Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts

Papageorgiou, George; Sarlis, Vangelis; Tjortjis, Christos

doi:10.3390/info15010061

Cited by 5 publications

(3 citation statements)

References 63 publications

(73 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML techniques have become increasingly prominent in AD due to their capability to manage complex data patterns [22,23]:…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Loddo,

Ghiani,

Perniciano

et al. 2024

Information

View full text Add to dashboard Cite

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company’s reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly-detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

show abstract

“…ML techniques have become increasingly prominent in AD due to their capability to manage complex data patterns [22,23]:…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Loddo,

Ghiani,

Perniciano

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…Overall, the use of SA can lead to a better understanding of the causes of musculoskeletal injuries and help to develop more effective strategies for preventing these injuries. By identifying risk factors and protective strategies, it is possible to reduce the incidence of musculoskeletal complications and ultimately improve athletes' health and performance [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Injury Patterns and Impact on Performance in the NBA League Using Sports Analytics

Sarlis,

Papageorgiou,

Tjortjis

2024

Computation

Self Cite

View full text Add to dashboard Cite

This research paper examines Sports Analytics, focusing on injury patterns in the National Basketball Association (NBA) and their impact on players’ performance. It employs a unique dataset to identify common NBA injuries, determine the most affected anatomical areas, and analyze how these injuries influence players’ post-recovery performance. This study’s novelty lies in its integrative approach that combines injury data with performance metrics and salary data, providing new insights into the relationship between injuries and economic and on-court performance. It investigates the periodicity and seasonality of injuries, seeking patterns related to time and external factors. Additionally, it examines the effect of specific injuries on players’ per-match analytics and performance, offering perspectives on the implications of injury rehabilitation for player performance. This paper contributes significantly to sports analytics, assisting coaches, sports medicine professionals, and team management in developing injury prevention strategies, optimizing player rotations, and creating targeted rehabilitation plans. Its findings illuminate the interplay between injuries, salaries, and performance in the NBA, aiming to enhance player welfare and the league’s overall competitiveness. With a comprehensive and sophisticated analysis, this research offers unprecedented insights into the dynamics of injuries and their long-term effects on athletes.

show abstract

“…Financial aspects are equally critical; unplanned absences due to injuries can lead to significant financial losses for NBA franchises. This study explores how Data Science and Sports Analytics can mitigate these risks by providing deeper insights into player health and strategic game management [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Sports Analytics and Association Rule Mining to Uncover Recovery and Economic Impacts in NBA Basketball

Sarlis,

Papageorgiou,

Tjortjis

2024

Data

Self Cite

View full text Add to dashboard Cite

This study examines the multifaceted field of injuries and their impacts on performance in the National Basketball Association (NBA), leveraging a blend of Data Science, Data Mining, and Sports Analytics. Our research is driven by three pivotal questions: Firstly, we explore how Association Rule Mining can elucidate the complex interplay between players’ salaries, physical attributes, and health conditions and their influence on team performance, including team losses and recovery times. Secondly, we investigate the relationship between players’ recovery times and their teams’ financial performance, probing interdependencies with players’ salaries and career trajectories. Lastly, we examine how insights gleaned from Data Mining and Sports Analytics on player recovery times and financial influence can inform strategic financial management and salary negotiations in basketball. Harnessing extensive datasets detailing player demographics, injuries, and contracts, we employ advanced analytic techniques to categorize injuries and transform contract data into a format conducive to deep analytical scrutiny. Our anomaly detection methodologies, an ensemble combination of DBSCAN, isolation forest, and Z-score algorithms, spotlight patterns and outliers in recovery times, unveiling the intricate dance between player health, performance, and financial outcomes. This nuanced understanding emphasizes the economic stakes of sports injuries. The findings of this study provide a rich, data-driven foundation for teams and stakeholders, advocating for more effective injury management and strategic planning. By addressing these research questions, our work not only contributes to the academic discourse in Sports Analytics but also offers practical frameworks for enhancing player welfare and team financial health, thereby shaping the future of strategic decisions in professional sports.

show abstract

Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts

Cited by 5 publications

References 63 publications

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Injury Patterns and Impact on Performance in the NBA League Using Sports Analytics

Leveraging Sports Analytics and Association Rule Mining to Uncover Recovery and Economic Impacts in NBA Basketball

Contact Info

Product

Resources

About