Highlights Evaluation of Data Mining algorithms and techniques used on Sports Analytics. Evaluate, use and benchmark existing performance basketball analytics. Analyze the strategy and decisions for team composition for team improvement. Identify game strengths and weaknesses for better decisions in short or long term. Case study analysis based on most important basketball performance analytics. Given the recent trend in Data Science (DS) and Sports Analytics, an opportunity has arisen for utilizing Machine Learning (ML) and Data Mining (DM) techniques in sports. This paper reviews background and advanced basketball metrics used in National Basketball Association (NBA) and Euroleague games. The purpose of this paper is to benchmark existing performance analytics used in the literature for evaluating teams and players. Basketball is a sport that requires full set enumeration of parameters in order to understand the game in depth and analyze the strategy and decisions by minimizing unpredictability. This research provides valuable information for team and player performance basketball analytics to be used for better understanding of the game. Furthermore, these analytics can be used for team composition, athlete career improvement and assessing how this could be materialized for future predictions. Hence, critical analysis of these metrics are valuable tools for domain experts and decision makers to understand the strengths and weaknesses in the game, to better evaluate opponent teams, to see how to optimize performance indicators, to use them for team and player forecasting and finally to make better choices for team composition.
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