A Data Science approach analysing the Impact of Injuries on Basketball Player and Team Performance. Issue 99 (July 2021) - Sarlis, Vangelis; Chatziilias, Vasilis; Tjortjis, Christos; Mandalidis, Dimitris
Abstract: 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. Highlights: Comparative analysis of Data Mining and Machine Learning techniques for injuries. Present most common injuries based on anatomical or pathology classification. Recognize important attributes correlated with injuries in basketball. Assess the influence of injuries in player performance. Weak positive correlation of injuries and performance.