Video Analytics in Elite Soccer: A Distributed Computing Perspective

TL;DR

This paper reviews recent advances in video analytics for elite soccer, emphasizing distributed computing, real-time data collection, and analysis techniques enabled by IoT and machine learning.

Contribution

It provides a comprehensive overview of current state-of-the-art video analytics methods, discusses distributed computing's role, and proposes future research directions in elite soccer performance analysis.

Findings

Real-time video analytics enhances player performance tracking.

Distributed computing is crucial for handling large-scale sports data.

IoT and machine learning improve tactical and technical analysis.

Abstract

Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA's 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing and its importance in video analytics and propose a future research perspective.