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.

考虑到数据科学（DS）和运动分析的最新趋势，出现了在运动中利用机器学习（ML）和数据挖掘（DM）技术的机会。本文回顾了美国国家篮球协会（NBA）和欧洲联赛比赛中使用的背景和高级篮球指标。本文的目的是对文献中用于评估团队和参与者的现有绩效分析进行基准测试。篮球是一项需要全面枚举参数的运动，以便深入了解比赛并通过最大限度地减少不可预测性来分析策略和决策。这项研究为球队和球员表现篮球分析提供了宝贵的信息，可用于更好地理解比赛。此外，这些分析可用于团队组成，运动员的职业发展，并评估如何将其实现以用于未来的预测。因此，对这些指标进行批判性分析是领域专家和决策者了解游戏优势和劣势，更好地评估对手团队，了解如何优化绩效指标，将其用于团队和球员预测以及最终评估的宝贵工具。为团队组成做出更好的选择。