DATA MINING APPROACH FOR BUSINESS VALUE ANALYSIS IN BASKETBALL

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 addedvalue 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.