Big Data Diagnosis Enhances Innovative Winning Formula in the World of Sports

Objectives: The central theme of this research paper is to analyze how sports have been benefited with the use of Big Data and to have a deep insight of the working of the Map Reduce algorithm which is responsible for handling and analyzing Big Data. Methods/Statistical Analysis: The research work is based on mining and filtering huge self-constructed database comprising records relevant to Indian cricketers after March 1980. The Apache Hadoop framework Hortonworks Sandbox 2.2.0 has been utilized for extracting information as per requirements from the database. The backbone behind working of the Apache Hadoop framework is the MapReduce algorithm which operates in three different phases and provides results depending on scripts and queries written in Apache Hive. Findings: With the advent of wearable and sensors enabled technologies, it has been easier for the analysts collect data from different sources. Many popular companies like Adidas and Nike are engaged in making wearable technologies that show real time stats of a player like his speed, heart rate, etc. These wearable devices can be used during training sessions to get a better picture about player’s fitness in real time. A team can analyze the tactics followed by opponents and predict how they are going to play in the next match with the help of Big Data. But it is not that the data is important, but what matters is that what organizations do with that data. The research paper concentrates on the methods that can be adopted to get the best out of the gathered data. The research work also proves that the MapReduce algorithm is far better than conventional data mining algorithms in terms of efficiency and timing. Application/Improvements: Big Data has brought a revolution in almost every sector and sports are no exemption. Forbes has mentioned that Big Data is the first ever use of statistics and data to make personal decisions in professional sports. The Apache Hadoop framework is also efficient in handling semi-structured and unstructured data.