Leveraging Big Data Analytics Utilizing Hadoop Framework in Sports Science

Abstract

The first ever utilization of statistics in professional sports has been made possible to make better personal decisions with the assistance of Big Data. Each day, a number of matches are played under different categories of sports and each day, new records are set up and old records are broken and all the concerned data, statistics, and records undergo major changes. With the introduction of innovative sensor enabled technologies and wearable devices, the data generated from different sources can be collecting easily and accurately and analysts can make most of it. This helps in taking decisions like when to substitute the player. A team can predict the policies and tactics to be adopted by the opposition prior to the next scheduled encounter with the assistance of Big Data. The same can be applied on the team itself to check out the shortcomings and flaws in the game plan of the team. The fundamental purpose of the research work is to investigate how sports have profited with the utilization of Big Data and how further enhancement can be made possible in this field. The major challenge in sports science is to gain the competitive advantage over opposition using big data and it can be accomplished via appropriately mining the collected data. The research work focuses on the comparison of conventional Apriori data mining algorithm with the Hadoop-based MapReduce algorithm capable of handling the enormous amount of data. With the use of the Apache Hadoop framework, all this generated data can be collected in huge servers and can be mined when and as required with much ease.