A Dockerized big data architecture for sports analytics

Abstract

The big data revolution has had an impact on sports analytics as well. Many large corporations have begun to see the financial benefits of integrating sports analytics with big data. When we rely on central processing systems to aggregate and analyze large amounts of sport data from many sources, we compromise the accuracy and timeliness of the data. As a response to these issues, distributed systems come to the rescue, and the MapReduce paradigm holds promise for large scale data analytics. We describe a big data architecture based on Docker containers with Apache Spark in this paper. We evaluate the architecture on four data-intensive case studies in sport analytics including structured analysis, streaming, machine learning approaches, and graph-based analysis.

A Comprehensive Statistical Evaluation System for Boxing Referees Based on Convolutional Neural Networks

“…The degree of modernisation of sport is also an important indicator of the level of national and regional sport and the overall strength of sport [4][5]. Boxing attracts a large number of sports fans with intense competitive scenes [6][7].…”