Abstract
This thesis ventures into the realm of Data Science (DS), and Sports Analytics are becoming pivotal in shaping the future of professional sports. This thesis presents a comprehensive exploration of their application within professional basketball, particularly focusing on the National Basketball Association (NBA). The primary aim of this research is to employ Machine Learning (ML) and Data Mining (DM) techniques to deepen the understanding of player performance, injury patterns, and economic impacts, thereby enabling more informed decision-making in sports management. The objectives of this research are manifold. First, it seeks to benchmark existing performance analytics and propose advanced algorithmic models to enhance the predictive power and understanding of player and team performance metrics in basketball. This involves a detailed analysis of NBA data spanning from 1996 to 2023, providing a longitudinal perspective on the evolution of the game and its players. Second, the study ...
This thesis ventures into the realm of Data Science (DS), and Sports Analytics are becoming pivotal in shaping the future of professional sports. This thesis presents a comprehensive exploration of their application within professional basketball, particularly focusing on the National Basketball Association (NBA). The primary aim of this research is to employ Machine Learning (ML) and Data Mining (DM) techniques to deepen the understanding of player performance, injury patterns, and economic impacts, thereby enabling more informed decision-making in sports management. The objectives of this research are manifold. First, it seeks to benchmark existing performance analytics and propose advanced algorithmic models to enhance the predictive power and understanding of player and team performance metrics in basketball. This involves a detailed analysis of NBA data spanning from 1996 to 2023, providing a longitudinal perspective on the evolution of the game and its players. Second, the study aims to quantify the relationship between player injuries and performance by examining how demographic factors such as age and position, as well as socioeconomic aspects, affect this dynamic. A particular focus is placed on musculoskeletal injuries, their prevalence, and the implications for player career trajectories and team strategies. An additional objective is to analyse the economic ramifications of injuries, identifying the costliest types and their impacts on team finances and player income. This research aims to provide actionable insights for coaches, managers, and healthcare professionals to optimize player care, team composition, and performance strategies. This study employs a variety of ML and DS techniques, including feature selection, clustering, and classification methods, to achieve these aims. Through comprehensive data analysis, it was benchmarked and proposed new algorithmic models to enhance the understanding and prediction of performance metrics. This study delves into the correlation of injuries with players' age, position, and performance, aiming to identify patterns and quantify their financial and strategic impacts. We explore the socioeconomic and demographic factors influencing sports, offering insights into the most prevalent injuries and their implications for the game. Another aim is to focus on understanding injury patterns in the NBA and how these injuries impact player performance. Utilizing a unique dataset, it identifies prevalent injuries, the anatomical areas most affected, and explores the influence of these injuries on players' performance post-recovery. The study stands out for its integrative method, merging injury data with performance and salary information to shed light on the interconnections between injuries, economic impacts, and on-court performance. It also looks into the timing and seasonal nature of injuries to find patterns related to time and external factors, as well as the specific effects of injuries on players' game-by-game performance metrics. This research is aimed at aiding coaches, sports medicine professionals, and team management by providing insights for injury prevention, player rotation optimization, and targeted rehabilitation strategies. The results of this research contribute to a more nuanced understanding of the multifaceted nature of sports performance, the strategic importance of injury management and prevention, and the significant economic considerations involved. By revealing the intricate interplay between these elements, this thesis offers a valuable resource for sports professionals, decision makers, and researchers, paving the way for more effective strategies in team management and player development. This study also sets a foundation for future work in this area, suggesting new avenues for research and application in the burgeoning field of sports analytics. The findings indicate a nuanced relationship between injuries, player performance, and economic aspects, shedding light on the critical age range for peak performance and the substantial financial burden injuries pose to teams. This research contributes to the fields of sports analytics and data science by providing a deeper understanding of game dynamics and presenting strategies for injury prevention and management, team composition, and performance optimization. This thesis not only aids decision makers in the sports industry but also sets the stage for future research in advanced sports analytics and injury management strategies.
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