Open AccessData mining approach for business value analysis in basketball
Author(s) -
Ivan Kekez,
Maja Ćukušić,
Mario Jadrić
Publication year - 2021
Publication title -
zbornik veleučilišta u rijeci
Language(s) - English
Resource type - Journals
eISSN - 1849-1723
pISSN - 1848-1299
DOI - 10.31784/zvr.9.1.14
Subject(s) - basketball , league , salary , value (mathematics) , selection (genetic algorithm) , athletes , computer science , data science , analytics , marketing , business , artificial intelligence , machine learning , economics , medicine , physics , archaeology , astronomy , market economy , history , physical therapy
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 sportsleagues such as the National Basketball Association (NBA). Specifically, the financial success of a team/franchise depends not onlyon 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 havebelow-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 donot 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.