With the rapidly increasing volume of data, novel methods and technologies for their analysis, and opportunities to support decision-making processes emerge in the domain of sports analytics. This, in particular, applies to analysing athletes’ performance and calculating related business added value in major sports leagues such as the National Basketball Association (NBA). Specifically, the financial success of a team/franchise depends not only on the results of the games but also on the success of attracting marketable individuals who bring higher business value. In that regard, this paper aims to demonstrate the potential and importance of data mining methods to uncover the factors influencing the decisions related to the player selection based on individual results, physical characteristics, and professional contract salaries in the NBA. For the study, 22 datasets were integrated into three large datasets. The data covers the period from 1946 (when the league was founded) to 2017. Data mining models were developed in RapidMiner, enabling correlation, cluster and regression analysis. Change in the factors affecting the selection of new players in recent years was uncovered, while the classification revealed, for example, that more than 50% of players have below-average coefficients of efficiency and individual result contribution. An artificial neural network algorithm was used to identify discrepancies for players with high-salary contracts as many do not meet high-performance standards. The study demonstrates how classification and prediction models can serve sports analysts and managers in making decisions related to future professional contracts and predict future salaries for active players, among other contributions.
The COVID-19 pandemic interrupted the higher education improvement and highlighted the maintenance of public health as an essential priority. Due to this unexpected situation, the educational system moved from face-to-face to distance learning without prior preparations. This contingency made it possible to study the benefits of ICT tools in the educational process. Starting from the primary function of the educational process and through the forecast of future trends in education, this paper presents the guidelines for improvement of the teaching and learning processes and some opportunities for their implementation. A survey was conducted among the Faculty of Economics, Business, and Tourism students in Split, Croatia, who evaluated the acceptance of various ICT tools. Based on the obtained research results, generic strategic guidelines for the effective use of IT tools in teaching are presented, including their potential impact on accreditation criteria.
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