of factors affecting the sports industry and development and integration with data-driven technologies. Based on the literature review, 15 frameworks in the field of sports and 13 frameworks for the development and integration of the sports industry with technology were identified. After the data analysis, a researcher-developed framework for the championship sports industry for the use of data-driven technologies and data mining was presented in three parts. In the first part, nine influential factors including athletes/champion teams, leagues/clubs, stadiums/sports venues, fans/spectators, brands, media, government, academic/research institutions, and technology companies, were identified. In the second part, a strategic plan based on the development and integration of sports industry with data-driven technologies and data mining was presented, which includes four stages: Identifying and selecting talents, pre-game and match preparation, in-game and match activities, and post-match and match analysis. All data-driven activities and data mining in the first and second sections were performed by the IBM data science analysis methodology presented in the third section. Then, a conceptual framework was provided to 7 experts and their opinions were collected through semi-structured interviews and focus group methods. This conceptual framework enables sports managers to plan for their organization and adopt appropriate strategies using data-driven technologies and data mining.

Keywords

References

ABeam. (2018). By generating business innovation, ABeam Cloud facilitates global competitiveness.
Retrieved from https://www.abeam.com/am/en/topics/insights/interviews2018_feature03
Allenby, B. (2006). The ontologies of industrial ecology? Progress in Industrial Ecology, An International
Journal, 3(1-2), 28-40.
Asselstine, S., & Edwards, J. R. (2019). Managing the resource capabilities of provincial Rugby unions in
Canada: an understanding of competitive advantage within a sport development system. Managing
Sport and Leisure, 24(1-3), 78-96.
Bramham, P. (2001). Sports policy. Sports development: policy, process and practice, 7-36.
Curry, E. (2016). The big data value chain: definitions, concepts, and theoretical approaches New horizons for
a data-driven economy (pp. 29-37): Springer, Cham.
De Leeuw, A.-W., Meerhoff, L. A., & Knobbe, A. (2018). Effects of pacing properties on performance in longdistance running. Big Data, 6(4), 248-261.
Deloitte. (2017). Sports Tech Innovation in the Start-up Nation. Retrieved from
https://www2.deloitte.com/content/dam/Deloitte/il/Documents/finance/sport_tech_report_short.pdf
Gilson, N. D., Hall, C., Holtermann, A., van der Beek, A. J., Huysmans, M. A., Mathiassen, S. E., & Straker,
L. (2019). Sedentary and physical activity behavior in “blue-collar” workers: a systematic review of
accelerometer studies. Journal of Physical Activity and Health, 16(11), 1060-1069.
Hosseini, S. S., Hamidi, M., Rajabi, A. G., & Sajjadi, S. N. (2013). Identification of Strengths, Weaknesses,
Opportunities and Threats for Talent Identification in Iran Championship Sport and Its Bottlenecks
and Challenges. Sport Management, 5(17), 29-54.
Houlihan, B., & Green, M. (2007). Comparative elite sport development: Routledge.
IBM. (2015). Foundational Methodology for Data Science. Retrieved from
https://www.ibm.com/downloads/cas/WKK9DX51
Kashef, m. m., Syed Ameri, m. h., ahmadi, m., & marefat, d. (2018). Providing Strategic Model of Athletic
Sport Development in East Azerbaijan Province, by Using SWOT and BSC. Applied Research in Sport
Management, 6(4), 27-38.
Kearney. (2014). Winning in the business of sports. Retrieved from
https://www.es.kearney.com/communications-media-technology/article?/a/winning-in-the-businessof-sports
Khosromanesh, R., Khabiri, M., Khanifar, H., Alidoust Ghahfarokhi, E., & Zarei Matin, H. (2019). Designing
the Conceptual Model of Iran's Sports Industry. Sport Management Studies, 11(57), 17-38.
KPMG. (2016). The business of sports. Retrieved from
https://assets.kpmg/content/dam/kpmg/in/pdf/2016/10/The-business-sports.pdf
KPMG. (2019). The SportsTech Report Advancing Victoria’s startup ecosystem. Retrieved from
https://launchvic.org/files/The-SportsTech-Report.pdf
Mollaei, F., & Nejad, M. H. (2013). Predicting track and field records in London 2012 Olympic games using
gray model. European Journal of Experimental Biology, 3(3), 672-677.
Newzoo. (2017). The Global Growth of eSports: Trends, Revenues and Audience Towards. Retrieved from
https://newzoo.com/insights/articles/industry-first-report-evolution-esports-ecosystem
Ofoghi, B., Zeleznikow, J., MacMahon, C., & Raab, M. (2013). Data mining in elite sports: a review and a
framework. Measurement in Physical Education and Exercise Science, 17(3), 171-186.
Pelechrinis, K., Winston, W., Sagarin, J., & Cabot, V. (2018). Evaluating NFL plays: Expected points adjusted
for schedule. Paper presented at the International Workshop on Machine Learning and Data Mining
for Sports Analytics.
Pitts, B., Fielding, L. W., & Miller, L. K. (1994). Industry segmentation theory and the sport industry:
Developing a sport industry segment model.
Pourkiani, M., Hamidi, M., Goodarzi, M., & Khabiri, M. (2017). Analyzing the role of championship and
professional sport on sport development. Sport Management Studies, 9(42), 55-72.
doi:10.22089/smrj.2017.454.1143
PricewaterhouseCoopers. (2019). Artificial Intelligence Application to the Sports Industry. Retrieved from
https://www.pwc.com.au/industry/sports/artificial-intelligence-application-to-the-sports-industry.pdf
Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and
data-analytic thinking: " O'Reilly Media, Inc.".
Sandelowski, M., & Barroso, J. (2006). Handbook for synthesizing qualitative research: springer publishing
company
Schumaker, R. P., Solieman, O. K., & Chen, H. (2010). Sports data mining: The field Sports Data Mining (pp.
1-13): Springer.
Smith, S. E., Anderson, I. C., & Smith, F. A. (2015). Mycorrhizal associations and phosphorus acquisition:
from cells to ecosystems. Annual plant reviews, 48, 409-440.
Wanless, L. A., & Naraine, M. (2021). Sport analytics education for future executives, managers, and
nontechnical personnel. Sport Management Education Journal, 15(1), 34-40.
Yurko, R., Matano, F., Richardson, L. F., Granered, N., Pospisil, T., Pelechrinis, K., & Ventura, S. L. (2020).
Going deep: models for continuous-time within-play valuation of game outcomes in American football
with tracking data. Journal of Quantitative Analysis in Sports, 16(2), 163-182. 
Journal of New Studies in Sport Management
Volume 3, Issue 2
May 2022
Pages 474-485

Files

Share

How to cite

Statistics