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Introduction

The advancement of technology has revolutionized the way sports performance analytics is conducted. In recent years, the introduction of edge computing has provided opportunities for real-time data processing and analysis, particularly in the field of sports. Edge computing involves processing data closer to the source, allowing for faster decision-making and improved performance monitoring. This thesis aims to explore the application of edge computing in real-time sports performance analytics, focusing on its potential benefits and challenges.

1.2 Background of Study

– Evolution of sports performance analytics
– Introduction of edge computing in sports analytics
– Importance of real-time data processing in sports

1.3 Problem Statement

– Limitations of traditional sports performance analytics methods
– Need for real-time monitoring and analysis in sports
– Challenges in implementing edge computing for sports analytics

1.4 Objective of Study

– To explore the potential benefits of edge computing in real-time sports performance analytics
– To identify the challenges and limitations of implementing edge computing in sports
– To propose recommendations for the successful integration of edge computing in sports analytics

1.5 Limitation of Study

– Limited availability of research on edge computing in sports analytics
– Constraints in access to real-time sports data for analysis

1.6 Scope of Study

– Focus on the application of edge computing in real-time sports performance analytics
– Examination of various sports disciplines and their use of edge computing
– Analysis of the impact of edge computing on athlete performance and coaching strategies

1.7 Significance of Study

– Contribution to the advancement of sports performance analytics
– Potential to improve athlete training and performance evaluation
– Relevance to sports organizations and coaching staff seeking real-time data insights

1.8 Structure of the Thesis

– Chapter 1: Introduction
– Chapter 2: Literature Review
– Chapter 3: Research Methodology
– Chapter 4: Discussion of Findings
– Chapter 5: Conclusion and Summary

1.9 Definition of Terms

– Edge computing: Processing data near the source to reduce latency and improve performance
– Real-time sports performance analytics: Analysis of athletic performance data in real-time for immediate feedback and decision-making

Thesis Overview on Edge Computing for Real-time Sports Performance Analytics

Edge computing has emerged as a promising technology in the field of sports performance analytics, offering opportunities for real-time data processing and analysis. This thesis aims to explore the application of edge computing in real-time sports performance analytics, focusing on its benefits, challenges, and implications for athlete training and coaching strategies.

Chapter 1 provides an introduction to the study, outlining the background, problem statement, objectives, scope, and significance of the research. The chapter also includes a discussion on the limitations of the study and defines key terms related to edge computing and sports performance analytics.

Chapter 2 presents a comprehensive literature review on edge computing in sports analytics, highlighting the evolution of sports performance analytics, the introduction of edge computing, and the importance of real-time data processing in sports. The chapter also discusses the challenges and limitations of traditional sports analytics methods, setting the stage for the exploration of edge computing in sports.

Chapter 3 details the research methodology employed in this study, including data collection methods, data analysis techniques, and research design. The chapter also describes the sample population, data sources, and data analysis tools used to explore the application of edge computing in real-time sports performance analytics.

Chapter 4 offers an elaborate discussion of the findings from the research, including the potential benefits of edge computing in sports, the challenges of implementing edge computing in sports analytics, and recommendations for integrating edge computing into sports performance monitoring and analysis.

Finally, Chapter 5 provides a conclusion and summary of the thesis, highlighting the key findings, implications, and future research directions in the field of edge computing for real-time sports performance analytics. By examining the potential of edge computing in sports analytics, this thesis aims to contribute to the advancement of sports performance monitoring and coaching strategies, ultimately enhancing athlete performance and competitive outcomes.

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