The research conducted will convey the importance of data analytics. In the second part of this research paper, we will emphasize on the world of sports and the role that data visualization plays in modern sports such as soccer, basketball, tennis, and football.
The topic of this research paper will be on how important descriptive or exploratory methods are, and how they can be used for social media data analytics. This research offers clear information on how data collected on and off the field can be outlined and classified into useful information that demonstrates player patterns in sports. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. We are going to strongly observe the accuracy and classification of tweets into critical useful data. We are going to explore data used by different media platforms e.g. In addition we will share a step by step overview of how critical machine learning(ML) tools of two essential machine learning tools can advance and improve the capturing of data and insights in sports.
We will explore deep into the use of descriptive data analysis which is mostly supported by modern technological models through the use of R Software. We will demonstrate modern and innovative ways of capturing data using machine learning. In this research article, we identify ways and means of using R Software and other digital mechanisation processes to capture, review, assess and record player patterns in the field of sports.
Sports in and of itself is just a sport but when you extend and look beyond sports as a business, the use of statistics, social media data analytics and digital concepts are crucial in trying to assess, review and examine income, scores and other player patterns.
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