The sports industry utilizes science to improve short to long-term team and player management regarding budget, health, tactics, training, and most importantly performance. Data Science (DS) and Sports Analytics play key roles in supporting teams, players and experts to improve performance. This paper reviews the literature to identify important attributes correlated with injuries and attempts to quantify their impact on player and team performance, using analytics in the National Basketball Association (NBA) from 2010 up to 2020. It also provides an overview of Machine Learning (ML) and DS techniques and algorithms used to study injuries. Additionally, it provides information for coaches, sports and health scientists, managers and decision makers to recognize the most common injuries and investigate possible injury patterns during competitions. We identify teams and players who suffered the most, and the type of injuries requiring more attention. We found a high impact from injuries and pathologies on performance; musculoskeletal impairments are the most common ones that lead to decreased performance. Finally, we conclude that there is a weak positive relationship between performance and injuries based on a holistic multivariate model that describes player and team performance.

体育行业利用科学来改善短期和长期的团队和球员管理，涉及预算，健康，战术，训练以及最重要的表现。数据科学（DS）和体育分析在支持团队，运动员和专家改善绩效方面发挥着关键作用。本文回顾了文献，以鉴定与伤害相关的重要属性，并尝试使用2010年至2020年美国国家篮球协会（NBA）的分析方法来量化其对球员和球队绩效的影响。它还提供了机器学习（ML）的概述）以及用于研究伤害的DS技术和算法。此外，它还为教练，体育与健康科学家，经理和决策者提供信息，以识别最常见的伤害并调查比赛中可能出现的伤害类型。我们确定遭受损失最大的球队和球员，以及需要更多关注的伤害类型。我们发现伤害和病理对绩效有很大影响；肌肉骨骼损伤是导致表现下降的最常见损伤。最后，我们得出结论，基于描述球员和团队绩效的整体多元模型，绩效与伤害之间存在弱的正相关关系。