Sport Science Model to Support the Professional Sports Organization Decision Making

The role of information technology in sports organizations is important to analyze all aspects including how to win in the competition based on various internal and external factors. The approach that used to enhance the sports organizations is sports science, which is a concept that contributes to the development of athletes by collaborating in many fields and studies, such as anatomy, physiology, psychology, engineering, chemistry, etc. The approach of Sport science articulates the technology that blended on all of the approached. Currently, the use of sports technology or science in Indonesia is also very low in all sports categories. Usually, the management and coaches look at player statistics based on manual data or reports to view player statistics periodically. There is still a lack of exploration to use player statistics to be used by coaches to improve the ability of the players and the organization. This research will propose how to develop sports science with a business intelligence approach that used the Kimball data mart approach as decision support in the field of sports so that management and coaches can apply the correct approach to improve the capacity of players and organizational development. The result of this study will provide the model of sports science that can be implemented for a professional sports organization in Indonesia.

[1]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[2]  Mostafa Jafari,et al.  Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS , 2012, Expert Syst. Appl..

[3]  Carlo Vercellis,et al.  Business Intelligence: Data Mining and Optimization for Decision Making , 2009 .

[4]  Daliang Zhou,et al.  Sports Competitive Intelligence and its Influence on China CompetitiveSports , 2015 .

[5]  Ray Hackney,et al.  How information-sharing values influence the use of information systems: An investigation in the business intelligence systems context , 2014, J. Strateg. Inf. Syst..

[6]  Simon Fong,et al.  Data Mining in Sporting Activities Created by Sports Trackers , 2013, 2013 International Symposium on Computational and Business Intelligence.

[7]  Arnaldo Coelho,et al.  The influence of Business Intelligence capacity, network learning and innovativeness on startups performance , 2019, Journal of Innovation & Knowledge.

[8]  Efraim Turban,et al.  Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures , 2013 .

[9]  Malcolm J. Beynon,et al.  Psychological Contracts and Job Satisfaction: Clustering Analysis using Evidential C-Means and Comparison with Other Techniques , 2012, Intell. Syst. Account. Finance Manag..

[10]  Michael S. Scott Morton,et al.  A Framework for Management Information Systems , 2015 .

[11]  Josep Crespo Hervás,et al.  Sport management education through an entrepreneurial perspective: Analysing its impact on Spanish sports science students , 2018 .

[12]  Markus Grünwald,et al.  Business Intelligence , 2009, Informatik-Spektrum.

[13]  T. Kempton,et al.  Business Intelligence: How Sport Scientists Can Support Organization Decision Making in Professional Sport. , 2019, International journal of sports physiology and performance.

[14]  Fabio Casati,et al.  Business Process Intelligence , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[15]  HackneyRay,et al.  How information-sharing values influence the use of information systems , 2014 .